Easily the most interesting part of this announcement is buried in the second to last paragraph:
"We're also launching GPT‑5.6 Sol on Cerebras at up to 750 tokens per second in July, bringing frontier intelligence to customers at unprecedented speed. Access will initially be limited to select customers as we expand capacity."
750 tokens/s on a frontier model is going to be extremely interesting. I doubt this new version is anything but a version bump in terms of capabilities but if we can start getting these answers back faster, they end up being more useful.
Just off the top of my head, I can think of the tedious task of finding certain functionality within a codebase. I usually can't beat an AI agent harness at this task today. If the AI model is 3x faster I have less of chance.
> I can think of the tedious task of finding certain functionality within a codebase. I usually can't beat an AI agent harness at this task today.
Yup, I remember "racing" the AIs to figure things out in codebases just a year ago. Today, I have no chance. Whether it is due to degraded reasoning capabilities on my part or better models, I don't know.
At least in my case, much of the code in the codebase I'm working on is AI generated so even if I have an accurate mental model of how everything works, I have no idea where any of it is located or named.
In your own codebase you can at least run a "you simulator" to arrive at the answer fairly reliably. "Where would I have put this bit? Oh yes, of course ..."
I seem to remember - but cannot find, even with an AI boost - someone's "law of computing" or somesuch describing the amount of time that has to pass before code you wrote is indistinguishable to you from code written by someone else. At any rate the interval is not so long.
What about 15k tokens per second? [0] I remember looking at this earlier in the year and it being so fast that it feels fake. And, yes, this model is old - but still awesome for what it is.
But it’s irrelevant. 750 tokens/s on a full frontier model is useful. 15000 poor quality tokens is much less useful no matter how much scaffolding you put around it.
But it seems that there is some queuing/load balancing on their side, I mean when opus is actually outputting this 55t/s it feles fast, but apart from it's internal reasoning I think there's sometimes just waiting.
Oh wait yeah good point. At 750 tokens a second and the same amount of human patients they can set it to think for the same amount of time but four or five times the amount of thinking tokens, which may improve the quality of the eventual output.
Using gpt-5.4-mini in off-peak hours already feels like super-speed to me. That's probably no more than 100-150 tk/s. I can't imagine 750!
I've always eyed Cerebras but never had a use for it that would justify paying for the API directly. Although now that I think about it, trying out the API would probably cost less than a subscription for a month...
Agreed, 1000tok/s just fills up the context window (which is big by 2004 standards) super fast. But seems like 5.3-spark was just a taste of what’s to come.
The ChatGPT subscription gives you access to the -spark model(s) in Codex which are blazing fast (but pretty dumb) which I think runs on Cerebras hardware too.
I have a pretty good use case for gpt-oss. The amount of time savings has actually been wild. Definitely worth a try. Just to be clear, it gets like 2000tok/s
the more advanced models also utilize a lot more tokens, and a lot of these extra tokens may go towards safeguards at a higher rate than prior models as well.
not to say a speed boost isnt there but if they didnt increase tokens / s at all youd likely see things slow down a lot with the new model compared to current
At a certain rate we will be able to move towards continuous / real-time inference systems. The discrete, turn based solutions are quite confining with how they must be trained. Continuous and real-time would fundamentally alter the domain.
From an information theory perspective we are still in dial-up territory with regard to the actual information rate. 750 tokens per second would be a really bad dialup connection. Imagine 10 millions tokens per second.
We still have the problem that auto regressive decoders are memory bound.
The new Blackwell hardware combined with TensorRT-LLM and speculative decoding consistently can hit 1,000 TPS/user barrier, comparing to closer to ~250 TPS/user (out of 10k+/TPS on the server)
Is there something I missed, this looks more like 14.4 to 56 on a 64kbps backing channel modem story. I have no doubt that there are still massive gains to be found, but they seem to be using existing constraints more efficiently, not that fios is coming.
I don’t have the budget to work on the foundational model scale, but with a draft model 10x–20x faster than target and an 60-80 acceptance rate I can see how they could promise 750/TPS (with a lot of other hard work) but I would appreciate where I should look to figure out what I am missing.
Is there anyone exploring or writing about this in public? I've felt for a while that the turn-based model was not quite right, but also felt too stupid and ill-informed to have much of an opinion about what else it could be.
I have an active 'sleep' mode, where when the user is AFK the LLM goes into a loop with a sleep 10 between turns, and determines (via tool use) if something should be done. That's still a 'turn' in a way, but it's all the LLM just sort of sitting around like a human would, pondering what to do next.
But I could imagine after each space(eg, word) having a 27b model on a nice rig, with thinking off, doing a quick look at the sentence and determine if it should interrupt and start a real turn with thinking on. Which kind of is non-turn based in a way. If you're typing fast, it might hit that run every 3 or 4 words, but that's sort of how a human might be when a person is talking to them. That is, waiting for enough info to interrupt, if needed.
There might be a way to process chunks of a sentence using commas as break points, eg for comma delimitated phrases in sentences, so the whole sentence doesn't need to be re-processed each "should I break in" assessment at word break.
Could be fascinating. Could actually do some of this right now.
I don't think this is what the parent poster was thinking, but the idea even at this level seems fun.
Yeah, I've played with some similar stuff on my 9070xt. But ultimately all the ceremony on top is cloaking that it's still just two or more models taking turns prompting each other to give the illusion of continuous thought. It's still one thought at a time, with every thought starting from scratch with a big chunk of prior context.
The idea of true continuous thought and memory-generation is very interesting, though I can't even begin to conceive of how it would work.
Or if it's even correct? Maybe our brains are secretly actually turn based too?
And more importantly those 10 million tokens/s should cost fractions of a penny. Tokens need to be dirt cheap so I hope they build out massive solar+battery powered data centers asap.
Your comment made me think of another real time. Real time, dynamic code/apis.
Imagine a world where there is no code, just things mildly handshaking and then creating data APIs on the fly. Where communication is fuzzy and locked in on an individual basis. No years of RFCs, no RFCs at all, just... data.
Just data, man.
An API arbitration aberratically assigned at authorized access, abridged and annotated, analytically assuring absolute assurance.
Why remove the code and binary artifacts, though? Don't you want to verify that the business logic is accurate and the processing is deterministic?
In some circumstances there is no substitute for something that you know will produce the same answer for a given input, consistently. And that's before even considering the watts per response.
The AI is the business logic, and the processing, and all of it. The context window is effectively infinite, with layered context window depth and speed.
Think of short and long term memory, or think of RAM vs SWAP. Dip into swap to pull needed data into RAM context. SWAP can be anything storage related, including a symbolic database or a best-encoded set of priorities.
If a person knows 100 knots, but hasn't tied one in 23 years, they might have to think a bit before they get full use of their long term memory... and tie that knot. I don't see an issue with layered speed context, that is, GPU ram, slower RAM, DB storage, all in the same format.
Imagine a world where a 'factory' is just high-tech 3d printing, with a dozen different methods (eg, plastic, laser+metal, etc), and getting specs for everything possible is, well, an immense amount of work. Imagine having a billion item catalog of things to print, and, imagine new requests for new things to print.
And the request doesn't come from an expert, but from some dude who sketched something on the back of a cardboard box.
The LLM can pull from long term storage for how those things were done before, how similar things were done before, and just get to work.
Regardless, the connection was what I was talking about before. Data transfer. Do you need http? json once established? What? Imagine instead that's all in the wind?
And it's so fast, so capable, that dynamic is easy.
It's very easy to see how world changing this technology will be. In a few years these AIs are going to be negotiating how they communicate with each other. Humans won't necessarily be included in that negotiation unless we have some kind of specific reason to. So many communication layers are going to be opaque to humans. We just have to trust our AIs are communicating efficiently and safely.
I’ve thought about this before. No flaky config files, no updating endpoints, no status monitors. Just fuzzy everything that works almost all of the time.
I'm skeptical of how fast "up to" 750t/s really means. Maybe if they make it extremely expensive so it frees up enough capacity?
GPT‑5.3‑Codex‑Spark currently runs on Cerebras chips and it's giving me around 150t/s. Still relatively very fast, but nowhere near the 1,000t/s they claimed at launch. (Also it's not a very good model.)
That said, I'm super bought in to faster models being better for most use cases than smarter models.
Yep this is a glimpse into the future of 500+ t/s, which is in my opinion the next big thing that validates Jevon's paradox (the models are already smart enough)
I think the glimpse that is there will be exclusive access. So much for the open in openAI. If this technology really transforms society in the ways expected with inequality an unavoidable consequence equal access should be required like internet access was (isp can’t give preference to specific user traffic)
Yeah! So at a much smaller scale, being able to boost Step 3.7 Flash up to 40tk/s on my Spark-alike with proper triple head MTP was the thing that made it superior to Qwen 3.6 27B in wall clock time despite Step reasoning more
A lot of the open Chinese models get their results through huge reasoning loops. Being able to boost decode perf is what will make them worth it, and I’m sure OpenAI and Anthropic could do similar (if they aren’t already)
“Smart enough” really depends on how many other people have encountered a problem close enough to yours and solved it somewhere on the open internet, IMO.
Most of the frontier models can, when prompted and tooled correctly, do a lot of “reasoning” tasks that amount to resolving how the user has explained a particular widely known paradigm.
The more difficult and obscure the issues you provide them with, the faster you notice them reward hacking by altering the criteria until they are no longer attempting to solve the problem. Using “advisor” style loops helps hold this off at the cost of tokens, but there is still a fairly short limit at which they will essentially give up if they can’t find all of the necessary information - sometimes the issue is actually worse if they find a small amount of information instead of nothing - they’ll extrapolate from that tiny piece of data and generate plausible-sounding hallucinations almost every time.
And god forbid your problem involves doing something a different way than the majority of people do it. Unless you can write a full spec on it, the models will repeatedly spiral back into adjusting everything about your problem until it matches one of the most popular approaches in their training data.
I get how this is a trueism now but I never really understood why it would be useful to scrape cc/codex sessions for training. The relative amount of human input for that is so low (isn't that why they are so loved and used?), how could it actually be useful to them? Wouldn't you wanna focus on people not using it?
It's more useful as a set of feedback on the model results. You can do sentiment analysis on the user responses to see if they found the model results useful/frustrating/etc and use that to guide future training
I think this is a rosy estimate. The vast majority of what people do with these models is just the same old shit, I would be surprised if 1% of it were genuinely novel stuff worth folding back into the training data.
bean in mind that "GPT‑5.6 Sol on Cerebras at up to 750 tokens per second" not necessarily means the same model (in terms of inference result). It can mean anything like a very quantized model, a different level of model activation per inference etc.
