U.S. government will decide who gets to use GPT-5.6 - https://news.ycombinator.com/item?id=48690101
"We're also launching GPT‑5.6 Sol on Cerebras at up to 750 tokens per second in July, bringing frontier intelligence to customers at unprecedented speed. Access will initially be limited to select customers as we expand capacity."
750 tokens/s on a frontier model is going to be extremely interesting. I doubt this new version is anything but a version bump in terms of capabilities but if we can start getting these answers back faster, they end up being more useful.
Just off the top of my head, I can think of the tedious task of finding certain functionality within a codebase. I usually can't beat an AI agent harness at this task today. If the AI model is 3x faster I have less of chance.
750 tokens/s for their largest model is going to be nuts
I've always eyed Cerebras but never had a use for it that would justify paying for the API directly. Although now that I think about it, trying out the API would probably cost less than a subscription for a month...
If you have a subscription it's a different pool of usage.
not to say a speed boost isnt there but if they didnt increase tokens / s at all youd likely see things slow down a lot with the new model compared to current
Yup, I remember "racing" the AIs to figure things out in codebases just a year ago. Today, I have no chance. Whether it is due to degraded reasoning capabilities on my part or better models, I don't know.
[1] Not AI codebases (and of course, AI code bases I guess)
From an information theory perspective we are still in dial-up territory with regard to the actual information rate. 750 tokens per second would be a really bad dialup connection. Imagine 10 millions tokens per second.
The new Blackwell hardware combined with TensorRT-LLM and speculative decoding consistently can hit 1,000 TPS/user barrier, comparing to closer to ~250 TPS/user (out of 10k+/TPS on the server)
Is there something I missed, this looks more like 14.4 to 56 on a 64kbps backing channel modem story. I have no doubt that there are still massive gains to be found, but they seem to be using existing constraints more efficiently, not that fios is coming.
I don’t have the budget to work on the foundational model scale, but with a draft model 10x–20x faster than target and an 60-80 acceptance rate I can see how they could promise 750/TPS (with a lot of other hard work) but I would appreciate where I should look to figure out what I am missing.
But I could imagine after each space(eg, word) having a 27b model on a nice rig, with thinking off, doing a quick look at the sentence and determine if it should interrupt and start a real turn with thinking on. Which kind of is non-turn based in a way. If you're typing fast, it might hit that run every 3 or 4 words, but that's sort of how a human might be when a person is talking to them. That is, waiting for enough info to interrupt, if needed.
There might be a way to process chunks of a sentence using commas as break points, eg for comma delimitated phrases in sentences, so the whole sentence doesn't need to be re-processed each "should I break in" assessment at word break.
Could be fascinating. Could actually do some of this right now.
I don't think this is what the parent poster was thinking, but the idea even at this level seems fun.
The idea of true continuous thought and memory-generation is very interesting, though I can't even begin to conceive of how it would work.
Do you feel most of the speed upgrade will come from the software or hardware side?
Imagine a world where there is no code, just things mildly handshaking and then creating data APIs on the fly. Where communication is fuzzy and locked in on an individual basis. No years of RFCs, no RFCs at all, just... data.
Just data, man.
An API arbitration aberratically assigned at authorized access, abridged and annotated, analytically assuring absolute assurance.
In some circumstances there is no substitute for something that you know will produce the same answer for a given input, consistently.
GPT‑5.3‑Codex‑Spark currently runs on Cerebras chips and it's giving me around 150t/s. Still relatively very fast, but nowhere near the 1,000t/s they claimed at launch. (Also it's not a very good model.)
That said, I'm super bought in to faster models being better for most use cases than smarter models.
A lot of the open Chinese models get their results through huge reasoning loops. Being able to boost decode perf is what will make them worth it, and I’m sure OpenAI and Anthropic could do similar (if they aren’t already)
Most of the frontier models can, when prompted and tooled correctly, do a lot of “reasoning” tasks that amount to resolving how the user has explained a particular widely known paradigm.
The more difficult and obscure the issues you provide them with, the faster you notice them reward hacking by altering the criteria until they are no longer attempting to solve the problem. Using “advisor” style loops helps hold this off at the cost of tokens, but there is still a fairly short limit at which they will essentially give up if they can’t find all of the necessary information - sometimes the issue is actually worse if they find a small amount of information instead of nothing - they’ll extrapolate from that tiny piece of data and generate plausible-sounding hallucinations almost every time.
