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.
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
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...
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
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)
“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 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 surprise if 1% of it were genuinely novel stuff worth folding back into the 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?
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.
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.
"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
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]
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.
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.
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.
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.
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.
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.
> 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.
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.
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 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.
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...
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.
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
-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.
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.
> 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.
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.
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.
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?
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.
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.
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.
* 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?)
> 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.
"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
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.
What I find amusing is that people where mocking EU for regulations and now this is happening in the US.
I know that Europe is behind in AI but still...
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.
The space is mature enough that pricing should largely be disconnected from underlying cost. Basically, they are selling it for $X because that’s what the market expects the latest Pro-level frontier model to cost.
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.
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.
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.
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
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.
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.
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.
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.
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').
> 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.
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).
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?
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.
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.
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?
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
> 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)
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?
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.
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?
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.
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.
All: for comments on the policy side please go to this related thread:
U.S. government will decide who gets to use GPT-5.6 - https://news.ycombinator.com/item?id=48690101
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.
For comparison, openrouter says opus 4.8 is ~55 tokens/s and fast mode is ~102.
750 tokens/s for their largest model is going to be nuts
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
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...
Try gpt-5.3-codex-spark - it's 1000 TPS and from my experience more capable than 5.4 mini.
If you have a subscription it's a different pool of usage.
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
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)
Faster tokens = more reasoning loops, so it can actually make the models smarter as well.
“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.
This may have been the case one year ago, but with contemporary models such as Opus, I run into this less often.
> how many other people have encountered a problem close enough to yours and solved it somewhere on the open internet
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.
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 surprise if 1% of it were genuinely novel stuff worth folding back into the 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?
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.
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.
[0]: https://openai.com/index/openai-broadcom-jalapeno-inference-...
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.
Cerebras is different than what jalapeno is.
Jalepeno is for mass scale inference.
Cerebras is extremely expensive and difficult to scale, hence the limited release.
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.
"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
Here is a trend I'm noticing:
- 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.
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]
[0]: https://artificialanalysis.ai/models/comparisons/deepseek-v4...
Unless you are hosting it yourself on your own infrastructure it absolutely can be taken away.
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.
Most FSF guys actually have very nuanced views on the topic and you’re doing everyone a disservice by reducing it to an extremist sound bite.
Thankfully he didn't say that they're all like that. Instead he pointed out the few that are as a well known example of similar behavior.
If you reread the comment with a fresh mind you'll notice that you misunderstood what he wrote
It is the FSF itself who has these extremist views.
Unless the US Gov bans inference companies from serving Chinese models to US customers...
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.
No. As long as you downloaded the weights, you can run them somewhere.
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.
>Unless you're running Linux yourself, it can absolutely be taken away.
There is actual market competition to host open models. If one provider stops offering a model you likely can find another provider that will
But you have multiple providers, not just one.
And every single one of those providers would buckle under government pressure.
Fable itself is hosted on all major cloud providers. How many offer it today?
This seems a little fanciful.
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.
The providers on OpenRouter are not all in the US.
That doesn’t mean they are immune to US laws. If they want to continue to operate in the largest market in the world they will fall in line.
And if you are a legit American business you aren’t going to illegally bypass import/export controls.
More importantly, the download is out there. You can download it yourself today, and if it's that important to you, you can buy the hardware too.
I'm sure he's referring to the tightening of internet controls around social media as an extrapolation to controlling websites, etc.
Even in that case it can't be taken away; GPT and Claude are banned in China yet there's still a huge black market for tokens.
Popular open models on Openrouter have dozens of providers.
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.
“More intelligence” is the new feature. Almost everyone is asking for this.
Citation: have you looked at OAI and Anthropic’s customer growth numbers?
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.
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.
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.
Its happening to Anthropic Haiku and Gemini Flash/Flash lite. All of them are increasing prices and deprecating cheap models.
> 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.
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.
Eventually the pricing should be more stable.
> 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.
