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Took me a while to find what you were referring to by gram. Arxiv paper from 9 days ago that's not properly indexed by search engines.

(G)enerative (R)ecursive re(A)soning (M)odels. They really wanted the acronym.

https://arxiv.org/html/2605.19376v1

> Google, OpenAI, Anthropic could train a 30B GRAM-based model in days - and it could potentially have better local reasoning than the best model available today at >1T param

I agree but with their urgent IPO-driven need to keep increasing prices, the frontier vendors now have every incentive maintain the perception that frontier performance requires endless >$200K racks of unobtanium GPUs and RAM. While they'd love to reduce their actual costs, they'd only want to do it to the extent they are certain they can keep it secret. Otherwise, they can't maintain and keep increasing their prices. And post-IPO audited reporting makes keeping that secret even harder.

Game theory-wise they probably don't want their their armies of leading researchers optimizing frontier performance, at least in any way that would further accelerate the relative price/perf of smaller models or self/cloud-hosting. While they know the open source models will always improve, the still win as long as enough customers demand the latest frontier and the open source lag remains constant.

They profit most in a world where a few frontier labs stay far in front, drag-racing each other and expending vast capital. It keeps their customers reliant and paying top dollar while keeping low-cost alternatives farther back. They probably much prefer competing with a couple other frontier labs who have similar astronomical costs and biz models, than a world where self or cloud-hosted open-source models start closing the gap enough to start commoditizing their business.

>It is almost guaranteed that a 60-90B model can outperform current SOTA in coding tasks within 2-3 years.

I don't disagree, but how much of this ends up being distillation? I can't help but imagine that 4.8 was probably trained in part by leveraging Mythos.

If the very large models turn out to be very expensive to run relative to the benefits, it's possible that they could end up still being trained, but ultimately used as a tool to create smaller models that are nearly as effective.

I'm curious if someone here with a stronger background in the space has a similar intuition or not.

I looked into this "GRAM" stuff a sibling comment links further to, and just to say:

- this gets reinvented/rediscovered constantly under different names

- it cant be trained very well (right now, will change)

- massive theoretical improvements over current models (log_2(vocabsize)=17, residual stream dim is thousands of dimensions, recursivity means more information bandwidth by ~3 OoM)

- BUT it cant be interpreted or aligned <- this is why no one uses it and no one talks about it. the idea is 100% obvious to all the frontier labs and there is a good reason why it isn't used

I follow this stuff closely, I think I know what I'm talking about (edited for formating)

I second this idea: LLMs will plateau. They are already pretty good. Plus, scientists struggle to actually score their performance accurately (esp. when it comes to reasoning).

With that said, they are now hitting the walls of energy costs and memory shortages. You brain uses 20W -- don't take it as an insult. There are orders of magnitude to gain from producing energy-efficient models (or model runners).

So I am expecting same performance at lower costs for the coming years.

There is endless returns to frontier intelligence, just because most people can't make use of it doesn't mean someone can't make a ton of money off of it.

Most software engineers will just need cheap tokens.

But things like physics and drug discovery have no foreseeable upper bound.

Absolutely that’s why they’re rushing to IPO now to squeeze the last drop of the bubble they know this is a dead end.

GRAM is another one of those "stupid specific architectures" - same as HRMs, etc. It can sort of contest LLMs at specific puzzles. It demonstrated that much. It's not a general contender with LLMs at LLM tasks.

If you subscribe to things like "there are tasks LLMs are innately bad at due to insufficient depth and lack of recurrent capability", then GRAM might be another signal towards that.

But keep in mind: even ARC-AGIs have their frontiers dominated by LLMs. Even if "innately bad" is true, it clearly doesn't go all the way to "innately incapable".

By pointing out the exact things that will likely happen you are oddly enough hedging against (at least some of them) happening!

A) I reckon it's true that smaller models will continue to improve massively through optimization and better and better harnesses, this tech is all still very young and A LOT of resources and (good-)will is being thrown at it.

B) The 1T+ models will be able to sideload and improve upon a lot of the fundamental improvements that happen to the smaller models to speed up incredibly while getting better at tools while (on a gradient) getting -more- things right.

C) More of an observation that I think is worth keeping in mind clearly; Karl Popper's black swan and all, truth in our temporal world IS a gradient!

Small models don't have enough parameters to memorize the entire internet. For very common prompts you don't notice that, but when you rely on some niche knowledge that might only appear once in the entire web, a single blogpost, a single github issue, a single pdf, you need to be lucky enough that the agent runs a web search AND it returns what you need.