And god forbid your problem involves doing something a different way than the majority of people do it. Unless you can write a full spec on it, the models will repeatedly spiral back into adjusting everything about your problem until it matches one of the most popular approaches in their training data.
I'm 100% sure that all our web, cc, codex or whatsoever sessions are used in the training, RL or either both.
This makes the size of the universe models know about at least one order of magnitude bigger than the open internet.
Of course we can trust that wouldn't name the same thing with different levels of intelligence, right? Right?
[0]: https://openai.com/index/openai-broadcom-jalapeno-inference-...
Jalepeno is for mass scale inference.
Cerebras is extremely expensive and difficult to scale, hence the limited release.
I tend to doubt they would. Cerebras notably doesn't have a kv, is wildly high bandwidth, but within/across the chip, not able to dump/restore kv super well. I doubt openai is going to build something that is as expensive to run. Also, wafer-scale is absurdly hard & weird to pull off, so I doubt that would be their first foray.
Dude, 10x token speed is going to be absolutely nuts. Half the "parallel subagent workflow" business seems to be driven simply as a means to avoid tapping your thumbs waiting for the infernal robot to finish something. If things come back speedy quick all the time, it should keep up with the "speed of the human" and let me stay focused on one thread instead of half a dozen. Plus the cost of screwing up gets significantly lower because you just re-fire with an adjusted prompt and iterate.
Someday these things will be 100x as fast as they are today and that is when things will get insane.
- GPT-5 mini costs $0.25/$2 and will be discontinued in December.
- GPT-5.4 mini costs $0.75/$4.5 and is supposed to be the replacement.
- GPT-5.4 nano costs $0.2/$1.25 and, while it ranks better in benchmarks than GPT-5 mini, it's not even close when you test it in real scenarios.
So you're left being forced to go to GPT 5.4 mini if you use 5 mini today.
The same thing is happening here as their “Luna“ model will cost $1/$6.
Can't we just stay with the models we actually want? I don't need GPT 5.4 mini. GPT-5 does the job.
Maybe it’s the realization that it was never that cheap in the first place and they're forcing us to upgrade in a slow and painful way.
Edit:
> GPT-5 does the job.
I bring up DeepSeek V4 Flash a lot on HN, but I want to mention that according to Artificial Analysis, it trades blows with GPT-5 (high) (from August, 2025) [0]
[0]: https://artificialanalysis.ai/models/comparisons/deepseek-v4...
Deepseek V4 Pro on the other hand is a really really good main driver and we have a lot of success using it. Its not Opus or GPT-5.5 level but on its way. Kimi 2.6 as well btw.. so there is already quite some choice.
I still wish it was a little better, but there's hope for another model checkpoint (maybe with some of GLM 5.2's goodness distilled into it, that would be nice).
I really dislike this rhetoric, you sound like the FSF guys who are like "you're not free until you're running coreboot with zero binary blobs". Sure they have a point but also, most people are fine running regular linux.
https://www.fsf.org/resources/hw
> For example: the Free Software Foundation only purchases desktop machines which support Libreboot, and Thinkpad X200 and X60 laptops with Libreboot. All desktops and servers we buy are KGPE-D16 motherboards, which are supported by Libreboot. As a result, all of the workstations used by the FSF staff have a free BIOS.
https://www.gnu.org/distros/common-distros.html
> Except where noted, all of the distributions listed on this page fail to follow the guidelines in at least two important ways:
> ...The kernel that they distribute (in most cases, Linux) includes “blobs”: pieces of object code distributed without source, usually firmware to run some device.
They are extreme, uncompromising, and live by their principles.
They are also the reason you can buy a computer meeting those requirements instead of being a pipe dream.
If you reread the comment with a fresh mind you'll notice that you misunderstood what he wrote
Regardless, the “misinterpretation” of the parent comment is actually a plausible interpretation. I suspend my judgement on what the actual “correct” interpretation of the original comment is: there are too many plausible interpretations to deductively decide. But I do know that since they first comment brought up a contentious issue, they should have put more work into crafting their message so there aren’t so many plausible interpretations that are contradictory. Or alternatively, they should have specified more precisely who they were talking about without a shadow of a doubt. That is if the commenter cared to be properly interpreted, but that may not be their goal. There are many reasonable reasons why that wouldn’t be their goal.
