Three Chinese labs now hold the top of the open-weight leaderboard. Moonshot AI’s Kimi K3, DeepSeek V4 Pro, and Zhipu AI’s GLM-5.2 are all sparse Mixture-of-Experts (MoE) models with million-token context windows. Each targets long-horizon coding and agent workloads. This article compares them on three axes an AI team actually decides on: measured capability, license terms, and serving cost.
‘Trillion-parameter’ fits Kimi K3 (2.8T) and DeepSeek V4 Pro (1.6T). GLM-5.2 is 744B total, so it is the smallest of the three by total parameters. It earns its place because it led the open-weight field before K3 shipped.
Which model for which job
For lowest cost per token at strong coding quality, DeepSeek V4 Pro is the clear pick. Its weights are downloadable, its license is clean, and its output price undercuts both rivals.
For the highest measured capability, Kimi K3 leads, but at 5x to 17x the output price and no downloadable weights until July 27. GLM-5.2 sits between them: cheaper than K3, faster than both rivals, self-hostable today, and more capable than its size suggests.
If you are planning to choose based on verification depth and license clarity favor DeepSeek and GLM now. Buyers chasing peak benchmark scores wait for K3 weights or pay the API premium.
Key Takeaways
- Kimi K3 leads the Artificial Analysis Intelligence Index (~57, #3 overall) but stays API-only until July 27.
- DeepSeek V4 Pro is the cost leader: ~$0.04 per task and ~1.15M output tokens per dollar at list rates.
- GLM-5.2 (744B) is the smallest yet fastest (~168 t/s) and self-hostable today under MIT.
- All three ship 1M-token context; only DeepSeek and GLM have open weights available now.
The post Kimi K3 vs DeepSeek V4 Pro vs GLM-5.2: Open Trillion-Scale MoE Models Compared on Benchmarks, License, and Serving Cost appeared first on MarkTechPost.