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.

The three contenders

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

Benchmarks

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.

License

All three ship as open-weight models, but the practical status differs today.

DeepSeek V4 Pro is MIT-licensed, with weights on Hugging Face from day one. GLM-5.2 is also MIT-licensed, with full weights on Hugging Face under the zai-org organization. Both allow unrestricted commercial use, fine-tuning, and self-hosting now.

Kimi K3 is the exception. Moonshot has committed to publishing weights by July 27, 2026, expected under a Modified MIT license. Until then, K3 is usable only through the API and Kimi apps. Moonshot’s recent Modified MIT terms add one attribution clause. It triggers only above 100M monthly active users.

Serving cost

API list pricing separates these models sharply.

DeepSeek V4 Pro is the cost leader by a wide margin. At list output rates, one dollar buys roughly 1.15M output tokens from V4 Pro, about 227K from GLM-5.2, and about 67K from K3.

Artificial Analysis prices every model on one blended 7:2:1 cache/input/output basis, which removes vendor framing. On that basis it lists K3 at $2.31 per 1M tokens, GLM-5.2 at $0.90, and DeepSeek V4 Pro at $0.18. On cost per task, the same source reports K3 at $0.94, GLM-5.2 at $0.32, and DeepSeek V4 Pro at $0.04.

Speed also differs. Artificial Analysis measures GLM-5.2 at about 168 tokens/sec, well ahead of DeepSeek V4 Pro and Kimi K3 at about 62 each. Moonshot reports above 90% cache hits in coding workloads, which drops K3’s effective input cost to $0.30 per million.

Self-hosting is a different constraint. GLM-5.2 at 744B needs over 1TB of VRAM in BF16, or roughly 8x H200 at FP8. DeepSeek V4 Pro at 1.6T needs more still. Kimi K3 is heaviest: Moonshot recommends 64 or more accelerators, putting local serving out of reach for most teams. K3 uses MXFP4 weights with MXFP8 activations for broader hardware support.

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.

Michal Sutter is a data science professional with a Master of Science in Data Science from the University of Padova. With a solid foundation in statistical analysis, machine learning, and data engineering, Michal excels at transforming complex datasets into actionable insights.