> 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.

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

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)

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.

[0]: https://openai.com/index/openai-broadcom-jalapeno-inference-...

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.

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...

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.

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.

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.

Its happening to Anthropic Haiku and Gemini Flash/Flash lite. All of them are increasing prices and deprecating cheap models.

> stay with the models we actually want

If you want control over the models you use, you have to self-host.

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?

5.5 is smart enough for 99% of my tasks. I need that level of intelligence at ever decreasing prices.

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.

Hardware hosting old models isn't hosting new models. If you want consistent models, host your own open weights ones.

> 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.

who tf would use mini when you have dsv4 flash

discontinuing the cheaper options is a risky move for openai

will trigger re-evaluations of models by other labs + inference providers

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.

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.

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.

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.

> I even referenced multiple code bases on GitHub

Well, GPT referenced every GitHub code base, no wonder it won! :)

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 :)

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

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.

If it's anything like Claude Ultracode, it burns 3 million tokens in half an hour with a single prompt.

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.

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.

I'm shocked they didn't use subagents already. Maybe they're just talking about their web deployment being unified with codex?

Claude also has ultra code mode which is exactly the same thing. This seems to be different from pro however.

Codex Spark models already run on Cerebras

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.

Open models arrived. They are not even that far behind anymore. But the hardware costs are a bit too high for now.

there is no GPT 5.6 init, so what's the point?

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)…

Given the expectations everyone has created GPT-6 has to pretty much be AGI.

They forgot how to do pretraining.

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.

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…

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.

5.5 Pro is $30 in / $180 out: https://developers.openai.com/api/docs/pricing

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.

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"?

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.

Some assume it was to try to slip under the radar and avoid being limited by the government as they did with Fable.

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

Wouldn't that be a stork?

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.

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

> - their internal representations are disgusting compared to JEPA

You say this based on a theoretical understanding or did you inspect them?

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

Why not? I’d bet most HN readers don’t know what GPT stands for

Can only hope. Anthropics usage caps are horrible

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".

I would love to see a more descriptive review from simonw instead of just SVGs generations.

Come on openAI - add @simonw to your privileged team before the plebs start a revolution!!!

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?

The Sun is bigger than the Earth which is bigger than the Moon.

New guardrails only allow you to code in rust. Just imagine.

It’s still easy to understand as the more capable the model the bigger the celestial body they’re named after.

https://news.ycombinator.com/item?id=48690510

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

The EU has regulations, the US doesn’t, it’s whatever Trump and his cultists decide

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 GPT-5.6 Sol will at-the-least be affordable.

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

TBF, they did it first with ada/babbage/curie/davinci. "Sol" is a much weaker branding, though.

sol has no soul

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.

[0] https://builtin.com/articles/openai-side-projects

> 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?

We have le chaton fat, worry not

There are 3 models. Ultra is just a reasoning setting.

AI marketing washcycle is very efficient.

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.

> 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

"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

It's either please the Orange or don't release at all (or worse). OpenAI's leverage is limited; even Anthropic folded.

https://news.ycombinator.com/item?id=48690510

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?

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?

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...

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.