GLM-5.1 or Kimi K2.6: which to choose?

What are we comparing?

This comparison pits two open-weight models that you can install on your own servers against each other: GLM-5.1 and Kimi K2.6, one from Zhipu AI (Z.ai), the other from Moonshot AI. The first dates from April 7, 2026, the second from April 20, 2026.

All figures below come from public sources checked on October 22, 2026: models.dev and OpenRouter for prices and capabilities, Epoch AI for scores.

What the benchmarks say

On our capability index, GLM-5.1 scores 70.9 out of 100 and Kimi K2.6 84.4. Kimi K2.6 leads by 13.5 points.

On the 4 benchmarks reported for both, GLM-5.1 wins 0 times and Kimi K2.6 4:

• GPQA Diamond (PhD-level science questions): GLM-5.1 89.9%, Kimi K2.6 90.8%
• OTIS Mock AIME (competition mathematics problems): GLM-5.1 93.3%, Kimi K2.6 96.1%
• FrontierMath (very hard research-level mathematics): GLM-5.1 36.8%, Kimi K2.6 57.2%
• SWE-bench Verified (fixing real software bugs): GLM-5.1 74.2%, Kimi K2.6 76.7%

Which one costs less?

GLM-5.1 costs $0.90 per million input tokens and $3.59 per million output tokens; Kimi K2.6 costs $0.95 and $4.

For a typical agent task (read 20,000 tokens, write 2,000), expect $0.025 with GLM-5.1 versus $0.027 with Kimi K2.6. The cost is almost the same.

Run the numbers with your own volumes in the agent cost calculator.

Context and capabilities

Kimi K2.6 reads more in one go: 262,144 tokens of context (roughly 393 pages), versus 200,000 for GLM-5.1.

Reasoning mode: GLM-5.1 yes, Kimi K2.6 yes. Tool calling: GLM-5.1 yes, Kimi K2.6 yes. Image input: GLM-5.1 no, Kimi K2.6 yes.

Both publish their weights: you can host them yourself.

In short

Our reading of the data:

• Choose Kimi K2.6 for a higher capability index, a larger context window and image input.

This verdict relies on public figures; it does not replace a trial on your own tasks.