Even as humans there's so much knowledge out there that exists but it's very hard to surface unless you know exactly what you're looking for beforehand.

I think you are assuming training from scratch, which I doubt is happening here. Fine-tuning and RL, especially based on synthetic feedback (coding skill, in particular) can be ongoing and is where these models obtain truly useful abilities.

"It is almost guaranteed that a 60-90B model can outperform current SOTA in coding tasks within 2-3 years"

What insight do you have to make this claim?

It is fascinating to me to see a new product category that improves so vastly year-after-year, where people commonly state that this is now the peak already.

I couldn’t even imagine having to go back to a model from 12 months ago, much less 24 months ago. GPT-5.5 is so much better than GPT-4o that it sure seems like they keep finding new juice to squeeze.

This is like going from dialup internet to DSL and acting like it has peaked before gigabit cable and fiber come along. We are at the beginning of hardware truly made for AI.

The GRAM model is so much into my research direction, I love it. Thank you for posting it.

Where do I find papers like this? Outside of hacker news comments. It's so hard to find the good stuff in all the noise IMO.

> It is almost guaranteed that a 60-90B model can outperform current SOTA in coding tasks

The benchmarks need to change. The current coding benchmarks don't capture the realities of software engineering.

I had a bunch of images that got masked by some logic, I had to evaluate something on the original images, Claude 4.7 decided to inpaint the masked images instead of just fetching the actual unmasked images from upstream.

I had another model once that decided that because it couldn't figure out how to fill out a form to log into HuggingFace to download weights for some open source model that it was going to instantiate the model with random weights and run inference on a thousand images.

Its coding was fine, but the solution was not the right one.

Smaller models can already outperform SOTA and massive models on specific tasks / domains.

> It is almost guaranteed that a 60-90B model can outperform current SOTA in coding tasks within 2-3 years

Given how well Qwen3.6-27B performs for such a small model I think you could be right. I suspect that Google,OpenAI,Anthropic must be looking at the Qwen3.6 models (as well as Deepseek V4-flash, MiMo-V2.5) and wondering if they could make some smaller models that are specifically trained for certain activities - like coding. Smaller, more targeted models would take up a lot less resources.

As far as it has been studied, the relationship between model size and capability is inversely logarithmic: 10x increase in params less than doubles capability.

> I won't be surprised if the next gen frontier models are the last.

the last?!? I'm excited to see :) I'll take the other side of that since llms are so new

There is endless returns to frontier intelligence, just because most people can't make use of it doesn't mean someone can't make a ton of money off of it.

Most software engineers will just need cheap tokens.

But things like physics and drug discovery have no forseeable upper bound.

I effectively distill the frontier models by building whole sets of skills, personas, and other artifacts that I can then run on smaller models and get 10% even 20% improvements on models like haiku or local models.

There's a lot of room for improving the smaller models at many levels of the stack.

surely training also gets cheaper so justifying it becomes easier?

i think it'll be more like we get 1-10T models and then distill those down into smaller models, though

It seems like the best small models today are all distilled from bigger models

Moreover, I hypothesize Claude Opus 4.7 and now 4.8 are a distillation of Claude Mythos

I'm frankly surprised the focus is still on these enormous "know everything in the world" models. I would think you could create an incredibly lean and smart "just React and React Native" model.

Let's hope that hitting a scaling wall and less money to spend will begin redirecting efforts to optimize inference and get the same results with less compute.

Boomer comparison, but I remember the 8 bit computer era when the hardware was what it was so the later games of that era used hardware better than previous ones.

And anyway, with quantum, there will be no need for frontier companies as you might be able to even run a 1T param model on a consumer quantum computer.

you just need to look at Mythos to see the jump in performance from a 10T(?) model. As they scale, they get more capable. We might have an yearly release, but I believe the releases will continue, as long as scaling laws are in tact, and there's huge problems still need solving. (think cancer)

> I won't be surprised if the next gen frontier models are the last.

I’d be surprised tbh. Investors don’t want to hear “everyone else is still training models and seeing improvements, but we don’t want to participate in the arms race anymore.” They want monumental leaps every quarter or two because they have sunk unholy amounts of money into these companies/products.

The whole idea of “hyper scale” doesn’t jive with caution and or otherwise slowing down.

I don't think this is true at all. It might feel like this because we are used to a very very fast release cycle but we are only in this topic for a few years.