Fable itself is hosted on all major cloud providers. How many offer it today?
There's really no comparison between a model that Anthropic allows Google and Amazon to host with one that has been downloaded hundreds of thousands of times and has dozens of public inference providers.
Now for the Chinese models on OpenRouter, yea. Those providers could be legit. Or it could be a failed crypto mining operation pivoting to providing AI compute. Who knows.
And if you are a legit American business you aren’t going to illegally bypass import/export controls.
Llms seem to only impress a certain type of person. Hint, this type of person also was really excited about NFTs.
Citation: have you looked at OAI and Anthropic’s customer growth numbers?
However, you said “new versions with features that nobody asked for”, and I would prefer that you concede the point before shifting to arguing a new point.
What customers are asking for is smarter models. Because the tasks that only smarter models can solve are higher value, higher margin, than the tasks that non-frontier models can solve.
I suspect the problem is that they need to charge a lot to keep revenue numbers up, and they are more worried about cannibalizing themselves than others cannibalizing them.
Eventually the pricing should be more stable.
Why do you think so? This game can be played forever, you just need strong marketing and orgs gullible enough to pay a higher price for a minor upgrade.
We are a claude shop but we already bought two mac studios to start migrating less complex but still agentic workflows there. We will break even on those in less than a year.
If you want control over the models you use, you have to self-host.
All the analysis I have seen points to frontier models being profitable to serve. It’s using 50% or more of your GPUs for research plus CapEx for capacity expansion that makes these businesses so heavily cash-negative.
What you are observing is downstream of another detail. It gets more expensive to serve a model as utilization goes down. Plus the opportunity cost vs newer, more-profitable models.
There are plenty of valid reasons to critique here. “OpenAI is lying about this being a sustainable price to serve” is not one of them.
will trigger re-evaluations of models by other labs + inference providers
See Uber, Netflix, etc.
Feels like they are just pulling in as much as they can whilst competing on capabilities instead. At which point its a case of who can last the longest.
Doesn't feel like Uber/Netflix.
How many people do you see using haiku or sonnet? I see very few and most people default to the latest model and just play with thinking effort. I think three layers are good enough and supporting more is not a good UX.
Many enterprise use cases, such as simple data extraction, are well served by cheaper models.
For my use case a model from a year ago is good enough
Also: calling the SV blitzscaling strategy of using VC money to fund loss leader products with the goal of building a monopoly via dumping a conspiracy is quite the position given there's entire books written in the topic...
Inference needs to cache, it can't cache random model data, so it's essentially dedicated; it can't spin up models on demand, it has to know what demand is coming.
These companies are going to end up with very few models offered and that's probably generous. They might end up with just one model and you pay for removing it's safe guards.
Recently, I went head-to-head with GPT on nearly 2,000 lines of code, and GPT's solution was superior and faster. I even referenced multiple codebases on GitHub while trying, but they were incomparable to GPT.
So using GPT brings both fear and excitement.
The fear comes from realizing that this level of code is now the average for most people. The excitement comes from knowing that I can now study and learn at this level too.
I'm really looking forward to seeing how much more advanced the code will be with the upgrade to 5.6.
On the contrary, pi + glm + DeepSeek… bliss.
Fable was a different kind of beast though. Rip.
For most important work (complex, cross-domain inquiries etc.), I still rely on Codex GPT 5.5 though.
I'm working in a 600k+ LoC codebase that has complex domain-specific logic and lots of moving parts. I find that Codex 5.5 is pretty good at surgical fixes, but does not go out of its way to explore and figure out what those surgical fixes might break. So I only use it to work on parts of the system that are pretty isolated from everything else so that risk of regression is small.
Seems odd that their announcement has zero coding benchmarks, with the closest related thing being terminal bench.
Personally, I think this kind of coding experience varies from person to person
If they really thought it was competitive with Mythos/Fable across the board, then why wouldn't they release a broader set of benchmarks, and why price it day 1 at 1/2 the cost of Fable?
Not saying that's the case with OP, but I've found folks sometimes just rationalize it so [0] as they're paying top dollar for it (especially, when compared to may be less capable but affordable models).
