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they're just late for work.

yeah. look at these 4 feathers (?) on his bum too.

a lot of dumplings

>Generate an SVG of a pelican riding a bicycle

at the top of the linked pages.

This is an excellent comment. Thanks for this - I've only ever thought about whether the frame is the right shape, I never thought about how different illustrations might map to different bicycle categories.

The Pedersen looks like someone failed the "draw a bicycle" test and decided to adjust the universe.

It's just Simon Willison (the person you are replying to) who always makes a pelican, as his personal flippant benchmark. It's not that deep.

No benchmark will be perfect, especially if it's public but it's a fun experiment to visually see how these models get better and better.

Why is it so wrong?

Thanks for the "scientific air" remark, that gave me a genuine LOL.

Which model's best depends on how you use it. There's a huge difference in behaviour between Claude and GPT and other models which makes some poor substitutes for others in certain use cases. I think the GPT models are a bad substitute for Claude ones for tasks such as pair-programming (where you want to see the CoT and have immediate responses) and writing code that you actually want to read and edit yourself, as opposed to just letting GPT run in the background to produce working code that you won't inspect. Yes, GPT 5.4 is cheap and brilliant but very black-box and often very slow IME. GPT-5.4 still seems to behave the same as 5.1, which includes problems like: doesn't show useful thoughts, can think for half an hour, says "Preparing the patch now" then thinks for another 20 min, gives no impression of what it's doing, reads microscopic parts of source files and misses context, will do anything to pass the tests including patching libraries...

Agree with your assessment, I think after models reached around Opus 4.5 level, its been almost indistinguishable for most tasks. Intelligence has been commoditized, what's important now is the workflows, prompting, and context management. And that is unique to each model.

What do you run these on? I've gotten comfortable with Claude but if folks are getting Opus performance for cheaper I'll switch.

This resonates with me a lot.

I do some stuff with gemini flash and Aider, but mostly because I want to avoid locking myself into a walled garden of models, UIs and company

[dead]

The thing is, it doesnt need to beat 4.7. it just needs to do somewhat well against it.

This is free... as in you can download it, run it on your systems and finetune it to be the way you want it to be.

It is more than good enough and has effectively caught up with Opus 4.6 and GPT 5.4 according to the benchmarks.

It's about 2 months behind GPT 5.5 and Opus 4.7.

As long as it is cheap to run for the hosting providers and it is frontier level, it is a very competitive model and impressive against the others. I give it 2 years maximum for consumer hardware to run models that are 500B - 800B quantized on their machines.

It should be obvious now why Anthropic really doesn't want you to run local models on your machine.

Anthropic fans would claim God itself is behind Opus by 3-6 months and then willingly be abused by Boris and one of his gaslighting tweets.

LMAO

Their Chinese announcement says that, based on internal employee testing, it is not as good as Opus 4.6 Thinking, but is slightly better than Opus 4.6 without Thinking enabled.

FWIW it's also like 10x cheaper.

There appears a flight of dragons without heads. Good fortune.

That's literally what the I Ching calls "good fortune."

Competition, when no single dragon monopolizes the sky, brings fortune for all.

Try interacting with it through the website, it will give an error and some explanation on the issue. I had to relax my guardrail settings.

Its on OR - but currently not available on their anthropic endpoint. OR if you read this, pls enable it there! I am using kimi-2.6 with Claude Code, works well, but Deepseek V4 gives an error:

`https://openrouter.ai/api/messages with model=deepseek/deepseek-v4-pro, OR returns an error because their Anthropic-compat translator doesn't cover V4 yet. The Claude CLI dutifully surfaces that error as "model...does not exist"

doesn't it get tiring after a while? using the same (perceived) gotcha, over and over again, for three years now?

no one is ever going to release their training data because it's full of copyrighted stuff. everyone, even the hecking-wholesome safety-first Anthropic uses copyrighted data without permission to train their models. there you go.

Please don't slander the most open AI company in the world. Even more open than some non-profit labs from universities. DeepSeek is famous for publishing everything. They might take a bit to publish source code but it's almost always there. And their papers are extremely pro-social to help the broader open AI community. This is why they struggle getting funded because investors hate openness. And in China they struggle against the political and hiring power of the big tech companies.

Just this week they published a serious foundational library for LLMs https://github.com/deepseek-ai/TileKernels

Others worth mentioning:

https://github.com/deepseek-ai/DeepGEMM a competitive foundational library

https://github.com/deepseek-ai/Engram

https://github.com/deepseek-ai/DeepSeek-V3

https://github.com/deepseek-ai/DeepSeek-R1

https://github.com/deepseek-ai/DeepSeek-OCR-2

They have 33 repos and counting: https://github.com/orgs/deepseek-ai/repositories?type=all

And DeepSeek often has very cool new approaches to AI copied by the rest. Many others copied their tech. And some of those have 10x or 100x the GPU training budget and that's their moat to stay competitive.

The models from Chinese Big Tech and some of the small ones are open weights only. (and allegedly benchmaxxed) (see https://xcancel.com/N8Programs/status/2044408755790508113). Not the same.

I didn't express this well but my interest isn't "who is in the top spot", and is more _why and _how various labs get the results they do. This is also magnified by the fact that I'm not only interested in hosted providers of inference but local models as well. What's your take on the best model to run for coding on 24GB of VRAM locally after the last few weeks of releases? Which harness do you prefer? What quants do you think are best? To use your sports metaphor it's more than following the national leagues but also following college and even high school leagues as well. And the real interest isn't even who's doing well but WHY, at each level.

It is funny seeing people ping pong between Anthropic and ChatGPT, with similar rhetoric in both directions.

At this point I would just pick the one who's "ethics" and user experience you prefer. The difference in performance between these releases has had no impact on the meaningful work one can do with them, unless perhaps they are on the fringes in some domain.

Personally I am trying out the open models cloud hosted, since I am not interested in being rug pulled by the big two providers. They have come a long way, and for all the work I actually trust to an LLM they seem to be sufficient.

there's no pinging. Someone's gotta email dang

Flash is less than 160 GB. No need to quantize to fit in 2x 96 GB. Not sure how much context fits in 30 GB, but it should be a good amount.

Very carefully

So, dual RTX PRO 6000

I've been calling that the "streaming experts" trick, the key idea is to take advantage of Mixture of Expert models where only a subset of the weights are used for each round of calculations, then load those weights from SSD into RAM for each round.

As I understand it if DeepSeek v4 Pro is a 1.6T, 49B active that means you'd need just 49B in memory, so ~100GB at 16 bit or ~50GB at 8bit quantized.

v4 Flash is 284B, 13B active so might even fit in <32GB.

Do you have the links for those? Very interested

More like he wants to ban accelerator chip sales to China, which may be about “national security” or self preservation against a different model for AI development which also happens to be an existential threat to Anthropic. Maybe those alternatives are actually one and the same to him.

I suspect you may have replied to a bot. Dead internet theory

OpenCode? Pi?

If one finds it difficult to set up OpenCode to use whatever providers they want, I won't call them 'dev'.

The only real friction (if the model is actually as good as SOTA) is to convince your employer to pay for it. But again if it really provides the same value at a fraction of the cost, it'll eventually cease to be an issue.

They have instructions right on their page on how to use claude code with it.