We have so many ways of optimizing:

- continusly creating more and better training data

- increasing parameters to 20/50/100TB

- We still wait for Mythos access

- We still wait for Mythos distilation (i haven't heard any rumors or so that there is a distilled version of Mythos out)

- Reinforcment learning and evolutionary algortihm only started to appear

- If a small 30GB Model can do stuff, these models can also be used as teachers for the big ones

- We have not seen yet specialized models at all. Like a coding java german expert model. Why? Even with MoE architecture, you still need to have these layers around

- Research for Diffusion and other models is still in progress

- Nvidia just announced/showed a 7x speedup on inferencing for Nemotron

- Multitoken prediction became available just a few weeks ago

- Compute gets only in a range were they can do a lot more and cheaper experiments (see Google IO 2026 announcement)

- World models are showing great progress and we do not know yet what they will bring to the table

- They are probably not finetuning/fixing all areas in parallel. I would argue that Anthropic focuses most of its efforts into coding and agentic. Google for sure does subagent and agentic optimizations too. Plenty of areas are just not touched i would say because they don't have the capacity

- We see more and more mulit modal models (these also consume compute)

- N-Gram paper and co i have not seen all of these things in chinese open models

- We don't even know yet what Meta is doing, but we do know they restarted their efforts again

- Anthropics models got a lot better benchmark wise for dening non sense asks. They do learn how to get rid or reduce hallucinations

- We are in the middle of the biggest Reinforcement loop whith all the training data we give them day to day and its not clear at all if they already use these models in thir training and at what stage.

- We do expect bigger models to be able to comprehend deeper concepts / broader code bases. Big companies with huge code bases probably are waiting for this

- Thre will be also continues progress in harnesses which in it alone is not part of the LLM progress (fair) but these harnesses do get better when you finetune a model to be optimized for a harness

- ChatGPTs Image model 2.0 got relevant better and came out just a month ago

I suspect, based on hardware requirements and progress on hardware infrastructure alone, that the industry wants to go to 100t models and we do not know yet what this will mean. I could see that we might skip normal transformer and find relevant other architectures.

Just a week ago there was a research paper about parallel input and output streams which has not been explored enough.

There was also a research paper were they showed that a LLM can compute things. This will take time to see were this leads to.

I don't think the focus on GRAM and facts is so relevant. Its about context and context handling not just some facts.

I think the future will be enterprise clients will train their own models based on their needs and data.

> It is almost guaranteed that a 60-90B model can outperform current SOTA in coding tasks within 2-3 years.

I am ready to bet against this. Knowledge benchmark like SimpleQA isn't increasing for small models.

> It is far less clear that a 1.2T model will be meaningfully better enough to justify training it.

Well for one, we know for certain there is Mythos which is meaningfully better. And I think there is a lot of juice left to squeeze for Mythos class model.

So, then I guess the big three are never going to make their money back.

I would be shocked if 5.5 is the last new pre-train from OpenAI. Your comment is nonsense.

| a 60-90B model can outperform current SOTA

My conspiracy theory is that Apple recognizes this.

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For long-running tasks, yes 4.7 has been a noticeable improvement. Goes off the rails alot less than 4.6 does. For shorter-sized windows, I havent felt as much and agree that the harness improvements have been fhe biggest lever

Yes. You and some random indigenous guy in the Amazon likely share the same intelligence but you are more capable because you have access to writing/reading, computer, car etc. Intelligence is more than raw intelligence. It's harness, skills, tools, memory etc. If you improve all the latter but keep the raw intelligence (LLM) fixed, you certainly get better results. Same with us humans.

To me 4.5 was mindblow, 4.6 noticeable, 4.7 more like a style/personality change regarding how much it asks back, how much it assumes, how eager it is to jump to action etc but not really in terms of my perception of its smartness.

In my experience, 4.7 was a noticeable step down from 4.6.

I was one of these people that Claude would never finish anything and just randomly say, this is a good stopping point, I think you should go to bed.

And then I'd tell it to continue, and it would burn tons of tokens, make no progress and say, "This is a really good stopping point..."

Canceled and switched to Codex and have been pretty happy with it. It doesn't plan as well as Claude, but I think it does better implementation - and neither of them can actually come up with good plans without a ton of help...

Codex is also way faster.

They all feel, more or less, the same to me in terms of output capabilities. Mostly get simple things right, can get more complex things right with nudging, eventually get stuck hard on something that takes a bunch of iterations through it/logging/etc or me fixing the code manually.

4.6 felt a bit better than 4.5 but slower. 4.7 doesn't feel better than 4.6.