Well, GPT referenced every GitHub code base, no wonder it won! :)
-Why do you cut API boundaries this way? -Why do you change the order of struct fields? -Why do you deliberately insert padding?
Most of it depends on the background and context. Sometimes you add it, sometimes you don't. To understand this tacit knowledge, you need access to senior developers. But their attitude often depends on how promising the student is and what background they come from. On top of that, you don't have to rely on the respondent's mood, authority, or availability.
Programming is fundamentally a field that requires seniors. In my case, I had no such seniors at all. I learned to code by buying codebases from failed companies and studying them. My first job didn't hire me as an employee—they hired me as the CEO of a subcontracting company (because that was structurally more advantageous for the contract). So I wasn't given the patience to learn programming fundamentals gradually. I had to pay penalties if I failed. Most of the projects I worked on were the kind where failure meant bankruptcy for me. Naturally, there was no one to teach me.
Most of my knowledge comes from reverse-engineering the code I purchased.
People say LLM code contains falsehoods, but commercially sold code has always had falsehoods too. Honestly, if we're just talking ratios, LLM code has fewer falsehoods.
In that sense, I still think it's a matter of context. If LLM code is false, was human code ever really true? LLMs do lie. They generate plenty of incorrect code. But humans do the same thing. If a problem comes up, you just look it up then and there. For me, LLMs and humans aren't all that different.
I've been mostly using it for Godot/GDScript code reviews, rubber duckying, asking it for better ideas for naming stuff (one of the hardest problems in programing)
I still can't trust it for generating code for entire files/classes/projects, because it's still icky, creating unnecessary variables and functions, using multiple `if`s instead of `and` or `or`, but it's good enough for generating Mac/iOS apps for my personal use in SwiftUI because fuck trying to keep up with Apple's documentation, or even migrating ancient Visual Basic stuff I made as a kid up to SwiftUI :)
> So using GPT brings both fear and excitement.
Only excitement for me. I've never been more productive, not because I ask AI to make something for me, but it helps me make what I was already going to, but better and quicker.
AI like any other tool could help smart people be smarter and dumb people be dumber, rather kinda like Toklien's Ring: You could be Sauron or you could be Bilbo or Frodo, or you could be Gollum :)
A while back I gave the same task to both, and Codex used 20x less of my 5-hour limit (both on the $20/month plan).
(This annoyed me since I tend to prefer Claude, but the limits at the time made it unusable for anything serious.)
However, since that time, both providers have massively reduced usage allowances (and at least one of them has gotten sued for it, lol).
I'm not currently subscribed to either but I'm weighing my options. With GPT being slightly better than Opus, and it used to have way higher limits, I'm leaning in the direction of an OpenAI sub. But I'm wondering if the current state matches my memory from 2-3 months ago. (Since both companies appear to be cost-cutting hard!)
Prefer responses from people who use both, but anecdotes welcome :)
Thanks!
I've been running some tests on a harness we're building, and suddenly saw a jump in a few points yesterday. I reran the vanilla codex benchmark and saw an ~88% score on Terminal Bench 2.1 from GPT-5.5 on vanilla Codex.
The biggest indicator, beyond the score, was that 3 tests which frequently hit "safety" blockers with 5.5 started succeeding last night without warning.
At their request, we are starting with a limited preview for a small group of trusted partners whose participation has been shared with the government, before releasing more broadly.
This comment is an excellent example why the average llm user is basically a slot machine user who thinks "this one is hot, this one is lucky, this one is better than the others" and constantly switching between models on a whim of some occulted understanding that only they posses.
Also, who cares about some 80% benchmark.. They train on these public benchmarks in order to impress people like yourself that subscribe meaning to them. How come they only get 4% pass on $20-30/hr Upwork tasks? It seems to me like these benchmarks are basically useless... There's a thing called variance, I'm not sure why a higher scores on a few tests would lead you to believe you have access to a model that they say you don't have access too..
To me that means “it’s an inferior product but marketing dictates we try and hide that.”
And “our most robust safety stack to date. We strengthened protections for higher-risk activity, sensitive cyber requests, and repeated misuse, and spent multiple weeks finding weaknesses, pressure-testing our system, and hardening it against real-world attacks” is of zero value to me at best, and most likely to my detriment (increasing refusals or nerfing utility). Why do providers keep leading with that? Are there customers (besides support ChatGPT chatbot users, maybe??) that ask for this?
