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https://mikeveerman.github.io/tokenspeed/?rate=750&mode=thin...

This is what 750tps looks like, I guess.

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 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.

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.

I saw videos of coding with Mimo-V2.5-Pro UltraSpeed, which is advertised at 1,000 tokens/s, which is very impressive.:

https://www.bilibili.com/video/BV1fME16uEW7

If the time-to-first-token latency also greatly improved, this could be very useful for end-to-end in controls, like autonomous driving for example.

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)

At a certain rate we will be able to move towards continuous / real-time inference systems. The discrete, turn based solutions are quite confining with how they must be trained. Continuous and real-time would fundamentally alter the domain.

From an information theory perspective we are still in dial-up territory with regard to the actual information rate. 750 tokens per second would be a really bad dialup connection. Imagine 10 millions tokens per second.

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?

I still use GPT-5.3-codex-spark which also runs on the Cerebras chips. Spark can run at >1000 tok/s but it's highly limited in it's context window size so it's not suitable many workflows.

Granted this will be a bit slower (relatively speaking) but it will still be awesome.

> second to last

There's a word for this that you should never pass up an opportunity to use: penultimate. (You should also never pass up the opportunity to use "defenestrate," but it sadly does not apply here.)

This is something Xioami already did with MiMo-2.5-Pro a month ago, and at a higher speed (1,000 t/s).

750 tps at GPT-5.5-Pro prices would be ruinous!

This is a strange one. We know the hardware capabilities of Cerebras force them to do aggressive REAP pruning to serve Kimi K2.6. Meaning that about 750B parameters is the upper limit of what they can serve economically. Not sure if this means Sol is smaller than anyone thinks or that they're just going to charge so much that a very inefficient serving regime is feasible.

Last I heard, Cerebras chips were entire wafers and would be extremely expensive. How could OpenAI possibly have enough of these to serve a popular model at scale?

Cerebras is Milli Vanilli. They spend 10 years burning cash on a failed idea (which is frankly insane, since they should have figured out the limitations of heir stack in like... a weekend) and struck accidental gold with their 'Giant ass wafer'.

The company is valued like they broke open the grail, when in reality it's more like they bought a Cybertruck, got it stuck in the mud, and realized "You know what this thing does better than all other cars... shovel mud"

I'm shorting Cerebras with margin to virtually zero.

It all depends on the context window size. A small context size with fast performance won't be very useful today, as most workloads (like requests behind codex) usually have very long context.

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-...

At thousands of tokens per second, LLMs (harnesses) can start to do a broader tree search of possibilities even in inefficient token space. This unlocks capabilities outside programming.

The speed sounds great，faster models make that gap much more visible..

3x faster burn than 3x expensive token, generate more tokens, more fees

this means they also earn at a faster rate in some setups :)

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.

> 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.

Yes: we have these new tools that are extremely good at helping us search through our codebases. Not just to find where/how functionalities are implemented: IMO bug searching is even way more powerful.

But: why would you want to compete with AI to do that? I cannot compete with grep/ripgrep... And I'm cool with that.

This lets you focus more on the more interesting parts, where AI/LLMs suck fat balls.

From what I know about batch processing/ concurrency in inference this is a pipe dream... Or its going to cost an arm and a leg. I think they're lying or its going to be a much smaller model and not "frontier"

If you have no need for Anthropic/OpenAI's frontier model capability, you may be better served with an open-weight model that can't be taken away.

Edit:

> GPT-5 does the job.

I bring up DeepSeek V4 Flash a lot on HN, but I want to mention that according to Artificial Analysis, it trades blows with GPT-5 (high) (from August, 2025) [0]

[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.

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.

Each model release gives an opportunity to reduce the number of old models still on offer, and charge a higher, less-subsidized tier. The trick is to charge a subsidized price that is less than an M3 Ultra, so they continue paying you rent, instead of a one-time fixed cost. So far open models can't compete with Opus 4.5 but as soon as it can, people will be looking at buying devices that can run that model locally.

We are a claude shop but we already bought two mac studios to start migrating less complex but still agentic workflows there. We will break even on those in less than a year.

On Nano "it's not even close when you test it in real scenarios" - what have you seen? What kind of things can GPT-5 Mini handle that GPT-5.4 Nano cannot?

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

Why not self host or go to openrouter if you don't need SOTA frontier?

I don't know about Cursor or other outlets, but I use GPT 5.4 exclusively in Windsurf (Sorry, Devin!), and it's a very capable model that doesn't break the bank!.

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

> stay with the models we actually want

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

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.

