Kimi K3 vs DeepSeek V4 Pro vs GLM-5.2: Open Trillion-Scale MoE Models Compared on Benchmarks, License, and Serving Cost

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.

Kimi K3 is a 2.8-trillion-parameter Stable LatentMoE model activating 16 of 896 experts per token. Moonshot has not published the exact active-parameter count. K3 adds native vision, a 1M-token context window, and always-on reasoning. Moonshot calls it the first open 3T-class model. Our launch coverage is here .

DeepSeek V4 Pro is a 1.6-trillion-parameter MoE with 49B active parameters, using 384 routed experts plus one shared expert. It carries a 1M-token context window with 384K max output. A smaller V4 Flash variant (284B total, 13B active) covers cheaper workloads. Weights are on Hugging Face .

GLM-5.2 is a 744-billion-parameter MoE with roughly 40B active parameters and a 1M-token context window. Zhipu ships it with High and Max reasoning modes. It comes with API access

Vendor-reported scores use different harnesses, so per-benchmark numbers rarely line up cleanly across labs. The neutral comparator is the Artificial Analysis Intelligence Index , which scores all three on the same suite.

On that index, Kimi K3 scores about 57 , DeepSeek V4 Pro (Max reasoning) scores 44, and GLM-5.2 scores 51. K3 ranks #3 overall, behind only Claude Fable 5 and GPT-5.6 Sol, and comparable to Opus 4.8 and GPT-5.5. GLM-5.2 held the top open-weight spot until K3 shipped.

Coding benchmarks tell a similar story with caveats. Moonshot’s own table runs K3 and GLM-5.2 through matched harnesses. There, K3 leads GLM-5.2 on every shared benchmark by wide margins.

DeepSeek does not appear in Moonshot’s table, so its numbers come from separate testing. DeepSeek-V4-Pro-Max scores 80.6% on SWE-bench Verified , the highest open-weight result at its release and tied with Gemini 3.1 Pro. It also posts 83.5 on MRCR 1M, confirming serious long-context ability. GLM-5.2 scored 62.1 on SWE-bench Pro , edging GPT-5.5 at 58.6.

So, K3 is the strongest of the three on measured capability. DeepSeek V4 Pro is competitive on isolated coding tasks. GLM-5.2 trails K3 but remains a capable open-weight option.