> To me that means “it’s an inferior product but marketing dictates we try and hide that.”
I interpret this to mean you're about to get today's mainline performance at a fraction of the price.
I'm curious about how does this work? Do the subagents also get to use the same tools? Will the client be flooded with tool calls? Why extra pricing for a new "model" when the same thing can happen in the client with more controls?
And if it's an army of subagents, why do they compare it to Fable and Mythos? Those models with similar harness would probably bench better I'm guessing
It's essentially a bunch of subagents being called by a deterministic script written by the main model thread, each eating tokens for lunch and output of which is synthesized by an orchestrator agent.
It's for sure a codex harness feature.
EDIT: yeah, it's the same thing. https://github.com/openai/codex/blob/main/codex-rs/core/test...
OpenAI flat out copying Anthropic is a pretty funny development. It's strong evidence that they've been in catch-up mode.
OpenAI is just way more careful with what features they add or enable by default in their harness. Anthropic's harness is a junk drawer of random features, with a new feature added every few hours. It feels like they're in panic mode, dropping random things to see what sticks when models are eventually commoditized.
I prefer OpenAI way - slow and steady.
Maybe it's a tune of the base model that works especially well with the subagent loop?
This seems like it would be the largest and first closed-source model Cerebras has offered till date
> "Yeah, we've got the absolute best model out there. Trust us. Truly scary."
> "O-ok? May I see it?"
> "Gtfo. Here's a worse version of it for you plebs."
> "Um, thanks?"
> "Lmao, actually no. The current admin fell for our scare marketing. Here, have this even worse crazy expensive token burner that gets more hardware limited every week."
You can say what you want about OpenAI, but their corporate strategy feels so much more solid.
(To be clear: I do not like this new paradigm)
This is really exciting. I work on voice AI, and we're still using 4.1/4.1 mini since none of the frontier models come close on latency. I'm excited to be able to have more interactive experiences, I think it'll unlock new ways of working with these models.
OpenAI's plot design has been consistently awful and inaccessible, it seems like they're optimizing for something other than readability because I find it hard to believe they aren't putting in any effort for such major announcements. If the colors have to be awful they should at least differentiate with marker shapes or line dashes.
At least it isn't as bad as the stacked bar chart where the 50-something bar was higher than the 60-something bar.
Who knows what they will fix, block or change in the model between the preview and GA time. Open models can't arrive soon enough.
Agent Arena (Dynamic ranking of models on how well they orchestrate tools for real-world agentic tasks, based on signals like tool reliability, task completion, and steerability.)
Top 10, Highest rank to lowest
Claude Fable 5 (High), Claude Opus 4.8 (Thinking), GPT 5.5 (xHigh), Claude Opus 4.7 (Thinking), GPT 5.5 (High), Claude Opus 4.7, Claude Opus 4.6, GPT 5.5, GPT 5.4 (High), GLM 5.2 (Max)
Text Arena View overall rankings across various AI models in text-to-text tasks across math, coding, creative writing, and other open-ended domains.
Top 10, Highest rank to lowest
claude-fable-5, claude-opus-4-6-thinking, claude-opus-4-7-thinking, claude-opus-4-6, claude-opus-4-7, muse-spark, gemini-3.1-pro-preview, gemini-3-pro, claude-opus-4-8-thinking, gpt-5.5-high
https://labs.scale.com/leaderboard/rli
Its clear to me these models are useless on any real world task, a 4% pass rate on $20-30/hr Upwork tasks. This whole trend of agentic engineering is a giant money grab.
There have been many leaps forward in the past - tool calling, reasoning, agentic loops etc. 5.6 doesn’t have any of this. More intelligence doesn’t necessarily warrant a major version bump.
If this is the new norm, we as workers should all start look for jobs in those companies.
But GPT-5.5 is as useful an LLM can be; it has solved lemmas I've thought about for a year, it can implement typed STLCs in Rust when I give it a formal grammar, it can help me analyze Postgres planner dumps.