Of course we can trust that wouldn't name the same thing with different levels of intelligence, right? Right?
This would be amazing for some of our "real-time" workflows, that need to fallback to AI for one reason or another. What used to happen is a rules based system did the majority of work, and occasional corner case would fall back to humans. Then we moved AI in, still not real time, but much faster. Cerebras could make that even faster.
OpenAI also announced two days ago that they're starting to make Cerebras style chips themselves [0], will be interesting to see how fast SotA model inference will be by the end of the year.
I don't understand how you refer to this as "Cerebras-style". Cerebras is wafer-scale and unique. Jalapeno is an inference-optimized conventional chip.
Even if their chip is a difference maker, end of the year is wayy too optimistic. It’ll at minimum be a multi-year effort to bring it to production at scale.
I don't see any indications that OpenAI is doing wafer-scale work.
I tend to doubt they would. Cerebras notably doesn't have a kv, is wildly high bandwidth, but within/across the chip, not able to dump/restore kv super well. I doubt openai is going to build something that is as expensive to run. Also, wafer-scale is absurdly hard & weird to pull off, so I doubt that would be their first foray.
Does the Cerebras variant offer input caching and corresponding discounts? Last I checked Cerebras would not cache or would cache but not give discounts for the cached input, making it impractical for agentic use and multiturn conversations.
"we can start getting these answers back faster, they end up being more useful."
Dude, 10x token speed is going to be absolutely nuts. Half the "parallel subagent workflow" business seems to be driven simply as a means to avoid tapping your thumbs waiting for the infernal robot to finish something. If things come back speedy quick all the time, it should keep up with the "speed of the human" and let me stay focused on one thread instead of half a dozen. Plus the cost of screwing up gets significantly lower because you just re-fire with an adjusted prompt and iterate.
Someday these things will be 100x as fast as they are today and that is when things will get insane.
it also makes the parent brain-dead because all those subtokens are missing from the context thus unable to steer the hyper dimensional context driven generation, and the subagent is dumb as a post so synthesizes something very weedsy while you're specifically attempting to understand the forest
You have an agent spawn the agents for you! You can ask Claude to do it for you, he is happy to use sonnet when you ask for grok and opus high when you ask for deepseek.
From what I know about batch processing/ concurrency in inference this is a pipe dream... Or its going to cost an arm and a leg. I think they're lying or its going to be a much smaller model and not "frontier"
If you have no need for Anthropic/OpenAI's frontier model capability, you may be better served with an open-weight model that can't be taken away.
Edit:
> GPT-5 does the job.
I bring up DeepSeek V4 Flash a lot on HN, but I want to mention that according to Artificial Analysis, it trades blows with GPT-5 (high) (from August, 2025) [0]
We rolled out Deepseek V4 Flash to our customers and it was an absolute disaster, unfortunately. It was not able to follow simple commands, always "forgot" to do things, lied consistently about its work, and so on.
It was pretty good though on on-off work, like summarizing something or executing simple commands, so we are experimenting now with using it for subagent work with clear instructions and hand off.
Deepseek V4 Pro on the other hand is a really really good main driver and we have a lot of success using it. Its not Opus or GPT-5.5 level but on its way. Kimi 2.6 as well btw.. so there is already quite some choice.
I found Flash to be a bit shaky as well until I started using it in xhigh/max thinking effort, then it became my daily driver. It runs quite well on a couple of DGX Sparks.
I still wish it was a little better, but there's hope for another model checkpoint (maybe with some of GLM 5.2's goodness distilled into it, that would be nice).
For all intents and purposes you'll be able to move an open weight model wherever you want.
I really dislike this rhetoric, you sound like the FSF guys who are like "you're not free until you're running coreboot with zero binary blobs". Sure they have a point but also, most people are fine running regular linux.
Reading your comment made me realize that I love that the position of the FSF is held by someone, in the interest of stretching the Overton Window to that side.
> For example: the Free Software Foundation only purchases desktop machines which support Libreboot, and Thinkpad X200 and X60 laptops with Libreboot. All desktops and servers we buy are KGPE-D16 motherboards, which are supported by Libreboot. As a result, all of the workstations used by the FSF staff have a free BIOS.
> Except where noted, all of the distributions listed on this page fail to follow the guidelines in at least two important ways:
> ...The kernel that they distribute (in most cases, Linux) includes “blobs”: pieces of object code distributed without source, usually firmware to run some device.
They are extreme, uncompromising, and live by their principles.
They are also the reason you can buy a computer meeting those requirements instead of being a pipe dream.
When attacking archetypes of people, there is some responsibility to make clear who you’re attacking and why, even to someone who’s not being hyper-open-minded. At least if you want them to learn from you: which may or may not be your goal. When you attack/signal you’re on the offensive, it is foolish to believe that they won’t knee-jerk attack back and become closed minded at least a little.
Regardless, the “misinterpretation” of the parent comment is actually a plausible interpretation. I suspend my judgement on what the actual “correct” interpretation of the original comment is: there are too many plausible interpretations to deductively decide. But I do know that since they first comment brought up a contentious issue, they should have put more work into crafting their message so there aren’t so many plausible interpretations that are contradictory. Or alternatively, they should have specified more precisely who they were talking about without a shadow of a doubt. That is if the commenter cared to be properly interpreted, but that may not be their goal. There are many reasonable reasons why that wouldn’t be their goal.
When you read someone's comment there is some responsibility to read the words they wrote and not attempt to attack them for an argument no reasonable person would extract from those words.
good luck doing it to inference companies in singapore or the netherlands. or one of the decentralized networks that dont look useful right now. the world is already sick of america acting like it can do whatever and force their rules on the rest of us.
Still, with the same model being served by multiple providers, it is much less likely to disappear entirely, even if you would like to keep using a cloud provider. Worst-case scenario, you change providers. Or you use OpenRouter as a proxy.
There's really no comparison between a model that Anthropic allows Google and Amazon to host with one that has been downloaded hundreds of thousands of times and has dozens of public inference providers.
I don't think they "allow" Google or Amazon to host them so much as Anthropic itself is deploying and managing their services on multiple cloud providers just like every other global scale business. Even the models served via OpenRouter are just being routed to compute under Anthropic control. Same with OpenAI. They aren't going to hand the world's most valuable intellectual property at the moment to some third party to run independently.
Now for the Chinese models on OpenRouter, yea. Those providers could be legit. Or it could be a failed crypto mining operation pivoting to providing AI compute. Who knows.
Yes. The difference is obviously that full, fat Linux runs on a superset of anything a layperson would call a computer, and can be built from source on roughly the same set of hardware. Running the full, fat Deepseek (as in the 1.6T model, unquantized) is too big to run on anything a layperson would call a computer, and being able to actually build it is even harder.
Deepseek V4 flash is actually useless. Sorry I've tested it after seeing so many comments like these. On Open router when trying to get it to output tool calls for creating tables, instead of providing the structured output correctly it was sending me peoples dropbox links and other image sharing site urls that led to pictures of random tables...
Llms seem to only impress a certain type of person. Hint, this type of person also was really excited about NFTs.
It’s the same as the SaaS model. Price keeps going up, and to justify it they keep forcing you to upgrade to new versions with features that nobody asked for.
Every use case of every customer doesn’t need more intelligence. I’m willing to bet that the vast majority will be perfectly fine running on “low intelligence” at a cheap price forever.
I for sure agree that plenty of current use-cases are solvable by non-frontier models.
However, you said “new versions with features that nobody asked for”, and I would prefer that you concede the point before shifting to arguing a new point.
What customers are asking for is smarter models. Because the tasks that only smarter models can solve are higher value, higher margin, than the tasks that non-frontier models can solve.
I've struggled with this. You definitely can have great cheap models. There are many of them open source and served profitably by neo-clouds. The big labs have basically given up on cheap models, and it is frustrating. It means applications are not likely to build as much on them anymore (we are shifting workloads from Haiku/Sonnet to Deepseek v4, for example).
I suspect the problem is that they need to charge a lot to keep revenue numbers up, and they are more worried about cannibalizing themselves than others cannibalizing them.
Good observations. There's definitely a trend in pricing increasing but also balanced by innovations and availability of other models (both open and closed) emerging as alternatives. It's natural for the labs to explore how much they can push pricing, and for competitors to explore how they can treat that margin as their opportunity to grow their business.
Why do you think so? This game can be played forever, you just need strong marketing and orgs gullible enough to pay a higher price for a minor upgrade.
Each model release gives an opportunity to reduce the number of old models still on offer, and charge a higher, less-subsidized tier. The trick is to charge a subsidized price that is less than an M3 Ultra, so they continue paying you rent, instead of a one-time fixed cost. So far open models can't compete with Opus 4.5 but as soon as it can, people will be looking at buying devices that can run that model locally.
We are a claude shop but we already bought two mac studios to start migrating less complex but still agentic workflows there. We will break even on those in less than a year.
On Nano "it's not even close when you test it in real scenarios" - what have you seen? What kind of things can GPT-5 Mini handle that GPT-5.4 Nano cannot?
We’re using GPT-5-mini in an enterprise data-processing workflow, and we too see that GPT-5.4 nano performs materially worse for our requirements, roughly 30% worse as measured through our test suite.
I think it's more that they're abandoning simpler AI tasks to chinese models. Qwen 35b and deepseek flash are better than gp5 mini on my tasks and way cheaper.
> Maybe it’s the realization that it was never that cheap in the first place and they're forcing us to upgrade in a slow and painful way.
All the analysis I have seen points to frontier models being profitable to serve. It’s using 50% or more of your GPUs for research plus CapEx for capacity expansion that makes these businesses so heavily cash-negative.
What you are observing is downstream of another detail. It gets more expensive to serve a model as utilization goes down. Plus the opportunity cost vs newer, more-profitable models.
There are plenty of valid reasons to critique here. “OpenAI is lying about this being a sustainable price to serve” is not one of them.
I don't see them capturing anything at this point. If inference was profitable then they could compete on price/model and capture the market. Then increase price and pay back the model training.
Feels like they are just pulling in as much as they can whilst competing on capabilities instead. At which point its a case of who can last the longest.
They're trying to do it more like a cartel where all major providers raise prices in unison. The intention is (probably) less specific entrapment and more getting people addicted to a fast LLM. From there, they all play with pricing to give a semblance of choice, without actually overly undercutting each other. At least, in the west.
This is all done to help valuations. The main revenue source are the investor dollars at the prospect that this industry will very soon actually be sustainable and highly profitable. It won't be, but if very soon stays around the corner consistently, the investor dollars keep coming.