Hardware hosting old models isn't hosting new models. If you want consistent models, host your own open weights ones.
discontinuing the cheaper options is a risky move for openai
will trigger re-evaluations of models by other labs + inference providers
I can speak for myself. We are exactly at this moment trying to replace GPT 5 mini with an open weight / open source model. No luck so far.
who tf would use mini when you have dsv4 flash
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.
Yeah, this is the classic silicon valley strategy of selling at a loss and then once they have captured the market inflate prices.
See Uber, Netflix, etc.
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.
Doesn't feel like Uber/Netflix.
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.
Are you only considering coding use cases?
Many enterprise use cases, such as simple data extraction, are well served by cheaper models.
Do I need the most intelligent model to generate boilerplate code, which is my main usage for AI? Resounding No.
For my use case a model from a year ago is good enough
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...
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.
I am on the opposite camp. Open models are starting to perform better. GPT 5.5 keeps on messing things up.
On the contrary, pi + glm + DeepSeek… bliss.
Fable was a different kind of beast though. Rip.
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.
Ditto on GLM 5.2 + DeepSeek V4 Flash combo.
For most important work (complex, cross-domain inquiries etc.), I still rely on Codex GPT 5.5 though.
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.
Is it possible for you to provide examples? What were you trying to solve? What was your solution and why was GPT's solution superior and faster?
I'm suspect on how much of a coding advance it will be.
Seems odd that their announcement has zero coding benchmarks, with the closest related thing being terminal bench.
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
They claim extreme performance on ExploitBench, which Mythos was touted as being incredible at. https://x.com/OpenAI/status/2070555278576439306
On graph, they are still slightly bellow Mythos. Maybe enough to not be prohibited by US government?
> I even referenced multiple code bases on GitHub
Well, GPT referenced every GitHub code base, no wonder it won! :)
How do you judge what is a good or bad thing to learn from a LLM? So you don't have to unlearn the bad bits later
There's a lot of tacit knowledge in programming.
-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.
What do you think of modern open-source codebases presently available to the public? Is closed-source/proprietary code that much better?
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.
> 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.
Confusion is: ultracode is not a different model with its own benchmarks
Neither is OpenaAI's ultra. Article specifically calls it 'mode' and it's not even mentioned in the model card.
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...
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.
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.
With pi they’re an extension, but that’s pi
Absolutely yes
I'm shocked they didn't use subagents already. Maybe they're just talking about their web deployment being unified with codex?
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?
Claude also has ultra code mode which is exactly the same thing. This seems to be different from pro however.
Previewing <minor version bump>: a next-generation model
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.
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.
Did GPT-5.6 Sol Ultra decide the terrible colors for the benchmark graphs?
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.
Time to create more LLM based startups.
Keep moving don't doom.How are they able to compare with Fable when Fable was only available for three days?
> 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.
"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
I looked at the charts and it is clear that 88% from OpenAI is more than 88% from Anthropic.
Some interesting stats here about the current landscape https://arena.ai/leaderboard/agent
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
What I find amusing is that people where mocking EU for regulations and now this is happening in the US. I know that Europe is behind in AI but still...
The EU has regulations, the US doesn’t, it’s whatever Trump and his cultists decide
> Sol, Terra and Luna
So the next naming scheme might be FTX, Madoff and Enron? :^)
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.
[delayed]
The space is mature enough that pricing should largely be disconnected from underlying cost. Basically, they are selling it for $X because that’s what the market expects the latest Pro-level frontier model to cost.
We need more coding benchmark score. Not sure that winning terminalbench 2.1 alone is a clear win over Fable/Mythos yet.
But they are the only ones who can benchmark, so the best and only benchmark will be the one where they win. It's just business baby.
If GPT-5.6 preview is not available outside US government approved "trusted partners", I don't see how the General Available can be trusted later.
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.
Open models arrived. They are not even that far behind anymore. But the hardware costs are a bit too high for now.
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.
[0]: GrandpaCAD.com
Is there a list of Gov-approved companies?
