Z.ai's GLM-5.3 Tops Open-Weights Leaderboard Using Only Post-Training
Z.ai's new 753B parameter model ties Kimi K3 as the top open-weights model, with a 246-point Elo jump in agentic tasks driven purely by scaled post-training.
- GLM-5.3 ties Kimi K3 at 60 on Artificial Analysis Intelligence Index, top open-weights slot.
- 246-point Elo jump on agentic GDPval-AA v2 (1524 to 1770), second only to Claude Opus 5.
- 753B total / 40B active MoE, 1M context, MIT license, weights arriving on Hugging Face in ~2 weeks.
- API pricing: $1.40/M input, $4.40/M output, 81% cache discount on repeat inputs.
- All gains from scaled post-training on the same GLM-5.2 base, no new pretraining run.
- Emergent multi-stage exploit reasoning delayed the weights release for extra safety review.
Z.ai has pushed GLM-5.3 to the top of the open-weights leaderboard, and the interesting part is how they got there. The base model is unchanged from GLM-5.2. Everything you see in the benchmarks came from throwing more reinforcement learning environments, more diverse tasks, and more compute at post-training. The launch blog puts it bluntly: Scaling post-training is all we did for GLM-5.3.
That's a striking claim because the score jumps are not small. On Artificial Analysis's real-world agentic evaluation GDPval-AA v2, the model's Elo climbed from 1524 to 1770, a 246-point leap that puts it second across all models tested, behind only Claude Opus 5 at 1855 and more than 100 points ahead of the previous open-weights leader Kimi K3. On the broader Artificial Analysis Intelligence Index it now sits at 60, tied with Kimi K3 for the top open-weights spot.
What you actually get
The architecture is a mixture-of-experts with 753B total parameters and 40B active, unchanged from the previous release. Context window is 1M tokens. The license is MIT. Pricing on the first-party API is $1.40 per million input tokens and $4.40 per million output tokens, with an 81% discount ($0.26/M) on cached input. The model is currently available through Z.ai's coding plan, coming soon to their API and in two weeks' time to Hugging Face as open weights.
Notably, this puts the model at the frontier of agentic coding benchmarks with only around 750B parameters, roughly a third of Kimi K3's size. That matters for anyone planning to self-host once the weights drop.