Hy4 preview

> [...] Let's maybe add a helmet? It could improve riding theme, but may obscure head. Maybe a small cycling cap or helmet? The user didn't ask; can add red helmet? Might be cute. But pelican with big beak; a helmet might obscure. Better maybe no.

> Maybe add sunglasses? no.

> Maybe add water? no.

https://tools.simonwillison.net/markdown-svg-renderer#url=ht...

> Notably, Hy4 preview also contributed to its own development process, participating for the first time in the automated optimization of training methods, data strategies, evaluation frameworks, and low-level operators. The model proposed approaches, ran experiments, and iterated based on the results, with the resulting code, logs, and feedback feeding into subsequent rounds of exploration. This established an early-stage recursive self-improvement loop.

This reminds me of one of the predictions from https://ai-2027.com/ . Only that there it's "OpenBrain" doing this, not the Chinese. And the authors of that paper were also slightly wrong about "Mid 2026: China Wakes Up": China woke up already a while ago. And:

> But China is falling behind on AI algorithms due to their weaker models. The Chinese intelligence agencies—among the best in the world—double down on their plans to steal OpenBrain’s weights.

No need to steal anything, they have already caught up.

And then there's this prediction for February 2027:

> Officials are most interested in its cyberwarfare capabilities: Agent-2 is “only” a little worse than the best human hackers

I think we're past that point now, too…

Hy4 apparently has ludicrous traction on OpenRouter already (https://openrouter.ai/tencent/hy4-preview), with trillions of tokens processed in a couple days: more than GLM 5.3 in a week. That said, it's relatively cheap with a 5% cache cost when everyone is still doing 10%/20% cache costs, so Hy4 may be more compelling.

Genuine Q about word optimization/token density:

If we create a stripped-down vocabulary with greater token density to use less resources and to resolve ambiguities earlier in the semantic process, aren't we creating NEWSPEAK and dragging along the worst aspects of it? The ambiguity and multi-valence of words is what creates more connections between words, increases the directionality of associations, and expands the potential subtlety and depth of meaning. By paring down (or requiring verifiability) we make it harder to say certain things, or at least make it harder to unintentionally say something that makes MORE or DEEPER sense than what we intended. If the token density becomes extreme, you're left with something like a calculator.

Maybe this is the ultimate path toward better coding? But the worse path toward better genuine thinking?

I wish model providers would stop committing chart crimes in their releases.

- if you're gonna order the rest of the bar chart by rank, order your model accordingly.

- if you're gonna highlight a winner in a table of benchmarks, don't highlight your entire model row in the table.

Etc etc

I experimented with Hy3 for a project and was surprised with how good it was. I don't know if it's good for coding, but as a general purpose agentic model, it was only beaten by deepseek4-flash in our tests. It was so close to deepseek behaviour I kept thinking it must have been forked from it.

Is anyone here working on a problem for which current generation LLMs are inadequate, but that could possibly be solved by the next release of a first tier LLM?

Or is it like bicycles? Unless your problem is named Tadej, you don't need a $13,000 bike.

I'm liking where LLMs are headed:

They can do the difficult small level optimization, the boring but tedious code but cannot be tasteful.

That means I'm more valuable and more productive. Good stuff

Maybe's its a problem with the hosting at novita.ai but I didn't got much useful out of this model as a coding agent.

I tried benchmarking it, but it keeps timing out/rate limiting, so the current provider(s) are unusable.

I am going to bring up graph issue for everyone of these announcements.

They all suck.

They shoulda put their stick where they belong, not at far left.

It just makes comparison to Deepseek 90% of them time as Hy4 has nothing to show off.

Unfortunately codebuddy login is not working for me in the United States of America

I used Hy3 quite a bit for the type of tasks it was suited for. Excited about this. My one concern over Hy3 was speed. In theory, it could be served much faster as a smaller model but it was relatively slow everywhere I could get it (including from Tencent directly) but also several other inference providers.

Open Weights is where the action is at in the past couple months, I’d have to think the US frontier labs are getting nervous. Like Anthropic hasn’t released anything pushing the frontier since “the event” earlier this summer.

> open-sources

link to source code?

is it just me or are the bar charts in the blog post strange? Higher numbers don't seem to correspond correctly to their actual height?

I, for one, welcome our new Chinese overlords.

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Congratulations, it turned out great!

I was briefly impressed that https://hylang.org/ had released a 4.0 version!

> Tencent has released and open-sourced Tencent Hy4 preview, a next-generation large language model with 770B total parameters and 49B active parameters, and a context window exceeding 1M tokens.

There are no open source models, at least not useful ones (yet) [0]. Open weight is not the same as open source. The current "open weight" models are just opaque binary blobs you can run on your own computer instead of through a web API.

[0] https://allenai.org/

Imagine thinking that running a Photoshop binary on your own computer instead of through a SaaS web app means that it's "open source". Of course you think that's ridiculous.