If this is the new norm, we as workers should all start look for jobs in those companies.
Like Mythos before it, I'm simply not excited about a model I can't use
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.
(To be clear: I do not like this new paradigm)
If it's a new generation why isn't it GPT-6?
Given the expectations everyone has created GPT-6 has to pretty much be AGI.
They forgot how to do pretraining.
5.5 was a new pretraining run.
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.
A major bump will be warranted if/when we can truly separate prompt from data.
That is a different product line. It may be recorded as a version bump for marketing purposes, as already mentioned, but semantically begins at 0.
What would it mean to be incompatible with the other 5.x models?
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.
Only speaks Klingon
not true. multimodality is still far from being solved
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
The choice of the name Sol is interesting for those Raised By Wolves fans out there… “Praise Sol!”
Waiting for @simonw to report on this, before I read and try it
You might be waiting a while, I'm not in that set of "a small group of trusted partners whose participation has been shared with the government".
The government doesn't have a Department of Vector Pelicans?
They have many that sometimes act like ones
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.
I would love to see a more descriptive review from simonw instead of just SVGs generations.
I try! https://simonwillison.net/2026/Jun/9/claude-fable-5/ and https://simonwillison.net/2026/Jun/11/fable-is-relentlessly-...
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.
Come on openAI - add @simonw to your privileged team before the plebs start a revolution!!!
"Next generation model"
If it was the next generation, why isn't it a major version change..?
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"?
[delayed]
You gotta check out the new ChatGPT 6.3 Betelgeuse bro
If they called it 6.0 and it wasn't AGI, you'd see a lot of complaining here too
LLM devs can't do version control
Honestly LLMs are the ideal candidate for CalVer. It’s not like there’s any real API so there’s no backwards compatibility to maintain.
Even Apple adopted and standardized on it for their latest platform releases.
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.
Some assume it was to try to slip under the radar and avoid being limited by the government as they did with Fable.
By all appearances, they did not succeed in doing so.
Because if it sucks, they can just default to "It was a minor version change anyways"
They could hold the GPT-6 name for the IPO
Semantic is passé, word models moved to the next generation.
vibe versioning
To be fair, versioning has always been vibes based.
I'll buy that its next generation if the svg bicycle pelican is carrying a baby
Wouldn't that be a stork?
I feel a bit like a Soviet hearing about Levi’s or the latest Springsteen release. C'mon!
It appears that between GLM-5.2 and GPT-5.6, anthropic is feeling the heat, atleast in the bang-for-the-buck heuristic?
Can only hope. Anthropics usage caps are horrible
When will GPT-5.6 Protomolecule drop? Me and the boys on Eros can't wait to get our hands on it!
Oh man, here inside Ganymede I'm way more excited about the GPT-5.7 Io experiment! Hopefully it won't blow up in our faces!
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.
I'm excited for GPT-5.7 Pneumonoultramicroscopicsilicovolcanoconiosis, hope they drop it soon
GPT-5.8 Llanfairpwllgwyngyll
You mean Llanfairpwllgwyngyllgogerychwyrndrobwllllantysiliogogogoch ?
… do you folks listen to Soft Skills Engineering? This has been a running joke on that podcast for a while
What is happening. I feel like I'm getting an aneurysm reading these comments.
It's the name of a place in Wales, which has made it a running joke for decades!
For me, it's GPT-5.9 Year of the Whisper-Quiet Maytag Dishmaster
I think Aramco GPT Coca Cola 6.0 will be a step change.
> We plan to make them more broadly available to people using ChatGPT, Codex, and the API soon.
I hope this means then fable will also get released again.
why would it? if you're the us gov and sam&greg your good boy giving you 25m
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.
Seems like OpenAI has succumbed to the urge to give their models catchy names like Anthropic does
Why not? I’d bet most HN readers don’t know what GPT stands for
GPT is kind of a stupid name when you stop and think about it.
Sun Earth Moon
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.
Pleasantly surprised that it costs as GPT 5.5, thank god for the competition.