This is a constantly repeated conspiracy theory and is not true at all. The api costs do increase but aggregate costs per task decrease. The question is: do people need lower intelligence models at all? The answer is a resounding NO!
How many people do you see using haiku or sonnet? I see very few and most people default to the latest model and just play with thinking effort. I think three layers are good enough and supporting more is not a good UX.
I... use them all the time: plan with a more advanced model, build with a cheaper one. Anthropic literally packages a metamodel (opusplan) for that pattern.
Also: calling the SV blitzscaling strategy of using VC money to fund loss leader products with the goal of building a monopoly via dumping a conspiracy is quite the position given there's entire books written in the topic...
No, you can't. These companies have two infrastructures: model training and model inference.
Inference needs to cache, it can't cache random model data, so it's essentially dedicated; it can't spin up models on demand, it has to know what demand is coming.
These companies are going to end up with very few models offered and that's probably generous. They might end up with just one model and you pay for removing it's safe guards.
I think GPT writes code the best. How well will it write in version 5.6? It gives me chills.
Recently, I went head-to-head with GPT on nearly 2,000 lines of code, and GPT's solution was superior and faster. I even referenced multiple codebases on GitHub while trying, but they were incomparable to GPT.
So using GPT brings both fear and excitement.
The fear comes from realizing that this level of code is now the average for most people. The excitement comes from knowing that I can now study and learn at this level too.
I'm really looking forward to seeing how much more advanced the code will be with the upgrade to 5.6.
Purely subjective, but I tend to prefer reading Opus 4.8 output over GPT 5.5 code, even when the latter can have a higher overall ceiling. The former is just a bit more convenient to review.
Yeah, Opus/GPT need multiple rounds of reviews from each other to get to clean auto review. Fable was like, it is done and indeed… crickets in bot comments. ‘No issues’ galore.
GPT-5.5 has been really hard to beat imho. I've spent $$$ on Opus, Deepseek v4 Pro and recently started to dogfood GLM-5.2 (which is not bad) but I cannot really trust any of them (almost blind) like I can trust GPT-5.5. It gives me tremendous confidence. I cannot say the same for any of the others I mentioned.
>> I am on the opposite camp. Open models are starting to perform better. GPT 5.5 keeps on messing things up.
I'm working in a 600k+ LoC codebase that has complex domain-specific logic and lots of moving parts. I find that Codex 5.5 is pretty good at surgical fixes, but does not go out of its way to explore and figure out what those surgical fixes might break. So I only use it to work on parts of the system that are pretty isolated from everything else so that risk of regression is small.
Tracking model performance on Artificial Analysis makes me think these models are constantly optimized/tuned in some way or another. GPT 5.5 was scoring in the mid 60's when it was first released, now it's almost 10 points higher.
Maybe I'll know once I try it? Honestly, for small functions or methods, I don't think there's a huge difference between models. But the larger the code gets, the more noticeable the difference seems to be.
Personally, I think this kind of coding experience varies from person to person
sadly with all the labs benchmaxxing I feel like you just have to try the model for a while to really evaluate how good it is, especially for each individual use case
My guess is that it's same base model as 5.5, but with additional post-training to improve and benchmaxx on a few things like that.
If they really thought it was competitive with Mythos/Fable across the board, then why wouldn't they release a broader set of benchmarks, and why price it day 1 at 1/2 the cost of Fable?
Not saying that's the case with OP, but I've found folks sometimes just rationalize it so [0] as they're paying top dollar for it (especially, when compared to may be less capable but affordable models).
When I searched for papers on using LLMs, I found that typically, you can have an LLM generate code and then ask it to find GitHub projects similar to that code. Then you can learn by looking at the pull requests and seeing how they structure things
In the old days, if I wanted to understand why memory offsets, padding techniques, or data layout structures were written a certain way, I had to stare at a senior programmer's code all day or wait for them to reply. But LLMs, while they do flatter me, explain things at a level I can actually understand. And LLMs don't get annoyed.
-Why do you cut API boundaries this way?
-Why do you change the order of struct fields?
-Why do you deliberately insert padding?
Most of it depends on the background and context. Sometimes you add it, sometimes you don't. To understand this tacit knowledge, you need access to senior developers. But their attitude often depends on how promising the student is and what background they come from. On top of that, you don't have to rely on the respondent's mood, authority, or availability.
Programming is fundamentally a field that requires seniors. In my case, I had no such seniors at all. I learned to code by buying codebases from failed companies and studying them. My first job didn't hire me as an employee—they hired me as the CEO of a subcontracting company (because that was structurally more advantageous for the contract). So I wasn't given the patience to learn programming fundamentals gradually. I had to pay penalties if I failed. Most of the projects I worked on were the kind where failure meant bankruptcy for me. Naturally, there was no one to teach me.
Most of my knowledge comes from reverse-engineering the code I purchased.
People say LLM code contains falsehoods, but commercially sold code has always had falsehoods too. Honestly, if we're just talking ratios, LLM code has fewer falsehoods.
In that sense, I still think it's a matter of context. If LLM code is false, was human code ever really true? LLMs do lie. They generate plenty of incorrect code. But humans do the same thing. If a problem comes up, you just look it up then and there. For me, LLMs and humans aren't all that different.
Codex 5.4/5.5 has been great for me as well compared to Claude Opus.
I've been mostly using it for Godot/GDScript code reviews, rubber duckying, asking it for better ideas for naming stuff (one of the hardest problems in programing)
I still can't trust it for generating code for entire files/classes/projects, because it's still icky, creating unnecessary variables and functions, using multiple `if`s instead of `and` or `or`, but it's good enough for generating Mac/iOS apps for my personal use in SwiftUI because fuck trying to keep up with Apple's documentation, or even migrating ancient Visual Basic stuff I made as a kid up to SwiftUI :)
> So using GPT brings both fear and excitement.
Only excitement for me. I've never been more productive, not because I ask AI to make something for me, but it helps me make what I was already going to, but better and quicker.
AI like any other tool could help smart people be smarter and dumb people be dumber, rather kinda like Toklien's Ring: You could be Sauron or you could be Bilbo or Frodo, or you could be Gollum :)
No offense but have you considered the strong possibility that you’re just not good at what you do? I am occassionally pleased but mostly annoyed or disappointed… but never getting anything close to chills. That sounds downright weird.
No offense but have you considered the strong possibility that you're just holding it wrong? You're entitled to your opinion, but OP is hardly the first person to say something like this and is surrounded by tons of folks saying the exact same thing. Just because it sounds weird to you, doesn't mean it's not true.
If you used GPT-5.5 over the last 24 hours or so, you may have already had access to 5.6.
I've been running some tests on a harness we're building, and suddenly saw a jump in a few points yesterday. I reran the vanilla codex benchmark and saw an ~88% score on Terminal Bench 2.1 from GPT-5.5 on vanilla Codex.
The biggest indicator, beyond the score, was that 3 tests which frequently hit "safety" blockers with 5.5 started succeeding last night without warning.
Did you even read the release, it wasn't broadly released to anyone..
At their request, we are starting with a limited preview for a small group of trusted partners whose participation has been shared with the government, before releasing more broadly.
This comment is an excellent example why the average llm user is basically a slot machine user who thinks "this one is hot, this one is lucky, this one is better than the others" and constantly switching between models on a whim of some occulted understanding that only they posses.
Also, who cares about some 80% benchmark.. They train on these public benchmarks in order to impress people like yourself that subscribe meaning to them. How come they only get 4% pass on $20-30/hr Upwork tasks? It seems to me like these benchmarks are basically useless... There's a thing called variance, I'm not sure why a higher scores on a few tests would lead you to believe you have access to a model that they say you don't have access too..
Hijacking popular thread to ask: What are the usage limits now for Codex and Claude?
A while back I gave the same task to both, and Codex used 20x less of my 5-hour limit (both on the $20/month plan).
(This annoyed me since I tend to prefer Claude, but the limits at the time made it unusable for anything serious.)
However, since that time, both providers have massively reduced usage allowances (and at least one of them has gotten sued for it, lol).
I'm not currently subscribed to either but I'm weighing my options. With GPT being slightly better than Opus, and it used to have way higher limits, I'm leaning in the direction of an OpenAI sub. But I'm wondering if the current state matches my memory from 2-3 months ago. (Since both companies appear to be cost-cutting hard!)
Prefer responses from people who use both, but anecdotes welcome :)
I find the Codex usage super generous (but on the $200 plan, I also have the Claude $200 plan). I can run xhigh with subagents pretty much all my waking hours if I want to. If I turn on speed (1.5x) I will hit the 5 hour limit sometimes.
I prefer Claude's vibe over 5.5 but 5.5 seems much less lazy. I'm sure it depends a lot on tasks and prompt strategy though.
This past month with Claude Max 5x actually felt really generous in terms of usage with a lot of resets because of Fable, bugs.
Honestly pretty similar levels of usage if you are using 5.5 high or Opus 4.8 high.
I think they just got rid of the separate Sonnet usage on Max plans (in preparation for Sonnet 5?) which is unfortunate because it made subagent workflows really feels nearly unlimited.
> Additionally, we’re introducing a new `ultra` mode that goes beyond the capabilities of a single agent by leveraging subagents to accelerate complex work.
I'm curious about how does this work? Do the subagents also get to use the same tools? Will the client be flooded with tool calls? Why extra pricing for a new "model" when the same thing can happen in the client with more controls?
And if it's an army of subagents, why do they compare it to Fable and Mythos? Those models with similar harness would probably bench better I'm guessing
If it's anything like ClaudeCode's ultracode, it's nothing new or revolutionary.
It's essentially a bunch of subagents being called by a deterministic script written by the main model thread, each eating tokens for lunch and output of which is synthesized by an orchestrator agent.
Eh, pretty much everyone that spent some time tweaking their harness already had a homemade 'ultracode' long before Anthropic did it.
OpenAI is just way more careful with what features they add or enable by default in their harness. Anthropic's harness is a junk drawer of random features, with a new feature added every few hours. It feels like they're in panic mode, dropping random things to see what sticks when models are eventually commoditized.
Don’t all the major harnesses (pi, Claude code, codex) utilize sub agents? Def if you direct it to, but I’ve seen at least pi spin them up without explicit instruction.
Yeah, I'm interested too. My guess for the reason, if not purely to eke out more performance, is so they can cleanly gather real-world data on this kind of usage.
With Codex, subagents are only used if you specifically prompt for them. Unlike Claude Code. Odd since it's the former with excess compute available to them.