I actually don't see any personal productivity improvements from using opus over sonnet for coding. If you're keeping tasks small and conversations short, reading the code and correcting before changes go in, whatever advantages opus has aren't practically significant. It's also just talky as hell, overexplains anything it touches and every token produced this way increases the surface area for hallucination so you need to have your guard up even more with it.

There's a sweet spot of complexity for low importance tasks where it's just big enough I don't want to do it and just simple enough to have opus plan/delegate/review with another model. So possibly model improvements will grow this window, but currently I don't do much in there.

I'm actually currently studying this :)

Honestly... not that dramatically. Each release is much more marginal. And quoted official benchmarks doesn't translate very well into the real world.

4.7 regressed hard in some ways. But a compounding factor too is that the claude code harness seems to nerf the model after a few months. Probably to reduce token use.

So far 4.8 seems less verbose but we'll see in practice what it translates into meaningfully.

They just showed the benchmarks it improved on but it regressed on so much more, such as the MCRR benchmark: "On multi-round coreference/context recall tests (often cited as MRCR or long-text retrieval benchmarks), Opus 4.7 reportedly dropped from roughly 78.3% down to 32.2% compared to Opus 4.6."

Same. 4.7 felt like a definite regression

Same here - we never bumped to 4.7 in our agentic app. Continue to use 4.6.

I managed to find that Haiku outperformed Sonnet on some tasks...don't want to blog spam but if anyone is interested: https://www.ruairidh.dev/blog/sonnet-4-6-drops-format-rule-o...

Same. 4.7 has done some incredibly stupid things.

Same. So happy when I found that option.

Yep, until 1st June 4.6 is still x1 on Copilot, but will jump up quite a bit in coat - 4.7 was already highly priced, and the output was frankly terrible.

It still seems trying to build general models is mostly cost prohibitive - the frontier model provider and resellers are repricing in such a way the return on investment is dropping as developers and users become more cautious of burning their limits.

I'm still of the opinion that models like 4.6 don't need to be improved on - rather they need to be better integrated with more domain specific models in agentic flows.

same!

Same here. Went back to 4.5 and was happy I did it. The only frustration was that I can tell the model has declined compared to the first few weeks it was released.

I also recently moved to 4.6 since I started hitting the context limit too often with my current project.

If you are using Claude code, just set effort to xhigh.

This one change will probably solve 80% of the problems you have noticed.

"personality issues" I was able to tell that Opus 4.7 would take instructions more literally, which I appreciated once I calibrated my phrasing to be more precise (often asking to investigate issues, pre-4.7 it'd start making code changes instead of just giving write up). But I can see contexts where handling vague prompts would've just been worse

Looking forward to the results. Thanks for your work.

It didn't do shit

> At what point does my CS degree become totally useless is an open question.

Why are you people saying all these things.

We'll probably see long-distance space travel long before a degree in generic problem identification and solving becomes totally useless.

> "4.6 was such a bad model,"

It's just amusing reading all these posts with different viewpoints, just in this thread there are multiple people saying 4.6 was so much better than 4.7 and that they switched back to 4.6.

I gave 4.6 a miss and only recently switched from 4.5 to 4.7. I found on a particularly different task 4.5 struggled with (getting stuck in loops and trying to convince me the problem had been solved) was quite solvable with 4.7.

I think they overtrained on scientific papers or such as it would spout really sophisticated sounding nonsense with a ton of complicated verbs and adjectives. 4.6 was definitely better in that regard. The more I use these tools the more I think they’re not actually that revolutionary. I mean it’s still amazing what they can do but they have very clear limitations it seems.

I find the quality ebbs and flows even on the same model. My guess it is something to do with GPU availability but only guessing.

i dont understand the nuances here. what does this mean. 4.8 is trained on same model as previous one then? what does brand new mean.

alot of investor money is hinging on models performing better every release.

I wonder if it's being done to improve revenue nunbers without changing an enterprise contract? Oh what's that your token usage went up because some of your developers switched to a new model? That sounds like a you problem.

I thinks there's a big push to get these companies in a state where they can be dumped on public markets.

I think it's telling how split the opinions are around all of this. A lot of people distinctly disliked 4.7.

Are the dividing lines around personality? Working domains? Opinionated software stuff?

Who knows?

most of my coworkers feel the opposite about 4.7 and that 4.6 was, to them, significantly better to point that several stopped using claude code

It didn't change at all, same as 4.6. Good morning to the Anthropic office btw.