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.
The Sun is bigger than the Earth which is bigger than the Moon.
There are infinitely many 3-level hierarchies. My point was about overloading the model sizing with one more unnecessary classification.
all the emphasis on cyber security. feels like a reaction to anthropic, not a real next generation.
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 work at OpenAI.)
how could that _not_ be the emphasis given what's happened with Anthropic and the Trump admin?
Would love to see benchmarks on cognition's FrontierCode
> 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.
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).
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?
I'm looking at you Codex.
It’s still easy to understand as the more capable the model the bigger the celestial body they’re named after.
I'm really getting sick of reading about safeguards and what I'm not allowed to do on every model release.
New guardrails only allow you to code in rust. Just imagine.
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.
What is this prediction based on?
I suspect the same just based on their versioning scheme fwiw.
solid
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.
I suspect GPT-5.6 Sol will at-the-least be affordable.
"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.
Affordable? I'd settle for available.
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.
why
Because he likes attention and wants to feel special
Seems like OpenAI's strategy to release models after Anthropic has been paying off.
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?
Sol? Looks like openai is jealous of anthropics good model naming ability and wants to emulate it.
TBF, they did it first with ada/babbage/curie/davinci. "Sol" is a much weaker branding, though.
sol has no soul
They should have used Figher Jet codenames instead. The MiG-15 one has a nice ring to it.
Sol Goodman
It's missing u
No comments on the cerebras version that might finally enable intelligent voice mode instead of being stuck with 4o-mini class
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.
Could not care less.
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?
Thoughts
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
Another model family, another naming scheme to get used to.
Sol Ultra ≈ Pro
Sol ≈ Standard
Terra ≈ Mini
Luna ≈ Nano
There are 3 models. Ultra is just a reasoning setting.
AI marketing washcycle is very efficient.
> 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)
Pre-official discussions:
https://news.ycombinator.com/item?id=48678789
https://news.ycombinator.com/item?id=48683021
they're trying to be anthropic with these model names
Not really news until it's widely available.
Anyone know the latest around Fable being re-released after gov smackdown?
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?
We have le chaton fat, worry not
I hate not being able to use the latest models. There needs to be a much faster resolution to whatever is happening with the federal government.
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.
... they have been doing this the entire time
Guess it's just another price bump hidden behind output token speed.
TLDR - It's not quite Mythos but it uses about 5 times less tokens, and those tokens are also cheaper?
https://pbs.twimg.com/media/HLwuJLvbwAAOfQZ?format=jpg&name=...
[flagged]
"Don't be snarky."
"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
This is disgusting groveling to the Orange Shit Stain.
Beam me up Scotty. No intelligent life forms on this planet.
It's either please the Orange or don't release at all (or worse). OpenAI's leverage is limited; even Anthropic folded.
So we just bend down then?
Unless you work at OpenAI/Anthropic/etc., you are not a part of the "we".
If you're asking what the average person can do, then the civic perogative is political action to help elect more AI-cognizant leaders.
Other than the worst naming I have ever seen (Sol / Terra / Luna), the pricing is still expensive:
> 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.
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?
If you've never hit the limits, why not do the $100/mo plan?
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.
But let's put it in perspective: what you're paying them is the gross income per person of various poorer countries.
I ran out of usage using GPT-5.5 and had to buy a second subscription. I now switched to GPT-5.4 which is basically 2x usage.
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.
What don't you like about the naming?
I feel like going with Space + Latin is LLM-level creativity.
Edit: yeah. https://claude.ai/share/06fefe02-4299-44da-8c5a-42607f54ca77
Doesn't it strike anyone as strange that SOL, TERRA, and LUNA are all quasi-scam crypto tickers?
There is a crypto ticker for literally any catchy short string.
There's also Fable coin, Mythos coin, and Opus coin all of which predate the Claude models.
Heck there's Fart coin, Harambe coin, Dog Wif Hat coin, you name it coin...
whoa, a new model that surpasses benchmarks of other models? wild.