Deep Research has been using the Orchestrator -> Subagents -> Synthesizer loop since the beginning. It's just strange that they'd put a loop benchmark next to actual model benchmarks.
Maybe it's a tune of the base model that works especially well with the subagent loop?
“ Terra has competitive performance to GPT‑5.5 [while being 2x cheaper]…”
To me that means “it’s an inferior product but marketing dictates we try and hide that.”
And “our most robust safety stack to date. We strengthened protections for higher-risk activity, sensitive cyber requests, and repeated misuse, and spent multiple weeks finding weaknesses, pressure-testing our system, and hardening it against real-world attacks” is of zero value to me at best, and most likely to my detriment (increasing refusals or nerfing utility). Why do providers keep leading with that? Are there customers (besides support ChatGPT chatbot users, maybe??) that ask for this?
"We're also launching GPT‑5.6 Sol on Cerebras at up to 750 tokens per second in July, bringing frontier intelligence to customers at unprecedented speed. Access will initially be limited to select customers as we expand capacity."
This seems like it would be the largest and first closed-source model Cerebras has offered till date
At least they plan to give the public all versions. Feels infinitely better than whatever the hell is happening at Anthropic.
> "Yeah, we've got the absolute best model out there. Trust us. Truly scary."
> "O-ok? May I see it?"
> "Gtfo. Here's a worse version of it for you plebs."
> "Um, thanks?"
> "Lmao, actually no. The current admin fell for our scare marketing. Here, have this even worse crazy expensive token burner that gets more hardware limited every week."
You can say what you want about OpenAI, but their corporate strategy feels so much more solid.
I don't see this as that different. Anthropic was the first one to get involved in the "AI models must be approved" regime. OpenAI just has the advantage of being second.
> We're also launching GPT‑5.6 Sol on Cerebras at up to 750 tokens per second in July, bringing frontier intelligence to customers at unprecedented speed.
This is really exciting. I work on voice AI, and we're still using 4.1/4.1 mini since none of the frontier models come close on latency. I'm excited to be able to have more interactive experiences, I think it'll unlock new ways of working with these models.
I was wondering the same thing. From textual context it is clear enough that Sol should be above Terra, but I had to zoom in really far to actually differentiate between the colors and I'm not colorblind. I saw a light mode version of the plot on twitter that was better but still not great.
OpenAI's plot design has been consistently awful and inaccessible, it seems like they're optimizing for something other than readability because I find it hard to believe they aren't putting in any effort for such major announcements. If the colors have to be awful they should at least differentiate with marker shapes or line dashes.
At least it isn't as bad as the stacked bar chart where the 50-something bar was higher than the 60-something bar.
I remember them using these chart colours during the 5 launch, maybe even 4.1 back in the day. Don’t know why, maybe its their CI manual that’s been generated by gpt-3.5-turbo…
Agent Arena (Dynamic ranking of models on how well they orchestrate tools for real-world agentic tasks, based on signals like tool reliability, task completion, and steerability.)
Top 10, Highest rank to lowest
Claude Fable 5 (High), Claude Opus 4.8 (Thinking), GPT 5.5 (xHigh), Claude Opus 4.7 (Thinking), GPT 5.5 (High), Claude Opus 4.7, Claude Opus 4.6, GPT 5.5, GPT 5.4 (High), GLM 5.2 (Max)
Text Arena
View overall rankings across various AI models in text-to-text tasks across math, coding, creative writing, and other open-ended domains.
Its clear to me these models are useless on any real world task, a 4% pass rate on $20-30/hr Upwork tasks. This whole trend of agentic engineering is a giant money grab.
Is this a new pre training run independent of 5.5s or post trained on it with Cerebras support and a rebrand of Pro mode at more usable speeds as Sol? The latter seems more likely to me, especially as 5.5 scales very well across its modes so separate branding could make sense, but I don’t see any clear information either way.
It does not introduce incompatibilities with earlier 5.x models? Frontier models are at a point now that there will never be a need for another major version bump, aside from those chasing marketing gimmicks. They are smart enough to adapt.
New request/response schema, new capabilities, or really anything that would break your existing workflows if you changed “5.5” to “5.6” in your application.
There have been many leaps forward in the past - tool calling, reasoning, agentic loops etc. 5.6 doesn’t have any of this. More intelligence doesn’t necessarily warrant a major version bump.
Terminalbench numbers are publicly available. What is more interesting, why is that the only benchmark they highlight. Maybe 5.6 isn’t that far ahead of Fable 5 in DeepSWE and FrontierCode (which I consider the most useful and close to my evals + subjective experience)…
All of these LLMs are getting better at being at an LLM
But GPT-5.5 is as useful an LLM can be; it has solved lemmas I've thought about for a year, it can implement typed STLCs in Rust when I give it a formal grammar, it can help me analyze Postgres planner dumps.
It's great at tasks that have short solutions but
- they cannot learn based on a project
- their long term planning capabilities are worse than worms
- they are unconfident in decision making
- their internal representations are disgusting compared to JEPA
- they don't have any "system
clock" like humans and computers do
- LLM architecture is not modular like computer architecture or human brain architecture
There's so many issues with LLMs. I wish that companies can start working on the next generation of architectures before the bubble pops
Totally agree! They also conflate things all the time (a major type of hallucination) and IIUC that can’t be solved with the current architecture, just patched over
How much dynamic routing do we think is being done here, especially in light of the cheaper options be 2x less cost than 5.5. I think learned routing is interesting because it could be the case that it only works as a way to get token and cost efficiency for in distribution tasks (like these benchmarks), yet on real world scenarios it could trend towards the same cost as the Sol cost.
Sol and 5.5 pro are in parity at $5 input / $30 output. What I'm inferring from this is that:
- model weight size didn't change, and this is mostly a result of better model architecture and scaled up RL
- better hardware utilization and and they're making better margins OR
- worse hardware utilization and they're okay with digging into their margins.
I agree it is probably the same size model. It's probably exactly built on top of 5.5, just with more training, or else they would have bumped the version number to 6.
The space is mature enough that pricing should largely be disconnected from underlying training cost. Basically, they are selling it for $X because that’s what the market expects the latest Pro-level frontier model to cost.
AFAIK there is no difference between "generation" and "version". Version naming/numbering depends on how good it turns out to be, and competition. If the competition releases something then you need to push something out too.
Calling it 5.6 creates the least possible expectations, and therefore more potential for positive feedback.
The Sol/Terra/Luna naming is interesting. I wonder what Anthropic are considering for their next models? "Terminator", "Armageddon"?
I think it makes more sense to make it so that major versions are different pretraining runs, and minor versions are simply the same pretraining run that was finetuned to different degrees. But it seems that that isn't cool anymore.
I think that there are some OAI employees on Hackernews. I do believe that they should give access to ya, because after all it would allows us to generate pelicans :-D
What is the consensus on who becomes part of the said small group of trusted partners and if they weren't so opaque about it. I'd expect comparatively big names like Simon to be included within such but Alas its not reality.
I should clarify that I've had plenty of preview access in the past, but clearly this has got a little bit delicate over the past few weeks!
I also don't like writing about preview models that I'm not 100% sure are the same as the general release model, because I don't want to review something which turns out not to be the model everyone else gets to use.
He is not an ML researcher or engineer, he is a passionate AI enthusiast blogger. He mostly does SVGs and other low effort checks (sometimes with major flaws, as people have pointed out a few times in the HN comments).
Properly evaluating the model across all fronts requires a deep understanding of LLMs, how they work, the trade offs behind new architectures and the relevant research papers. It also takes a lot of time to build a proper evaluation framework so basically you can't just vibe code that if you want something that is solid.
He created Django, what do you mean he's not an engineer? Also 'low-effort??' his posts are extremely in-depth, clearly very thought through with a significant amount of time and energy. Additionally he does perform multifaceted checks across LLMs in many of his other blog posts.
The charitable reading is that they meant “ML researcher or ML engineer” with the latter meaning, I guess, an engineer who works on developing LLMs not just using them.
> He created Django, what do you mean he's not an engineer?
I specifically said that he is not an ML engineer (emphasis on ML), so I'm not sure what Python web frameworks have to do with anything.
> Also 'low-effort??' his posts are extremely in-depth, clearly very thought through with a significant amount of time and energy
And yes, low effort. Pelican was low effort, his Fable test was low effort, his HN filter etc. Read the discussion in the comments under the Fable test, it's not just my opinion. There was also another example a few months ago. You can search for it, I don't keep track of these things.
I discussed this with him directly after he called himself an "ML expert" in comments.
This is a classic case of the Gell Mann amnesia effect. I read ML papers and work with ML, but to people outside the industry, his writing can look "extremely in-depth" even though it really isn't. People I work with have the same opinion.
> clearly very thought through with a significant amount of time and energy. Additionally he does perform multifaceted checks across LLMs in many of his other blog posts.
I have never seen an article by him about any model that I would describe that way.
And the most revealing sign that he is not an expert is the type of questions he asks and the mistakes he sometimes makes in the comments here. They show why he is not capable of doing any technically in depth evaluation (at least with his current knowledge level).
If you actually want to learn something as a layperson, read articles written by ML PhDs like Sebastian Raschka or watch Stephen from Welch Labs etc. that are directed at general audience.
We at HN: https://xkcd.com/2501/ to basically say that I think you might be considering low-effort what’s actually an attempt at simplifying - which is arguably higher effort
> you might be considering low-effort what’s actually an attempt at simplifying - which is arguably higher effort
I'm not saying that simplifying complex topics is low-effort, good simplification can obviously require a lot of work and I fully agree here.
What I meant is more that some of these tests feel methodologically sloppy, they are too shallow, miss important technical context, do not control for enough variables etc, yet the conclusions are sometimes presented lets just say... too strongly, as I don't want to be too harsh.
Musk steals Dario and they both train Epic on Mars. US Space Force promptly finds oil on Mars and launches an armada in the next window. In the meantime rocks painted black drop on Mar-a-Lago.
The sooner the USG figures out a standard process for approving releases the better. There are many differing opinions on how much to regulate AI, but I think we can all agree ad-hoc policy sucks.
> As part of our ongoing engagement with the U.S. government, we previewed our plans and the models’ capabilities ahead of today’s launch. At their request, we are starting with a limited preview for a small group of trusted partners whose participation has been shared with the government, before releasing more broadly.
The clowns in the US administration can barely remain coherent from one sentence to the next.
Having them be the gatekeepers of technological progress in 2026 is fucking lame.