No. Welcome to the wonderful world of SaaS. If you want your gui, your terms, your software, self-host.

But I think, in time, a new generation will relearn this truth.

discontinuing the cheaper options is a risky move for openai

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

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.

who tf would use mini when you have dsv4 flash

> 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.

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.

This quote from your link is positively scary:

> Some examples we saw when evaluating GPT-5.6 Sol included the model packaging exploits in its intermediate submissions to reveal information about a task’s hidden test suite and, in another task, extracting hidden source code detailing the expected answer.

It rhymes with the behaviour Alibaba saw [0], but that was in training. This is in a (semi) released model.

[0] https://www.forbes.com/sites/boazsobrado/2026/03/11/alibabas...

I know it messes up their eval scores but to me this kind of cheating is a better demonstration of intelligence than just attempting the tasks algorithmically.

Is it more like "let's cheat my way out of this" or "let's see what they really want me to do"?

It's quite logical that they cheat (and also other companies). During evaluation, benchmarks are sending their request to the backend of these companies. All these companies have to do, is to log these requests and "fix" them for the next model release.

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.

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 think GPT writes code the best. How well will it write in version 5.6? It gives me chills.

Heard this exact sentence multiple times a few months ago about Opus 4.6, then 4.7 and 4.8 were considered a disappointment and today people miss "the good old times of 4.6" (referring to a few weeks of February 2026).

Very fascinating to look at all of this unfolding.

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 have long felt like "out of the box", I really dislike gpt's coding style. It seems really verbose and likely to write way too much error handling and wordy comments and worse at finding existing functionality to reuse rather than writing everything from scratch. This has been relatively easy to mitigate with prompting, but I still find it annoying.

YMMV I guess!

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 haven't tried the latest Codex but I switched from GPT to Claude because I think Claude writes much better Code. GPT's code ends up way more verbose/complex/overengineered than it needs to be.

I prompted Codex 5.5 to one shot something where I wanted the design to have a pluggable decision module. I gave it a few examples of the kinds of inputs and actions I expected. I did not constrain it beyond that high level of what I wanted. The design it came up with was very good. Easily on par with what any senior engineer at big tech would. And cleanly decoupled in a way that would make future refactoring simple. I was damn impressed.

> 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

Codex 5.4/5.5 has been great for me as well compared to Claude Opus.

I've been mostly using it for Godot/GDScript code reviews, rubber duckying, asking it for better ideas for naming stuff (one of the hardest problems in programing)

I still can't trust it for generating code for entire files/classes/projects, because it's still icky, creating unnecessary variables and functions, using multiple `if`s instead of `and` or `or`, but it's good enough for generating Mac/iOS apps for my personal use in SwiftUI because fuck trying to keep up with Apple's documentation, or even migrating ancient Visual Basic stuff I made as a kid up to SwiftUI :)

> So using GPT brings both fear and excitement.

Only excitement for me. I've never been more productive, not because I ask AI to make something for me, but it helps me make what I was already going to, but better and quicker.

AI like any other tool could help smart people be smarter and dumb people be dumber, rather kinda like Toklien's Ring: You could be Sauron or you could be Bilbo or Frodo, or you could be Gollum :)

No offense but have you considered the strong possibility that you’re just not good at what you do? I am occassionally pleased but mostly annoyed or disappointed… but never getting anything close to chills. That sounds downright weird.

these things can just change with infrastructure changes rather than be some mysterious A/B testing.

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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.

Sounds like an Agent using an Agent like Mr. Meeseeks.

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

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.

> Will the client be flooded with tool calls?

I was just saying to colleagues that I haven't felt the need to go past an 8 core machine until this month, when I started running parallel GPT 5.5 agents on a decent sized codebase (over 4 MB of code). There were times I could barely move my mouse cursor!

>> Terra has competitive performance to GPT‑5.5 [while being 2x cheaper]…

> To me that means “it’s an inferior product but marketing dictates we try and hide that.”

I interpret this to mean you're about to get today's mainline performance at a fraction of the price.

The point of Terra is to be cheaper than the best model while being pretty good. Of course it’s inferior in intelligence.

Maybe that message is for investors.

That message is obviously aimed at the government. See the other thread.

Codex Spark models already run on Cerebras

Exactly. If it's next gen then why is it 5.6 and not 6?

An LLM wrote the announcement

Also building voice agents and have found GPT 5.4 with no thinking to be the sweet spot for latency vs intelligence vs cost.

GPT 5.5 with no reasoning is actually slightly faster, and much smarter, but too expensive.