5.5 is much better than any Anthropic model. I hate both companies with passion but the Anthropic shills here are in overdrive mode. On top of it, it's cheaper.

Greetings to the Anthropic office good sirs btw.

i dont think theres anything particularly special about new models for that though. thats a harness improvement

Humanizing this technology seems like a step in the wrong direction.

If LLMs have tough me anything, is that the average person is far more gullible than what I could have imagined.

I appreciate the generosity, but you're gonna want to meet me first.

No question at all that a dolphin swims better than a submarine.

Why shouldn’t they be? They are probably the same size and cost the same to run. They are not doing full training runs (eg Mythos) so don’t need to recover insane training costs.

Opus 4.7 and presumably 4.8 are more expensive due to a new tokenizer that translates data into more tokens per input.

Same price on a token basis, but usually steadily decreasing on a task basis

meta threw in the towel when it came to producing AI models since their gains couldn't keep up with China.

Exactly. Go back to Opus 4.5 and see how you like it.

You won't, really.

Or the model could just be shite.

Doesn't look minified, just very dense, almost like progcomp code. First time I've seen an LLM spit out that style of code, I'm impressed!

Yeah looks extremely compact. I didn't instruct it or told it to use as few lines of code or characters or nothing of the sort.

Not sure why it did that. Its own rationale (which is highly suspect, but the only lead I have) is that it defaults to dense style if it has to write a file in a single go. May be a kernel of truth somewhere in there.

I just had Opis 4.8 code up something and actually that's exactly how it coded it!

It looked gross and minimized, the result was awesome but the code looked pretty awful visually

A friend sent me something he vibe coded which included a massive webassembly blob in the HTML file. My friend is not a programmer so he was not able to explain to me how it did that.

"Readability by humans" may no longer be as important as it once was.

Yeah! Host on GitHub pages, so it's easy to click a link and play!

Usually just once (and I did just one test for this particular one), but I've found the overall quality to be relatively consistent.

There's too many confounding variables here, randomness just one of them. So I don't think of it as a definitive test (and reliable ordering), just another data point (along with actual benchmarks, pelicans, etc) to get a sense of the capabilities.

For example, I managed to get something out of DeepSeek 4 Flash quantized to 2-bit with Antirez' DwarfStar, used via Pi. Almost kinda worked! :) Which makes me optimistic for using local models for serious development soon - I'd say within a year.

If you look on the page of games, the style of chatgpt 5.5 is almost identical to the Claude style.

It's a combination of reasoning effort (max) + enabling workflow that orchestrates multiple sub-agents.

After some interrogation, here's how it organized the work:

1. Design workflow (rts-game-design, 11 agents, ~13 min) ran first, produced SPEC.md + DESIGN.md:

1.1. Proposals (3 parallel agents): each designed a complete RTS from a different philosophy

1.2 Judge (1 agent): evaluated all three and synthesized one unified design, committing to specific numbers (costs, HP, map size, etc.).

1.3 Deep-dives (6 parallel agents): each wrote an implementation-ready spec for one subsystem, all consistent with the chosen design

1.4 Synthesis (1 agent): merged the design + all six subsystem specs into one conflict-free master spec

2. Code-review workflow (rts-code-review, 25 agents, ~5 min), ran after the main agent had written and tested the code:

2.1 Review (6 agents, read-only Explore type): each scrutinized one dimension and returned structured findings.

2.2. Verify (19 agents): every finding got its own skeptic agent told to try to refute it, Result: 19 flagged → 16 confirmed, 3 rejected as non-bugs.

What the main agent did in the main loop:

- Wrote all ~2,400 lines of index.html by hand from the spec.

- All browser testing/debugging via headless Chrome (I told it to use rodney by @simonw, love the tool :)

- Applied all 16 fixes from the review and re-verified them in the browser.

it's a brand new mode

Biases the model to solve problems with teams of agents

I'm saving them all as gists here: https://gist.github.com/senko

But I just vibe-coded a handy list of all the tests I did (unfortunately without the commentary I usually leave in social media posts -- I should add those at some point): https://senko.net/vibecode-bench/

There isn't, as I wasn't going for strictness, more like a playful challenge in the vein of Simon's SVG pelican.

Between the two, Opus 4.8 seems more capable. But, I suspect the harness plays a large role here. It's possible the result would be as good if Codex ran 10+ agents and spent an hour on it.

OpenAI and Anthropic usually fast-follow each other, so I wouldn't be surprised if Codex got the same capability in a couple of days (and even an update to the model), then it'll be a better test.