How can I become a trusted organization/partner? For my SaaS[0] where we generate 3D models using code it would be an absolute game changer to have such speedy generations. This would mean AI could do 10 iterations in the time it makes 1 now.
Yeah, we'll share a lot more details and evals when we can release GPT-5.6 widely. We focused on cyber (and bio) here to help explain why it's being held back for now. We would have loved to launch it to everyone - it's the best coding model I've ever used - and we plan to do so as soon as we can ('coming weeks').
I do not like the fact that this forces people to remember one more hierarchy of "Sol vs Terra vs Luna". OpenAI was supposed to simplify their naming since at least 2025.
What happened to the nano/mini/standard/pro naming scheme, which worked perfectly fine and is intuitive to understand? Why does OpenAI insist on having the most inconsistent and confusing model and product names possible?
Are cyberweapons/cyberattacks "munitions"? if so, then isn't a machine capable of producing those munitions also itself a munition? I don't think you can put this down to "orange man bad" or "regulations", we're dealing with a genuinely groundbreaking technology with clear military applications
Haven't we established defensive and offensive security usage are intractably entangled? I.e. "patch all [security] bugs, make no mistakes" gives one a list of potential exploits to hand off to less capable models.
Doesn't that undermine all good-faith discourse on cybersecurity safeguards, controlled usage etc? Or is that overstating the case (I'm not a security researcher myself so kinda parroting).
we expect substantial benefit for legitimate defensive work, while meaningfully constraining prohibited offensive use.
That's literally impossible. Writing an exploit agains a known vulnerability needs the exact same knowledge that defending against the exploit of the same vulnerability.
Also just making the model better at code is just making it better to writing offensive code.
I'm going to pre-register my prediction that GPT-5.6 Sol is significantly behind Claude Fable 5, as evaluated by general consensus once time has passed for people to get familiar with both.
Claude will win on "vibes" and it'll be close in coding but considering how incremental Fable is above 5.5 in terms of overall smarts, there's no way 5.6 isn't considerably smarter on the whole.
Fable is allegedly a massive model (estimates between 6-10+ trillion, with a few hundred billion active). If 5.6 is just an incremental upgrade over 5.5 (at the same model size) then it won't be able to fully compete with Fable just yet.
"Affordable" depends on what you need. When a task is able to be achieved by two different calibers of model, it's obviously more cost effective to use the less capable model, in the same way that you wouldn't hire a math PhD to do simple addition.
If what you need is only possible with the more capable model then the "affordability" of the less capable model is sort of irrelevant. If what you need is a novel mathematical proof, it doesn't matter that a high school student is "more affodable". You need the math PhD.
As "old" models get more and more capable, it's going to be an increasingly important skill to be able to adequately recognize when a task requires a frontier model and when it doesn't, so that the less capable (and therefore cheaper) model can be used.
I’m countering this prediction by stating that Fable and Sol will be somewhat similar - this has always been the trend and I see no reason why this should stop now.
OpenAI may have a model in the works that is similar next-gen size and architecture to Fable, but this isn't necessarily it. I'd guess that 5.6 was more of a hasty reaction to Mythos - same base model (same size, same price) as 5.5 but with additional post-training to make it more competitive with Mythos/Fable in some benchmarks.
Mythos/Fable is supposedly next generation in size vs Opus, and is rumored to have some architectural innovation in terms of dynamic routing/compute, possibly only fully enabled with Fable which at $10/50 is still twice the price of Sol 5.6's $5/30, but a big reduction from Mythos preview which had been an astronomical $30/150 possibly due to the dynamic routing not yet having been enabled.
Is this the trend? There have been various points where one of Anthropic or OpenAI was substantially ahead. Sure, many times they're close, but now doesn't seem like one of them.
OpenAi dropped what they called 'side quests' like Sora [0] after Anthropic pursued a strategy of targeting software engineers.
In many companies, it's IT who will have major input into which company they sign up with as non-technical leaders need guidance, and by making IT fan boys of Claude Code, the enterprise contracts followed.
I didn't know that I was color blind, but thanks to those charts, I think I need to see a doctor...
I mean, you can read them even without the colors, but who on earth thought that those are a good set of colors? Oh, I forgot it was probably someone on 'Sol'.
> I mean, you can read them even without the colors
I'm not colorblind and I was depending on the textual context implying Sol was better than Terra. I had to zoom in quite far to actually differentiate between the colors.
If they insist on terrible colors would it be so hard to differentiate by marker shape or line dashing too?
A question I always have is, how to the AI labs safeguard the leak of their model? Training a cutting edge model basically cost a minimum of hundreds of millions of dollars. And its all contained within a file. Okay, that file might be 500GB large, but its still just one blob that is worth almost a billion dollars. And they need to train new models every few weeks, have lots of people with access to it to debug it, run inference etc. I wonder when we will see the first leaks? Imagine if e.g. Opus 4.8 got leaked. Wouldnt that bankrupt Anthropic?
1. Naming convention is copied from Anthropic and honestly is more catchy than a number (amongst normal people)
2. How in the world did Anthropic have to do all the theatrics about Mythos just to have OpenAI release an equivalent or stronger model a month later without any drama???
3. Cheaper models are just don’t fit any usecase imo and OpenAI knows it so they keep increasing the floor - I’m still convinced task per capability is reduced with each release
4. How in the world would open source models keep up with the multi layer security? Either this security is all theater or we will finally see a ceiling in open source models because by definition they can’t have those protections
5. Cybersecurity things are boring to me because it’s all zero sum cat and mouse games
The language used in this press release is borderline hilarious. It’s simultaneously trying to tell you how great it is while also telling it’s not THAT great. Nothing to worry about, move along.
Are GPT 5.5 and Opus 4.8 the last models we're going te be allowed to use in Europe? Is there going to be a cut, and we're only be allowed to use less capabale models outside of the US?
I mean, if they deem Fable 5 to powerful to share with the rest of the world, what's left for us?
Note that GPT 5.5 currently is $5 input / $30 output (short context) so Sol is in the same class, while Terra if the benchmarks are as claimed is indeed a half-price GPT 5.5 at comparable performance.
With the $200/month plan I’ve never ran into any limits or issues. The product can be used every day for extensive sessions and development. What is everyone doing that makes them talk about tokens versus dollars?
You can hit limits with $100 if you use it all day.
You can do it easily if you use in fast mode.
I bet you could hit the limits of the $200/month using fast mode if you were using multiple sessions at the same time all day on fast mode.
The OpenAI tiers seem pretty well tuned.
I used to use the plus ($20/month), and that was good for a few sessions every once in a while.
But now that I'm using it to configure my network, monitoring, maintenance, I'm using it every day and I'm on the $100 plan. And I do pretty consistently hit the limits, but it's easy to pace myself.
I'mam thinking about upgrading to $200/month though. It would be nice not to have to ration it.
From what my own experiences are, and what's on their checkout page, $100 is 5x base usage and $200 is 20x. If $100 was 10x, then I personally would drop down. They want people to go to the highest tier.
Fair. From a business perspective said amount is very reasonable in Europe / USA. For personal use it’s already different. Sometimes the answer is simple, thanks.
> For GPT‑5.6 and later models, cache writes are billed at 1.25x the model’s uncached input rate
Charging for cache writes is cringe and literally only Anthropic did it. Anyway this does mean the "real" prices are +25% on top of what you wrote there.
Can't buy cheaper as a selling point when Deepseek is basically free when hitting cache? Unsubsidized too, cloudflare and digital ocean can be the model provider for similar pricing.
> For GPT‑5.6 and later models, cache writes are billed at 1.25x the model’s uncached input rate, while cache reads continue to receive the 90% cached-input discount.
Not them joining Anthropic with this bullshit. *
Caching infrastructure is already a leaky abstraction over a feature that is not as reliable or debuggable to the end user as it should be, charging for the 'privilege' of interacting with it is really annoying.
(* for reference on 'this bullshit': ChatGPT previously didn't require anything special for a basic level of caching. Unless you wanted extended cache times, it'd just "do the right thing" and try to use nodes that had your prefix already cached in memory)
How else is this administration going to make money?!? How dare you...if they do not accept bribes...what is there left for them? This is a premium buy...First one to beat competition gets the worm. So, you pay Trump, trump gives you access...then you pay subscription to SAMA lol.
Flagged activity can also trigger account-level review across relevant conversations and risk signals, consistent with our terms and policies around content retention and review. Looking beyond a single conversation helps our systems distinguish persistent malicious behavior from legitimate dual-use security work, where similar technical concepts may appear in very different contexts.
Fascinating!
Every conversation you have with these "more capable" models will be monitored and joined up and then your entire account might one day be tagged as Distiller or Cyber Threat Actor or whatnot. When combined with identity verification (which isn't discussed in this press release), expect people to be falsely flagged and banned from ever using OpenAI models again.
Wish I could find the thread from last week where discussions of exactly this kind of thing were dismissed as daft and outlandish.
> falsely flagged and banned from ever using GPT models again
That would be the best case scenario. More realistically a few wrong prompts is going to get you on a government list, and if you’re an immigrant some dark cell.
* House design plans from prompts
* Government surveillance of public communication
* Extracting world/spatial concepts from language models (do we really need a world/spatial models now?)
* Driverless City planning startups
* Election vote rigging/harvesting startups
* Video game NPC backstory startups (all NPCs in GTA 6 go to work, go home, shower, go to sleep now?)
I can’t help but think that these benchmarks are completely fake. Sam even posted a benchmark on X a couple days ago of how the ‘complete version’ of 5.5 cyber was already ahead of Mythos apparently. This just feels like absolutely fake nonsense. The impact of Mythos on the industry was clear and in front of everyone’s eyes. The amount of vulnerabilities Mozilla fixed. The vulnerabilities and exploits Anthropic showcased in that blog post about the chrome sandbox escape etc.
And now we’re supposed to believe this 5.5 cyber is already ahead of Mythos, ok. And yeah, gpt 5.6 is even further ahead, alright.
Well if they are posting fraudulent benchmarks, that's a good sign to invest in their IPO. It's pure downside protection: IPO does well, profit. IPO does poorly, concrete evidence of pre-IPO fraud.
I personally don't think it's likely that OpenAI would post completely fake numbers in this pre-IPO period, but if you do, this is an opportunity.
I’m not an open-ai hater by default, I’m just waiting for someone to please explain how an incremental 5.5 cyber version is supposed to be already ahead of the flagship mythos model that’s been shaking up the software industry for a few months. If OpenAI had these supposed better-than-mythos 5.5 capabilities in their hands internally for some time now, why didn’t they make anything out of it in this era where everybody is desperate for any good press they can get?