It's great at tasks that have short solutions but
- they cannot learn based on a project
- their long term planning capabilities are worse than worms
- they are unconfident in decision making
- their internal representations are disgusting compared to JEPA
- they don't have any "system clock" like humans and computers do
- LLM architecture is not modular like computer architecture or human brain architecture
There's so many issues with LLMs. I wish that companies can start working on the next generation of architectures before the bubble pops
You say this based on a theoretical understanding or did you inspect them?
JEPA gives you interpretability for free.
I have not personally inspected them and my view is maybe a more exaggerated/dramatic claim of those working in the JEPA sphere
I think you meant 5.5.
I agree it is probably the same size model. It's probably exactly built on top of 5.5, just with more training, or else they would have bumped the version number to 6.
If it was the next generation, why isn't it a major version change..?
Calling it 5.6 creates the least possible expectations, and therefore more potential for positive feedback.
The Sol/Terra/Luna naming is interesting. I wonder what Anthropic are considering for their next models? "Terminator", "Armageddon"?
Even Apple adopted and standardized on it for their latest platform releases.
What is the consensus on who becomes part of the said small group of trusted partners and if they weren't so opaque about it. I'd expect comparatively big names like Simon to be included within such but Alas its not reality.
I also don't like writing about preview models that I'm not 100% sure are the same as the general release model, because I don't want to review something which turns out not to be the model everyone else gets to use.
I specifically said that he is not an ML engineer (emphasis on ML), so I'm not sure what Python web frameworks have to do with anything.
> Also 'low-effort??' his posts are extremely in-depth, clearly very thought through with a significant amount of time and energy
And yes, low effort. Pelican was low effort, his Fable test was low effort, his HN filter etc. Read the discussion in the comments under the Fable test, it's not just my opinion. There was also another example a few months ago. You can search for it, I don't keep track of these things.
I discussed this with him directly after he called himself an "ML expert" in comments.
This is a classic case of the Gell Mann amnesia effect. I read ML papers and work with ML, but to people outside the industry, his writing can look "extremely in-depth" even though it really isn't. People I work with have the same opinion.
> clearly very thought through with a significant amount of time and energy. Additionally he does perform multifaceted checks across LLMs in many of his other blog posts.
I have never seen an article by him about any model that I would describe that way.
And the most revealing sign that he is not an expert is the type of questions he asks and the mistakes he sometimes makes in the comments here. They show why he is not capable of doing any technically in depth evaluation (at least with his current knowledge level).
If you actually want to learn something as a layperson, read articles written by ML PhDs like Sebastian Raschka or watch Stephen from Welch Labs etc. that are directed at general audience.
I'm not saying that simplifying complex topics is low-effort, good simplification can obviously require a lot of work and I fully agree here.
What I meant is more that some of these tests feel methodologically sloppy, they are too shallow, miss important technical context, do not control for enough variables etc, yet the conclusions are sometimes presented lets just say... too strongly, as I don't want to be too harsh.
I hope this means then fable will also get released again.
and dario's you naughty boy who you dont agree with politically.
Let 5.6 free, keep fable chained and anthropic instantly sees rev loss and has to cave.
The clowns in the US administration can barely remain coherent from one sentence to the next.
Having them be the gatekeepers of technological progress in 2026 is fucking lame.
[0]: GrandpaCAD.com
(I work at OpenAI.)
FFS. I hate this world so much. I wish I could just flip a switch and never have to hear about or have anything to do with AI ever again.
Do you ever stop to think about the horrific dystopia you and your acolytes are creating?
I'm looking at you Codex.
Doesn't that undermine all good-faith discourse on cybersecurity safeguards, controlled usage etc? Or is that overstating the case (I'm not a security researcher myself so kinda parroting).
we expect substantial benefit for legitimate defensive work, while meaningfully constraining prohibited offensive use.
Also just making the model better at code is just making it better to writing offensive code.
If what you need is only possible with the more capable model then the "affordability" of the less capable model is sort of irrelevant. If what you need is a novel mathematical proof, it doesn't matter that a high school student is "more affodable". You need the math PhD.
As "old" models get more and more capable, it's going to be an increasingly important skill to be able to adequately recognize when a task requires a frontier model and when it doesn't, so that the less capable (and therefore cheaper) model can be used.