Sooo, let's say, winging it, vibes-based: 85% for Opus 4.8, 75% for GPT 5.5. Compare with GPT 5.3 (let's say 25%) here: https://senko.net/vibecode-bench/2026/rts-codex-5.3.html

It took 50 minutes, would be ~$20 in API costs (I'm on a Pro sub).

Is that for bsky.app (BlueSky platform) or my personal site (senko.net) where I put up the list of tests? What browser/device was that?

Token equivalent of ~ $20 (I'm on a $100 Max sub).

Opus 4.7 does not support disabling adaptive thinking (web, Claude Code). [1] Like the OP, I experienced similar issues and I'm glad that they brought back the ability to disable adaptive thinking in Opus 4.8.

[1] https://code.claude.com/docs/en/model-config#adaptive-reason...

> Opus 4.7 and later always use adaptive reasoning. The fixed thinking budget mode and `CLAUDE_CODE_DISABLE_ADAPTIVE_THINKING` do not apply to them.

Originally Indians didn't drink tea. British East India company got Indians addicted to tea/chai when they sold it for free. Then the real prices came in.

While all these models are nondeterministic a feature bump is still necessary as the same input can have wildly different output on a new model. For API users being able to pin a model is a necessity.

The 4.8 model costs the same as it's 4.7 predecessor.

All the 4.x models are still available, and they all cost the same.

What are some examples?

> 4.8 is also 2x more expensive for a "modest" performance bump. How refreshing.

Where are you seeing it's 2x more expensive? https://platform.claude.com/docs/en/about-claude/pricing

Price hasn’t changes at all, though.

One capability that I see is missing from opus is this ability to understand an entire system. My hope is that a mythos class model will be able to comprehend even something as complicated as an IOT system with a hardware and firmware layer multiple API’s backend and different kinds of API and web clients.

The main limitation we’ve had to agentic coding is an understanding of this system that spans processes running on different machines and architectures.

What sort of clever handholding does Claude code do?

Anthropic's wants to sell us Claude Code with no model selection at all.

Opus seems to be overly eager of late to 'vibe' out entire solutions and build out things that you didn't ask for.

/goals is helping set the narrative that does it really matter if Sonnet and 3 Haiku agents got you to that end state...eventually...if its what you asked for?

For better or worse Opus is already handing off 80% of its work to background agents of Sonnet, Haiku, and likely a quantized Opus.

Want model selection? Pay for the API.

Its amazing how quickly ive just become accustomed to being a max subscriber. I dont think I could go back to pro.

They have already been experimenting with such ideas [1]:

> Claude Code Removed from $20-a-Month "Pro" Subscription for New Users

[1] https://news.ycombinator.com/item?id=47855832

> you will just get a limited and nerfed Claude Code slash command /mythos-security-audit or something.

Unless it's so expensive that we can't realistically use it for anything, I wouldn't complain about getting at least that. I would also rather have the actual model, but that's a useful application of it (and I'm probably not going to afford using it for much more).

Isn't OpenAI's public flagship already beating Mythos on penetration testing? I get the impression Mythos is just valuation-juicing for IPO more than anything else.

The fact that they haven't released it yet suggests a cost/margins issue to me more than anything else. Short term, I'll probably keep using Antrhopic, but my long-term bet is that locally-served models win, if only because the quest for profitability will probably lead to intentionally-nerfed / enshittified frontier models.

At other vendors, ad placement within LLM responses is either coming or already here. Anthropic's handling of OpenClaw shows they're willing to engage in anti-competitive behavior, and the courts are not in a hurry to stop them. Why would I pay them $200 a month for such treatment when a $2K box does what I need locally?

What does an average Joe need a Mythos level model for that Opus can't do for them?

It does sound like an even higher API price tier for sure.

This command would be not so bad for not a billionaire me.

It already has been and this has been widely written about. AI was used to identify and prioritize targets for the US to bomb in Iran.

Here's an article from 2 months ago for example: https://www.theguardian.com/technology/commentisfree/2026/ma...

It was also implicated in the bombing of a girls elementary school which left 168 dead. The US did a "triple tap" to kill any first responders.

https://www.theguardian.com/news/2026/mar/26/ai-got-the-blam...

https://www.theguardian.com/technology/2026/apr/01/dont-blam...

I think it's beyond decent. I don't understand how people are not more impressed by this. Just a few years ago the only expectation would be garbled nonsense.