U.S. government will decide who gets to use GPT-5.6 - https://news.ycombinator.com/item?id=48690101
"We're also launching GPT‑5.6 Sol on Cerebras at up to 750 tokens per second in July, bringing frontier intelligence to customers at unprecedented speed. Access will initially be limited to select customers as we expand capacity."
750 tokens/s on a frontier model is going to be extremely interesting. I doubt this new version is anything but a version bump in terms of capabilities but if we can start getting these answers back faster, they end up being more useful.
Just off the top of my head, I can think of the tedious task of finding certain functionality within a codebase. I usually can't beat an AI agent harness at this task today. If the AI model is 3x faster I have less of chance.
Yup, I remember "racing" the AIs to figure things out in codebases just a year ago. Today, I have no chance. Whether it is due to degraded reasoning capabilities on my part or better models, I don't know.
[1] Not AI codebases (and of course, AI code bases I guess)
https://www.youtube.com/watch?v=43QHhEfzz-Q
750 tokens/s for their largest model is going to be nuts
[0] https://chatjimmy.ai/
I've always eyed Cerebras but never had a use for it that would justify paying for the API directly. Although now that I think about it, trying out the API would probably cost less than a subscription for a month...
If you have a subscription it's a different pool of usage.
not to say a speed boost isnt there but if they didnt increase tokens / s at all youd likely see things slow down a lot with the new model compared to current
From an information theory perspective we are still in dial-up territory with regard to the actual information rate. 750 tokens per second would be a really bad dialup connection. Imagine 10 millions tokens per second.
The new Blackwell hardware combined with TensorRT-LLM and speculative decoding consistently can hit 1,000 TPS/user barrier, comparing to closer to ~250 TPS/user (out of 10k+/TPS on the server)
Is there something I missed, this looks more like 14.4 to 56 on a 64kbps backing channel modem story. I have no doubt that there are still massive gains to be found, but they seem to be using existing constraints more efficiently, not that fios is coming.
I don’t have the budget to work on the foundational model scale, but with a draft model 10x–20x faster than target and an 60-80 acceptance rate I can see how they could promise 750/TPS (with a lot of other hard work) but I would appreciate where I should look to figure out what I am missing.
But I could imagine after each space(eg, word) having a 27b model on a nice rig, with thinking off, doing a quick look at the sentence and determine if it should interrupt and start a real turn with thinking on. Which kind of is non-turn based in a way. If you're typing fast, it might hit that run every 3 or 4 words, but that's sort of how a human might be when a person is talking to them. That is, waiting for enough info to interrupt, if needed.
There might be a way to process chunks of a sentence using commas as break points, eg for comma delimitated phrases in sentences, so the whole sentence doesn't need to be re-processed each "should I break in" assessment at word break.
Could be fascinating. Could actually do some of this right now.
I don't think this is what the parent poster was thinking, but the idea even at this level seems fun.
The idea of true continuous thought and memory-generation is very interesting, though I can't even begin to conceive of how it would work.
Or if it's even correct? Maybe our brains are secretly actually turn based too?
Do you feel most of the speed upgrade will come from the software or hardware side?
Imagine a world where there is no code, just things mildly handshaking and then creating data APIs on the fly. Where communication is fuzzy and locked in on an individual basis. No years of RFCs, no RFCs at all, just... data.
Just data, man.
An API arbitration aberratically assigned at authorized access, abridged and annotated, analytically assuring absolute assurance.
In some circumstances there is no substitute for something that you know will produce the same answer for a given input, consistently. And that's before even considering the watts per response.
Think of short and long term memory, or think of RAM vs SWAP. Dip into swap to pull needed data into RAM context. SWAP can be anything storage related, including a symbolic database or a best-encoded set of priorities.
If a person knows 100 knots, but hasn't tied one in 23 years, they might have to think a bit before they get full use of their long term memory... and tie that knot. I don't see an issue with layered speed context, that is, GPU ram, slower RAM, DB storage, all in the same format.
Imagine a world where a 'factory' is just high-tech 3d printing, with a dozen different methods (eg, plastic, laser+metal, etc), and getting specs for everything possible is, well, an immense amount of work. Imagine having a billion item catalog of things to print, and, imagine new requests for new things to print.
And the request doesn't come from an expert, but from some dude who sketched something on the back of a cardboard box.
The LLM can pull from long term storage for how those things were done before, how similar things were done before, and just get to work.
Regardless, the connection was what I was talking about before. Data transfer. Do you need http? json once established? What? Imagine instead that's all in the wind?
And it's so fast, so capable, that dynamic is easy.
GPT‑5.3‑Codex‑Spark currently runs on Cerebras chips and it's giving me around 150t/s. Still relatively very fast, but nowhere near the 1,000t/s they claimed at launch. (Also it's not a very good model.)
That said, I'm super bought in to faster models being better for most use cases than smarter models.
A lot of the open Chinese models get their results through huge reasoning loops. Being able to boost decode perf is what will make them worth it, and I’m sure OpenAI and Anthropic could do similar (if they aren’t already)
Most of the frontier models can, when prompted and tooled correctly, do a lot of “reasoning” tasks that amount to resolving how the user has explained a particular widely known paradigm.
The more difficult and obscure the issues you provide them with, the faster you notice them reward hacking by altering the criteria until they are no longer attempting to solve the problem. Using “advisor” style loops helps hold this off at the cost of tokens, but there is still a fairly short limit at which they will essentially give up if they can’t find all of the necessary information - sometimes the issue is actually worse if they find a small amount of information instead of nothing - they’ll extrapolate from that tiny piece of data and generate plausible-sounding hallucinations almost every time.
And god forbid your problem involves doing something a different way than the majority of people do it. Unless you can write a full spec on it, the models will repeatedly spiral back into adjusting everything about your problem until it matches one of the most popular approaches in their training data.
I'm 100% sure that all our web, cc, codex or whatsoever sessions are used in the training, RL or either both.
This makes the size of the universe models know about at least one order of magnitude bigger than the open internet.
Of course we can trust that wouldn't name the same thing with different levels of intelligence, right? Right?
[0]: https://openai.com/index/openai-broadcom-jalapeno-inference-...
Jalepeno is for mass scale inference.
Cerebras is extremely expensive and difficult to scale, hence the limited release.
I tend to doubt they would. Cerebras notably doesn't have a kv, is wildly high bandwidth, but within/across the chip, not able to dump/restore kv super well. I doubt openai is going to build something that is as expensive to run. Also, wafer-scale is absurdly hard & weird to pull off, so I doubt that would be their first foray.
Dude, 10x token speed is going to be absolutely nuts. Half the "parallel subagent workflow" business seems to be driven simply as a means to avoid tapping your thumbs waiting for the infernal robot to finish something. If things come back speedy quick all the time, it should keep up with the "speed of the human" and let me stay focused on one thread instead of half a dozen. Plus the cost of screwing up gets significantly lower because you just re-fire with an adjusted prompt and iterate.
Someday these things will be 100x as fast as they are today and that is when things will get insane.
- GPT-5 mini costs $0.25/$2 and will be discontinued in December.
- GPT-5.4 mini costs $0.75/$4.5 and is supposed to be the replacement.
- GPT-5.4 nano costs $0.2/$1.25 and, while it ranks better in benchmarks than GPT-5 mini, it's not even close when you test it in real scenarios.
So you're left being forced to go to GPT 5.4 mini if you use 5 mini today.
The same thing is happening here as their “Luna“ model will cost $1/$6.
Can't we just stay with the models we actually want? I don't need GPT 5.4 mini. GPT-5 does the job.
Maybe it’s the realization that it was never that cheap in the first place and they're forcing us to upgrade in a slow and painful way.
Edit:
> GPT-5 does the job.
I bring up DeepSeek V4 Flash a lot on HN, but I want to mention that according to Artificial Analysis, it trades blows with GPT-5 (high) (from August, 2025) [0]
[0]: https://artificialanalysis.ai/models/comparisons/deepseek-v4...
Deepseek V4 Pro on the other hand is a really really good main driver and we have a lot of success using it. Its not Opus or GPT-5.5 level but on its way. Kimi 2.6 as well btw.. so there is already quite some choice.
I still wish it was a little better, but there's hope for another model checkpoint (maybe with some of GLM 5.2's goodness distilled into it, that would be nice).
I really dislike this rhetoric, you sound like the FSF guys who are like "you're not free until you're running coreboot with zero binary blobs". Sure they have a point but also, most people are fine running regular linux.
https://www.fsf.org/resources/hw
> For example: the Free Software Foundation only purchases desktop machines which support Libreboot, and Thinkpad X200 and X60 laptops with Libreboot. All desktops and servers we buy are KGPE-D16 motherboards, which are supported by Libreboot. As a result, all of the workstations used by the FSF staff have a free BIOS.
https://www.gnu.org/distros/common-distros.html
> Except where noted, all of the distributions listed on this page fail to follow the guidelines in at least two important ways:
> ...The kernel that they distribute (in most cases, Linux) includes “blobs”: pieces of object code distributed without source, usually firmware to run some device.
They are extreme, uncompromising, and live by their principles.
They are also the reason you can buy a computer meeting those requirements instead of being a pipe dream.
If you reread the comment with a fresh mind you'll notice that you misunderstood what he wrote
Regardless, the “misinterpretation” of the parent comment is actually a plausible interpretation. I suspend my judgement on what the actual “correct” interpretation of the original comment is: there are too many plausible interpretations to deductively decide. But I do know that since they first comment brought up a contentious issue, they should have put more work into crafting their message so there aren’t so many plausible interpretations that are contradictory. Or alternatively, they should have specified more precisely who they were talking about without a shadow of a doubt. That is if the commenter cared to be properly interpreted, but that may not be their goal. There are many reasonable reasons why that wouldn’t be their goal.
Fable itself is hosted on all major cloud providers. How many offer it today?
There's really no comparison between a model that Anthropic allows Google and Amazon to host with one that has been downloaded hundreds of thousands of times and has dozens of public inference providers.
Now for the Chinese models on OpenRouter, yea. Those providers could be legit. Or it could be a failed crypto mining operation pivoting to providing AI compute. Who knows.
And if you are a legit American business you aren’t going to illegally bypass import/export controls.
Llms seem to only impress a certain type of person. Hint, this type of person also was really excited about NFTs.
Citation: have you looked at OAI and Anthropic’s customer growth numbers?
However, you said “new versions with features that nobody asked for”, and I would prefer that you concede the point before shifting to arguing a new point.
What customers are asking for is smarter models. Because the tasks that only smarter models can solve are higher value, higher margin, than the tasks that non-frontier models can solve.