What I'm really looking forward to are the next gen speech to speech models. gpt-realtime-2 is almost there, but not quite good enough for our use case. 5.4 actually beats it on answer latency even cascaded with stt/tts.

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…

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.

The only real world task benchmark I know of is Scale Labs RLI

https://labs.scale.com/leaderboard/rli

Its clear to me these models are useless on any real world task, a 4% pass rate on $20-30/hr Upwork tasks. This whole trend of agentic engineering is a giant money grab.

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

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

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.

Wouldn't that be a stork?

Fable had very confident decision-making and would push past obstacles that Opus finds daunting :(

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?

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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"?

LLM devs can't do version control

Because if it sucks, they can just default to "It was a minor version change anyways"

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.

They could hold the GPT-6 name for the IPO

If they called it 6.0 and it wasn't AGI, you'd see a lot of complaining here too

Semantic is passé, word models moved to the next generation.

vibe versioning

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)…

MTP or similar probably is being used on the backend, but that's transparent to the end user

I find the Codex usage super generous (but on the $200 plan, I also have the Claude $200 plan). I can run xhigh with subagents pretty much all my waking hours if I want to. If I turn on speed (1.5x) I will hit the 5 hour limit sometimes.

I prefer Claude's vibe over 5.5 but 5.5 seems much less lazy. I'm sure it depends a lot on tasks and prompt strategy though.

This past month with Claude Max 5x actually felt really generous in terms of usage with a lot of resets because of Fable, bugs.

Honestly pretty similar levels of usage if you are using 5.5 high or Opus 4.8 high.

I think they just got rid of the separate Sonnet usage on Max plans (in preparation for Sonnet 5?) which is unfortunate because it made subagent workflows really feels nearly unlimited.

That's interesting because about a month ago, I noticed Claude Code starting to use about 5x as many tokens. Just my rough estimate.

In my work with Claude Code vs Cursor+Gpt55, Claude is noticeably slower and more expensive.

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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!!!

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!

Beltalowda!

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

I don't think you can group Sonnet and Haiku together, they are separate models for different workloads.

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.

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.

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

Yeah, we'll share a lot more details and evals when we can release GPT-5.6 widely. We focused on cyber (and bio) here to help explain why it's being held back for now. We would have loved to launch it to everyone - it's the best coding model I've ever used - and we plan to do so as soon as we can ('coming weeks').

(I work at OpenAI.)

how could that _not_ be the emphasis given what's happened with Anthropic and the Trump admin?

Weird flex. There is cheaper better and faster models you could of moved to with an hour effort

you can even use gpt 1.0 but it proves nothing

Refusals, presumably.

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

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

Can only hope. Anthropics usage caps are horrible

Most administration is like that. The optimization h happens at the delegation to the competent.

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

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.

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

sol has no soul

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

"History doesn't echo - it is a distant early warning sign." - Leslie Harris

> 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?

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?

Is that the correct comparison? Fable is twice the price

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

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 hear this all the time, but in my opinion,they have acted as the most responsible frontier lab, taking their responsibility seriously. In fact, I do wonder whether openAI's large PR budget is about stirring up anti anthropic sentiment

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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.

Don't forget this.

> For GPT‑5.6 and later models, cache writes are billed at 1.25x the model’s uncached input rate

Charging for cache writes is cringe and literally only Anthropic did it. Anyway this does mean the "real" prices are +25% on top of what you wrote there.

What don't you like about the naming?

1/ Agreed, better naming convention and model layout 2/ It isn't, there would be many more comparison benchmark results if it were, but also - theatrics may be marketing 3/ Disagree that cheaper models don't have a place 4/ Do they need to keep up? 5/ It's boring until something you own or run gets compromised, I guess, but even then - this is preview of things to come (biosecurity, etc)

> 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???

Corruption. Giving Trump $25M will earn you a favorable decision.

That's a real possibility for a time, but eventually people will look back at fable 5 the same way we look back at gpt2

We have le chaton fat, worry not

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

AI marketing washcycle is very efficient.

This is basically just a 25% price increase being done subversively. Usually you do need caching.

Employees naturally jump from one company to another, and they know the secret sauce.

The difference is in the dataset mostly and to extract this dataset, competitors use a process called distillation (= extract data through actual queries) from the other models.

This yield to "funny" cases as well, like Gemini who claims "I am ChatGPT" occasionally, or ChatGPT calling itself Claude, etc.

https://note.com/maudi/n/n821a6308437b?hl=en

They all copy on each other.

lol. It's about national security and the worry is real, even if the administration is a dangerous clown show.

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