Haha, yeah. I tried for it to create a SVG with scissors and it was hopelessly overwhelmed. I think at least the SVG design niche will be safe a little while longer

the battlefield sounds much easier. worst case scenario you kill somebody, but that's what you're trying to do anyways.

if you kill somebody while trying to render a pelican on a bicycle it's a real problem.

I think that's a fair tradeoff. There's no way I'm going back to writing code by hand again. No one deserves that.

Could be an extremely high angle stem that just happens to match the downtube angle.

Maybe the pelican is just riding a road bike/gravel bike

As an aside, some of the renders have only a single side connection to the wheel and that is a valid bike design, the Cannondale Lefty front fork only has a left leg:

https://duckduckgo.com/?q=cannondale+lefty&iar=images&t=ffab

On a new model release, you can guarantee two things are in the replies to Simon. One is your link, the other is "surely the models are being trained on this now"

Sure, but no one is trying to force art from most people into about every area in the economy where anyone ever pays for something visual. If you asked professional artists to draw a realistic bicycle, I'm guessing few of them would try to just randomly guess what the mechanical parts looked like

> The most unintelligible drawing has also the most unintelligible handwriting. It was made by a doctor.

Haha

But if you need to draw a bicycle, you wouldn’t pick a random person in the street. You would hire an artist and you’d be guaranteed to have at least a believable one if not a perfect rendering.

No guarantees is why LLM is akin to gambling. Every new context is essentially picking someone out of the crowd.

And yet some people doubt Anthropic's commitment to AI safety

Max seems to me to be notably better than the others.

low: yolo

medium: redesign bike so peli can reach bars

high: redesign bike so peli can rest on frame

xhigh: yolo

max: big peli reach bars

I like the way the max pelican has a stern look on his face

Is the output on the max level meant to be missing?

If these were in the internal evals then the output would be much better. The 4.8 pelicans are pretty meh

Click the link

I don't think the API supports "max" as an option, that might just be a Claude Code harness thing.

UPDATE: My mistake, the API does support max. I added a max one at the bottom of this page (cost 43 cents): https://tools.simonwillison.net/markdown-svg-renderer#url=ht...

I've been meaning to do a "run 3 times and pick the best" version for quite a while, I should really pull the trigger on that one. Currently it's one-shot only.

tried it myself, not much of difference

https://gist.github.com/fendy3002/3026a8c4d67d1301666ec40fc0...

looks like the model already trained well on both bicycle and pelicans

This happened to me in elementary school. We were doing fingerpaintings using plasticine. After all the bikes were hung on the wall, mine was racing the other way... Somehow it really stuck with me.

It's facing left but looking right...

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Good call, it's cute: https://gist.github.com/simonw/68560eddb0b268a8417f80ceb7304... - but nothing like GLM-5.1: shttps://static.simonwillison.net/static/2026/glm-possum-esco...

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The reasoning xhigh one is pretty solid: https://simonwillison.net/2026/Apr/23/gpt-5-5/#and-some-peli...

Same here. I use codex for planning and deepseek v4 flash for implementation. Its worked really well so far.

People be saying these things with certainity. 99% of the time one has just inspired more confidence through sycophancy, or just good varience in outputs for a session/prompt.

Slop heads be swearing by one slot machine one week and swearing it off the next like an addicted gambler describing their favorite slot machines from week to week.

This isn't a coincidence, these companies hire UX designers from mobile gaming and online gambling to help engineer their addictiveness.

Its all in your head, and the output is no matter what always going to be worse than learning how to do something yourself and putting care into it.

Handmade watches > mass manufactured watches. There's nothing special about the skills needed for the guy who runs a conveyer belt at a watch manufacturer in China. The watch made by the guy who makes one watch a month in Switzerland is prized and beloved.

GPT 5.5 and 5.4 are such great models. I just tried opus 4.8 and took 30 minutes to be confronted with a bit laziness that makes me go crazy. 5.5 just doesn’t have this issue.

Also check mine[0], basically random private tests/questions and an ok-ish methodology, testing mostly for general intelligence than coding-specific tasks.

I built it for myself, to test which models to use via OpenRouter for my n8n agents. Currently actually still using gpt-5.3-codex for many things, as its pricing is really good in production (due to how their token caching works).

Gemini models still have the best intelligence (when asked any questions, most likely to get it right), but in production they still have many failure modes[1].

[0]: https://aibenchy.com

[1]: https://news.ycombinator.com/item?id=48230368

Have you seen https://deepswe.datacurve.ai/blog? This is the closest to a vibe check i’ve felt even with the open models.