I suspect the problem is that they need to charge a lot to keep revenue numbers up, and they are more worried about cannibalizing themselves than others cannibalizing them.
Eventually the pricing should be more stable.
Why do you think so? This game can be played forever, you just need strong marketing and orgs gullible enough to pay a higher price for a minor upgrade.
We are a claude shop but we already bought two mac studios to start migrating less complex but still agentic workflows there. We will break even on those in less than a year.
If you want control over the models you use, you have to self-host.
All the analysis I have seen points to frontier models being profitable to serve. It’s using 50% or more of your GPUs for research plus CapEx for capacity expansion that makes these businesses so heavily cash-negative.
What you are observing is downstream of another detail. It gets more expensive to serve a model as utilization goes down. Plus the opportunity cost vs newer, more-profitable models.
There are plenty of valid reasons to critique here. “OpenAI is lying about this being a sustainable price to serve” is not one of them.
will trigger re-evaluations of models by other labs + inference providers
See Uber, Netflix, etc.
Feels like they are just pulling in as much as they can whilst competing on capabilities instead. At which point its a case of who can last the longest.
Doesn't feel like Uber/Netflix.
This is all done to help valuations. The main revenue source are the investor dollars at the prospect that this industry will very soon actually be sustainable and highly profitable. It won't be, but if very soon stays around the corner consistently, the investor dollars keep coming.
How many people do you see using haiku or sonnet? I see very few and most people default to the latest model and just play with thinking effort. I think three layers are good enough and supporting more is not a good UX.
Many enterprise use cases, such as simple data extraction, are well served by cheaper models.
For my use case a model from a year ago is good enough
Also: calling the SV blitzscaling strategy of using VC money to fund loss leader products with the goal of building a monopoly via dumping a conspiracy is quite the position given there's entire books written in the topic...
Inference needs to cache, it can't cache random model data, so it's essentially dedicated; it can't spin up models on demand, it has to know what demand is coming.
These companies are going to end up with very few models offered and that's probably generous. They might end up with just one model and you pay for removing it's safe guards.
Recently, I went head-to-head with GPT on nearly 2,000 lines of code, and GPT's solution was superior and faster. I even referenced multiple codebases on GitHub while trying, but they were incomparable to GPT.
So using GPT brings both fear and excitement.
The fear comes from realizing that this level of code is now the average for most people. The excitement comes from knowing that I can now study and learn at this level too.
I'm really looking forward to seeing how much more advanced the code will be with the upgrade to 5.6.
On the contrary, pi + glm + DeepSeek… bliss.
Fable was a different kind of beast though. Rip.
For most important work (complex, cross-domain inquiries etc.), I still rely on Codex GPT 5.5 though.
I'm working in a 600k+ LoC codebase that has complex domain-specific logic and lots of moving parts. I find that Codex 5.5 is pretty good at surgical fixes, but does not go out of its way to explore and figure out what those surgical fixes might break. So I only use it to work on parts of the system that are pretty isolated from everything else so that risk of regression is small.
Seems odd that their announcement has zero coding benchmarks, with the closest related thing being terminal bench.
Personally, I think this kind of coding experience varies from person to person
"What gets measured gets managed"
If they really thought it was competitive with Mythos/Fable across the board, then why wouldn't they release a broader set of benchmarks, and why price it day 1 at 1/2 the cost of Fable?
Not saying that's the case with OP, but I've found folks sometimes just rationalize it so [0] as they're paying top dollar for it (especially, when compared to may be less capable but affordable models).
[0] https://en.wikipedia.org/wiki/Choice-supportive_bias
Well, GPT referenced every GitHub code base, no wonder it won! :)
-Why do you cut API boundaries this way? -Why do you change the order of struct fields? -Why do you deliberately insert padding?
Most of it depends on the background and context. Sometimes you add it, sometimes you don't. To understand this tacit knowledge, you need access to senior developers. But their attitude often depends on how promising the student is and what background they come from. On top of that, you don't have to rely on the respondent's mood, authority, or availability.
Programming is fundamentally a field that requires seniors. In my case, I had no such seniors at all. I learned to code by buying codebases from failed companies and studying them. My first job didn't hire me as an employee—they hired me as the CEO of a subcontracting company (because that was structurally more advantageous for the contract). So I wasn't given the patience to learn programming fundamentals gradually. I had to pay penalties if I failed. Most of the projects I worked on were the kind where failure meant bankruptcy for me. Naturally, there was no one to teach me.
Most of my knowledge comes from reverse-engineering the code I purchased.
People say LLM code contains falsehoods, but commercially sold code has always had falsehoods too. Honestly, if we're just talking ratios, LLM code has fewer falsehoods.
In that sense, I still think it's a matter of context. If LLM code is false, was human code ever really true? LLMs do lie. They generate plenty of incorrect code. But humans do the same thing. If a problem comes up, you just look it up then and there. For me, LLMs and humans aren't all that different.
I've been mostly using it for Godot/GDScript code reviews, rubber duckying, asking it for better ideas for naming stuff (one of the hardest problems in programing)
I still can't trust it for generating code for entire files/classes/projects, because it's still icky, creating unnecessary variables and functions, using multiple `if`s instead of `and` or `or`, but it's good enough for generating Mac/iOS apps for my personal use in SwiftUI because fuck trying to keep up with Apple's documentation, or even migrating ancient Visual Basic stuff I made as a kid up to SwiftUI :)
> So using GPT brings both fear and excitement.
Only excitement for me. I've never been more productive, not because I ask AI to make something for me, but it helps me make what I was already going to, but better and quicker.
AI like any other tool could help smart people be smarter and dumb people be dumber, rather kinda like Toklien's Ring: You could be Sauron or you could be Bilbo or Frodo, or you could be Gollum :)
I've been running some tests on a harness we're building, and suddenly saw a jump in a few points yesterday. I reran the vanilla codex benchmark and saw an ~88% score on Terminal Bench 2.1 from GPT-5.5 on vanilla Codex.
The biggest indicator, beyond the score, was that 3 tests which frequently hit "safety" blockers with 5.5 started succeeding last night without warning.
At their request, we are starting with a limited preview for a small group of trusted partners whose participation has been shared with the government, before releasing more broadly.
This comment is an excellent example why the average llm user is basically a slot machine user who thinks "this one is hot, this one is lucky, this one is better than the others" and constantly switching between models on a whim of some occulted understanding that only they posses.
Also, who cares about some 80% benchmark.. They train on these public benchmarks in order to impress people like yourself that subscribe meaning to them. How come they only get 4% pass on $20-30/hr Upwork tasks? It seems to me like these benchmarks are basically useless... There's a thing called variance, I'm not sure why a higher scores on a few tests would lead you to believe you have access to a model that they say you don't have access too..
https://labs.scale.com/leaderboard/rli
A while back I gave the same task to both, and Codex used 20x less of my 5-hour limit (both on the $20/month plan).
(This annoyed me since I tend to prefer Claude, but the limits at the time made it unusable for anything serious.)
However, since that time, both providers have massively reduced usage allowances (and at least one of them has gotten sued for it, lol).
I'm not currently subscribed to either but I'm weighing my options. With GPT being slightly better than Opus, and it used to have way higher limits, I'm leaning in the direction of an OpenAI sub. But I'm wondering if the current state matches my memory from 2-3 months ago. (Since both companies appear to be cost-cutting hard!)
Prefer responses from people who use both, but anecdotes welcome :)
Thanks!
I prefer Claude's vibe over 5.5 but 5.5 seems much less lazy. I'm sure it depends a lot on tasks and prompt strategy though.
Honestly pretty similar levels of usage if you are using 5.5 high or Opus 4.8 high.
I think they just got rid of the separate Sonnet usage on Max plans (in preparation for Sonnet 5?) which is unfortunate because it made subagent workflows really feels nearly unlimited.
I'm curious about how does this work? Do the subagents also get to use the same tools? Will the client be flooded with tool calls? Why extra pricing for a new "model" when the same thing can happen in the client with more controls?
And if it's an army of subagents, why do they compare it to Fable and Mythos? Those models with similar harness would probably bench better I'm guessing
It's essentially a bunch of subagents being called by a deterministic script written by the main model thread, each eating tokens for lunch and output of which is synthesized by an orchestrator agent.
It's for sure a codex harness feature.
EDIT: yeah, it's the same thing. https://github.com/openai/codex/blob/main/codex-rs/core/test...
OpenAI flat out copying Anthropic is a pretty funny development. It's strong evidence that they've been in catch-up mode.
OpenAI is just way more careful with what features they add or enable by default in their harness. Anthropic's harness is a junk drawer of random features, with a new feature added every few hours. It feels like they're in panic mode, dropping random things to see what sticks when models are eventually commoditized.
I prefer OpenAI way - slow and steady.
Maybe it's a tune of the base model that works especially well with the subagent loop?
To me that means “it’s an inferior product but marketing dictates we try and hide that.”
And “our most robust safety stack to date. We strengthened protections for higher-risk activity, sensitive cyber requests, and repeated misuse, and spent multiple weeks finding weaknesses, pressure-testing our system, and hardening it against real-world attacks” is of zero value to me at best, and most likely to my detriment (increasing refusals or nerfing utility). Why do providers keep leading with that? Are there customers (besides support ChatGPT chatbot users, maybe??) that ask for this?
> To me that means “it’s an inferior product but marketing dictates we try and hide that.”
I interpret this to mean you're about to get today's mainline performance at a fraction of the price.
This seems like it would be the largest and first closed-source model Cerebras has offered till date
> "Yeah, we've got the absolute best model out there. Trust us. Truly scary."
> "O-ok? May I see it?"
> "Gtfo. Here's a worse version of it for you plebs."
> "Um, thanks?"
> "Lmao, actually no. The current admin fell for our scare marketing. Here, have this even worse crazy expensive token burner that gets more hardware limited every week."
You can say what you want about OpenAI, but their corporate strategy feels so much more solid.
(To be clear: I do not like this new paradigm)
This is really exciting. I work on voice AI, and we're still using 4.1/4.1 mini since none of the frontier models come close on latency. I'm excited to be able to have more interactive experiences, I think it'll unlock new ways of working with these models.
So the next naming scheme might be FTX, Madoff and Enron? :^)
OpenAI's plot design has been consistently awful and inaccessible, it seems like they're optimizing for something other than readability because I find it hard to believe they aren't putting in any effort for such major announcements. If the colors have to be awful they should at least differentiate with marker shapes or line dashes.