Mythos/Fable is supposedly next generation in size vs Opus, and is rumored to have some architectural innovation in terms of dynamic routing/compute, possibly only fully enabled with Fable which at $10/50 is still twice the price of Sol 5.6's $5/30, but a big reduction from Mythos preview which had been an astronomical $30/150 possibly due to the dynamic routing not yet having been enabled.
Is it just me, or does it seem like Anthropic has been more of a pioneer the past few years, and OpenAI tries to copy features they like?
In many companies, it's IT who will have major input into which company they sign up with as non-technical leaders need guidance, and by making IT fan boys of Claude Code, the enterprise contracts followed.
I mean, you can read them even without the colors, but who on earth thought that those are a good set of colors? Oh, I forgot it was probably someone on 'Sol'.
I'm not colorblind and I was depending on the textual context implying Sol was better than Terra. I had to zoom in quite far to actually differentiate between the colors.
If they insist on terrible colors would it be so hard to differentiate by marker shape or line dashing too?
1. Naming convention is copied from Anthropic and honestly is more catchy than a number (amongst normal people)
2. How in the world did Anthropic have to do all the theatrics about Mythos just to have OpenAI release an equivalent or stronger model a month later without any drama???
3. Cheaper models are just don’t fit any usecase imo and OpenAI knows it so they keep increasing the floor - I’m still convinced task per capability is reduced with each release
4. How in the world would open source models keep up with the multi layer security? Either this security is all theater or we will finally see a ceiling in open source models because by definition they can’t have those protections
5. Cybersecurity things are boring to me because it’s all zero sum cat and mouse games
Anyone know the latest around Fable being re-released after gov smackdown?
I mean, if they deem Fable 5 to powerful to share with the rest of the world, what's left for us?
Sol Ultra ≈ Pro
Sol ≈ Standard
Terra ≈ Mini
Luna ≈ Nano
> GPT‑5.6 is priced per 1M tokens across three model sizes:
> Sol is $5 input / $30 output;
> Terra is $2.50 input / $15 output
> Luna is $1 input / $6 output.
The OpenAI casino has never been more ready to take your money on gambling even more tokens.
> 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.
You can do it easily if you use in fast mode.
I bet you could hit the limits of the $200/month using fast mode if you were using multiple sessions at the same time all day on fast mode.
The OpenAI tiers seem pretty well tuned.
I used to use the plus ($20/month), and that was good for a few sessions every once in a while.
But now that I'm using it to configure my network, monitoring, maintenance, I'm using it every day and I'm on the $100 plan. And I do pretty consistently hit the limits, but it's easy to pace myself.
I'mam thinking about upgrading to $200/month though. It would be nice not to have to ration it.
Edit: yeah. https://claude.ai/share/06fefe02-4299-44da-8c5a-42607f54ca77
Not them joining Anthropic with this bullshit. *
Caching infrastructure is already a leaky abstraction over a feature that is not as reliable or debuggable to the end user as it should be, charging for the 'privilege' of interacting with it is really annoying.
(* for reference on 'this bullshit': ChatGPT previously didn't require anything special for a basic level of caching. Unless you wanted extended cache times, it'd just "do the right thing" and try to use nodes that had your prefix already cached in memory)
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.
Every conversation you have with these "more capable" models will be monitored and joined up and then your entire account might one day be tagged as Distiller or Cyber Threat Actor or whatnot. When combined with identity verification (which isn't discussed in this press release), expect people to be falsely flagged and banned from ever using OpenAI models again.
Wish I could find the thread from last week where discussions of exactly this kind of thing were dismissed as daft and outlandish.
That would be the best case scenario. More realistically a few wrong prompts is going to get you on a government list, and if you’re an immigrant some dark cell.
https://pbs.twimg.com/media/HLwuJLvbwAAOfQZ?format=jpg&name=...
"Please don't post shallow dismissals, especially of other people's work. A good critical comment teaches us something."
Beam me up Scotty. No intelligent life forms on this planet.
Heck there's Fart coin, Harambe coin, Dog Wif Hat coin, you name it coin...
* House design plans from prompts
* Government surveillance of public communication
* Extracting world/spatial concepts from language models (do we really need a world/spatial models now?)
* Driverless City planning startups
* Election vote rigging/harvesting startups
* Video game NPC backstory startups (all NPCs in GTA 6 go to work, go home, shower, go to sleep now?)
I 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.