No way is Muse Spark generally better than offerings from Google and OpenAI. I actually find arena to be amongst the most useless indicators

I'm finding it a little hard to believe that GPT 5.5 is in 11th place for webdev, outranked by models like Kimi, Qwen, and Z.ai. I'm not saying it's not true (I have noticed GPT being less smart in recent weeks), but this is very different from my expectation.

On paper it's one of the best because it's meant to be blind comparison of your own prompts. However if you are someone who geeks hard on one or a few models, you learn their "personality" and can recognize them in a blind test.

If you don't know their methodology, or anything about it why do you think its a good ranker?

Their Cybergym score is reportedly awful because of the cybersecurity nerfing. https://x.com/i/status/2060046843023630841

Gonna assume it's because they barely budged or moved downward and most of their reported benchmark results are probably within sampling errors...

I find this site useful https://artificialanalysis.ai/leaderboards/models

This is my exact vibesperience

> people are going to use the smartest models for coding. They don't care how cheap your tokens are

I actually think that's still true and will continue to be true as long as someone else subsidizes the tokens. Once the "free money" runs out, things will get interesting.

Props for making a falsifiable claim, noticing it was falsified, and owning up to it.

I pretty strongly feel the opposite way. Granted I have not used deepseek enough to “know” their model idiosyncrasies as well as Anthropic, so there is a partial skill issue. But I just find it really hard to justify using a less powerful model while I work.

The most I’ve ever spent in a month extra on API tokens for my own work is $200, and I pay for the $200/mo Claude. I use these models quite a lot, though not idly (I usually just walk around and do other stuff until I know how im going to approach the next set of problems). So it costs me about $3000/year to get as much as I want of the best model available. Already that seems low enough to not be worth stressing out too much about optimizing it, because it feels like an indisputable good value, and trying to save money with a less powerful model would be optimizing for a $1000-$2000 saving at the expense of a large portion of my work taking longer or being more frustrating and iterative.

That’s not a flex or anything, I get that in other countries $3000/yr is a lot of money for a software developer and also a lot of people would perhaps rationally be better off doing X% worse at work or spending Y% more time on tasks to save $Z, if their productivity improvements didn’t translate to more salary. Otherwise if your performance has more upside I really do think that the smartest models are better with the current pricing scheme. Deepseek and the other Chinese models spend a LOT of time thinking, and tend to be much more jagged (benchmaxxed) in performance. How can dealing with that over an entire year be worth $2k?

The only situation I can think of where sacrificing my own time/performance to save on inference is batch compute (of course, $1k vs $100k is different from $30 vs $3k) or work where the tier 2 models have crossed the “good enough” threshold. But I think Opus is not even close to that threshold generally yet. As it gets smarter I, and I think most others probably, just try to do harder things faster and hit the next wall.

> And I was dead wrong. Now I mostly use DeepSeek Pro myself.

I've wasted over a hundred Euros re-doing work that was done badly due to the model not being up to task (Vue with TS + wrapper components around PrimeVue, needing to handle event and property passthrough and deal with the stupid Vue SFC issues, TS made this much worse than JS would be). I think it was the GLM model through Cerebras Code at the time, in addition to some GPT and Gemini models with the API pricing.

That said, DeepSeek V4 Pro is pretty good and I can totally see myself offloading some of the work, as long as a better model reviews the work and provides suggestions/tests for it.

The other thing that's changing is more and more CFOs are looking at the AI spend in engineering departments and hitting the brakes. Token leaderboards were cool when the spend wasn't a double-digit-percent of the entire department's budget including salaries.

you weren't wrong your tasks/problems didn't warrant a frontier model and it was always solvable with a cheap chinese model

doesn't invalidate the rest of us working on tough problems that demand more expensive models and valuable enough to justify it

Your comment is a slice of the reasoning underlying the "AI will take all the jobs" claim. I would constantly see references to what AI could do and how fast it was improving. Never a word about cost. We should anticipate that there will always be demand for human labor, for cheap models, for local models, and probably even frontier models.

Yeah I've also found that models are good enough that the extra spend on premium models isn't always worth it, particularly for my small personal toy projects.

A $20 claude sub goes a long way when you plan with Opus and execute with Sonnet.

I think two things happened:

1. The sheer number of tokens that a coding agent can use flipped the math upside down on this equation. If you use the most expensive model for everything those costs quickly become untenable, even for software companies.

2. We realized many of the coding problems we're solving aren't incredibly difficult.