At least it isn't as bad as the stacked bar chart where the 50-something bar was higher than the 60-something bar.
Who knows what they will fix, block or change in the model between the preview and GA time. Open models can't arrive soon enough.
Agent Arena (Dynamic ranking of models on how well they orchestrate tools for real-world agentic tasks, based on signals like tool reliability, task completion, and steerability.)
Top 10, Highest rank to lowest
Claude Fable 5 (High), Claude Opus 4.8 (Thinking), GPT 5.5 (xHigh), Claude Opus 4.7 (Thinking), GPT 5.5 (High), Claude Opus 4.7, Claude Opus 4.6, GPT 5.5, GPT 5.4 (High), GLM 5.2 (Max)
Text Arena View overall rankings across various AI models in text-to-text tasks across math, coding, creative writing, and other open-ended domains.
Top 10, Highest rank to lowest
claude-fable-5, claude-opus-4-6-thinking, claude-opus-4-7-thinking, claude-opus-4-6, claude-opus-4-7, muse-spark, gemini-3.1-pro-preview, gemini-3-pro, claude-opus-4-8-thinking, gpt-5.5-high
https://labs.scale.com/leaderboard/rli
Its clear to me these models are useless on any real world task, a 4% pass rate on $20-30/hr Upwork tasks. This whole trend of agentic engineering is a giant money grab.
There have been many leaps forward in the past - tool calling, reasoning, agentic loops etc. 5.6 doesn’t have any of this. More intelligence doesn’t necessarily warrant a major version bump.
But GPT-5.5 is as useful an LLM can be; it has solved lemmas I've thought about for a year, it can implement typed STLCs in Rust when I give it a formal grammar, it can help me analyze Postgres planner dumps.
It's great at tasks that have short solutions but
- they cannot learn based on a project
- their long term planning capabilities are worse than worms
- they are unconfident in decision making
- their internal representations are disgusting compared to JEPA
- they don't have any "system clock" like humans and computers do
- LLM architecture is not modular like computer architecture or human brain architecture
There's so many issues with LLMs. I wish that companies can start working on the next generation of architectures before the bubble pops
You say this based on a theoretical understanding or did you inspect them?
JEPA gives you interpretability for free.
I have not personally inspected them and my view is maybe a more exaggerated/dramatic claim of those working in the JEPA sphere
I think you meant 5.5.
I agree it is probably the same size model. It's probably exactly built on top of 5.5, just with more training, or else they would have bumped the version number to 6.
If it was the next generation, why isn't it a major version change..?
Calling it 5.6 creates the least possible expectations, and therefore more potential for positive feedback.
The Sol/Terra/Luna naming is interesting. I wonder what Anthropic are considering for their next models? "Terminator", "Armageddon"?
Even Apple adopted and standardized on it for their latest platform releases.
What is the consensus on who becomes part of the said small group of trusted partners and if they weren't so opaque about it. I'd expect comparatively big names like Simon to be included within such but Alas its not reality.
I also don't like writing about preview models that I'm not 100% sure are the same as the general release model, because I don't want to review something which turns out not to be the model everyone else gets to use.
The charitable reading is that they meant “ML researcher or ML engineer” with the latter meaning, I guess, an engineer who works on developing LLMs not just using them.
I specifically said that he is not an ML engineer (emphasis on ML), so I'm not sure what Python web frameworks have to do with anything.
> Also 'low-effort??' his posts are extremely in-depth, clearly very thought through with a significant amount of time and energy
And yes, low effort. Pelican was low effort, his Fable test was low effort, his HN filter etc. Read the discussion in the comments under the Fable test, it's not just my opinion. There was also another example a few months ago. You can search for it, I don't keep track of these things.
I discussed this with him directly after he called himself an "ML expert" in comments.
This is a classic case of the Gell Mann amnesia effect. I read ML papers and work with ML, but to people outside the industry, his writing can look "extremely in-depth" even though it really isn't. People I work with have the same opinion.
> clearly very thought through with a significant amount of time and energy. Additionally he does perform multifaceted checks across LLMs in many of his other blog posts.
I have never seen an article by him about any model that I would describe that way.
And the most revealing sign that he is not an expert is the type of questions he asks and the mistakes he sometimes makes in the comments here. They show why he is not capable of doing any technically in depth evaluation (at least with his current knowledge level).
If you actually want to learn something as a layperson, read articles written by ML PhDs like Sebastian Raschka or watch Stephen from Welch Labs etc. that are directed at general audience.
I'm not saying that simplifying complex topics is low-effort, good simplification can obviously require a lot of work and I fully agree here.
What I meant is more that some of these tests feel methodologically sloppy, they are too shallow, miss important technical context, do not control for enough variables etc, yet the conclusions are sometimes presented lets just say... too strongly, as I don't want to be too harsh.
I hope this means then fable will also get released again.
and dario's you naughty boy who you dont agree with politically.
Let 5.6 free, keep fable chained and anthropic instantly sees rev loss and has to cave.
If this is the new norm, we as workers should all start look for jobs in those companies.
The clowns in the US administration can barely remain coherent from one sentence to the next.
Having them be the gatekeepers of technological progress in 2026 is fucking lame.
[0]: GrandpaCAD.com
(I work at OpenAI.)
FFS. I hate this world so much. I wish I could just flip a switch and never have to hear about or have anything to do with AI ever again.
Do you ever stop to think about the horrific dystopia you and your acolytes are creating?
I'm looking at you Codex.
Doesn't that undermine all good-faith discourse on cybersecurity safeguards, controlled usage etc? Or is that overstating the case (I'm not a security researcher myself so kinda parroting).
Also just making the model better at code is just making it better to writing offensive code.
If what you need is only possible with the more capable model then the "affordability" of the less capable model is sort of irrelevant. If what you need is a novel mathematical proof, it doesn't matter that a high school student is "more affodable". You need the math PhD.
As "old" models get more and more capable, it's going to be an increasingly important skill to be able to adequately recognize when a task requires a frontier model and when it doesn't, so that the less capable (and therefore cheaper) model can be used.
Mythos/Fable is supposedly next generation in size vs Opus, and is rumored to have some architectural innovation in terms of dynamic routing/compute, possibly only fully enabled with Fable which at $10/50 is still twice the price of Sol 5.6's $5/30, but a big reduction from Mythos preview which had been an astronomical $30/150 possibly due to the dynamic routing not yet having been enabled.
Is it just me, or does it seem like Anthropic has been more of a pioneer the past few years, and OpenAI tries to copy features they like?
In many companies, it's IT who will have major input into which company they sign up with as non-technical leaders need guidance, and by making IT fan boys of Claude Code, the enterprise contracts followed.
[0] https://builtin.com/articles/openai-side-projects
I mean, you can read them even without the colors, but who on earth thought that those are a good set of colors? Oh, I forgot it was probably someone on 'Sol'.
I'm not colorblind and I was depending on the textual context implying Sol was better than Terra. I had to zoom in quite far to actually differentiate between the colors.
If they insist on terrible colors would it be so hard to differentiate by marker shape or line dashing too?
https://news.ycombinator.com/item?id=48678789
https://news.ycombinator.com/item?id=48683021
1. Naming convention is copied from Anthropic and honestly is more catchy than a number (amongst normal people)
2. How in the world did Anthropic have to do all the theatrics about Mythos just to have OpenAI release an equivalent or stronger model a month later without any drama???
3. Cheaper models are just don’t fit any usecase imo and OpenAI knows it so they keep increasing the floor - I’m still convinced task per capability is reduced with each release
4. How in the world would open source models keep up with the multi layer security? Either this security is all theater or we will finally see a ceiling in open source models because by definition they can’t have those protections
5. Cybersecurity things are boring to me because it’s all zero sum cat and mouse games
Anyone know the latest around Fable being re-released after gov smackdown?
I mean, if they deem Fable 5 to powerful to share with the rest of the world, what's left for us?
Sol Ultra ≈ Pro
Sol ≈ Standard
Terra ≈ Mini
Luna ≈ Nano
> GPT‑5.6 is priced per 1M tokens across three model sizes:
> Sol is $5 input / $30 output;
> Terra is $2.50 input / $15 output
> Luna is $1 input / $6 output.
The OpenAI casino has never been more ready to take your money on gambling even more tokens.
You can do it easily if you use in fast mode.
I bet you could hit the limits of the $200/month using fast mode if you were using multiple sessions at the same time all day on fast mode.
The OpenAI tiers seem pretty well tuned.
I used to use the plus ($20/month), and that was good for a few sessions every once in a while.
But now that I'm using it to configure my network, monitoring, maintenance, I'm using it every day and I'm on the $100 plan. And I do pretty consistently hit the limits, but it's easy to pace myself.
I'mam thinking about upgrading to $200/month though. It would be nice not to have to ration it.
> For GPT‑5.6 and later models, cache writes are billed at 1.25x the model’s uncached input rate
Charging for cache writes is cringe and literally only Anthropic did it. Anyway this does mean the "real" prices are +25% on top of what you wrote there.
Edit: yeah. https://claude.ai/share/06fefe02-4299-44da-8c5a-42607f54ca77
Not them joining Anthropic with this bullshit. *
Caching infrastructure is already a leaky abstraction over a feature that is not as reliable or debuggable to the end user as it should be, charging for the 'privilege' of interacting with it is really annoying.
(* for reference on 'this bullshit': ChatGPT previously didn't require anything special for a basic level of caching. Unless you wanted extended cache times, it'd just "do the right thing" and try to use nodes that had your prefix already cached in memory)
Every conversation you have with these "more capable" models will be monitored and joined up and then your entire account might one day be tagged as Distiller or Cyber Threat Actor or whatnot. When combined with identity verification (which isn't discussed in this press release), expect people to be falsely flagged and banned from ever using OpenAI models again.
Wish I could find the thread from last week where discussions of exactly this kind of thing were dismissed as daft and outlandish.
That would be the best case scenario. More realistically a few wrong prompts is going to get you on a government list, and if you’re an immigrant some dark cell.
https://pbs.twimg.com/media/HLwuJLvbwAAOfQZ?format=jpg&name=...
"Please don't post shallow dismissals, especially of other people's work. A good critical comment teaches us something."
https://news.ycombinator.com/newsguidelines.html
Beam me up Scotty. No intelligent life forms on this planet.
If you're asking what the average person can do, then the civic perogative is political action to help elect more AI-cognizant leaders.
Heck there's Fart coin, Harambe coin, Dog Wif Hat coin, you name it coin...
I personally don't think it's likely that OpenAI would post completely fake numbers in this pre-IPO period, but if you do, this is an opportunity.