Tencent Hy4

    > we are open-sourcing two foundation models co-developed by Hy4 and Hyra 2.0: HyCell for single-cell representation and HyPDE for physical field modeling.

That's how you get a civilizational advantage. By allowing people to just do things. It's so bizarre to watch China embrace the wonders of science and technology, while certain American startups and communities repeat "WHAT ABOUT THE BIOWEAPONS?"

The American century has been fumbled hard.

> For game development, we partnered with professional developers at Tencent Interactive Entertainment Group (IEG) to curate high-quality training data and build evaluations. We focused on feature implementation, gameplay logic, state tracking, and asset pipelines. We also extensively adapted Hy4 for major engines such as Unreal Engine, Unity, and Godot, and expanded its capabilities to multiplayer game development. Compared with Hy4 preview, Hy4 improves by 19.1% on GameDev and 9.8% on GameLogic.

Here's more about that game development studio - https://en.wikipedia.org/wiki/Tencent_Games - they've been around since 2004.

I like that they acknowledge that. For working with ai as a tool open models are already great. For millennium problems and doomermaxxing they are not there yet.

Verrrry thorough in its work and therefore verrrrry slow. It spent a long time with each question, running about 26 queries for each question, and in some questions it ran 64 queries. One of the queries that it wrote also took about an hour to run, which affected its overall exam time.

Last year the open weight models were the cheap & good alternatives. Then in September/October that cost story got rewritten by the GPT-6 series and Claude's 5 series.

Hy4 brings open models back on the cost/accuracy frontier, but its behavior is too thorough + slow.

Another thing we're seeing is that post training open models is starting to see results. Particle-1 from last week is also on our frontier, which is post-trained on DeepSeek V4.1 and Fireworks Ember-1 from a few weeks ago was post-trained on Kimi K3.

Take these numbers with a grain of salt: this exam is just for short horizon, low-context data analysis tasks, not full-fledged software projects.

If the gap closes, we do zero austerity, and interest rates are high, I cannot see a way out of a major economic crisis, even if we do start to see dramatic productivity wins.

More on-topic (and maybe more positive?), but this model feels a bit more expensive than I would have expected!

Personally, the thing I'm most excited about is the FrontierCode 2.0 score, which is a benchmark I trust a bit more - 31 seems to be roughly on par with 6.1 Sol and Opus 5.5 medium

   Model          | Cached Input | Input (Miss) | Output
   ---------------|--------------|--------------|----------
   Tencent Hy4    | $0.042       | $0.83        | $2.50
   DeepSeek (peak)| $0.006       | $0.30        | $1.20
   DeepSeek (off) | $0.003       | $0.15        | $0.60

Le cordonnier mal chaussé.

Tencent Hy4 ties DeepSeek V4.1 Flash at 82/100, in 23.2 min for $0.30 vs 20.1 min for $2.26. Same game quality for about a seventh of the price. Cheap models keep closing the gap.

Model: https://jonclegg.github.io/pacman-bakeoff/#tencent-hy4

Closest: https://jonclegg.github.io/pacman-bakeoff/#deepseek-v4.1-fla...