GLM-5.3 or Kimi K2.6: which to choose?

Two models head to head

This comparison pits two open-weight models that you can install on your own servers against each other: GLM-5.3 and Kimi K2.6, one from Zhipu AI (Z.ai), the other from Moonshot AI. The first dates from August 14, 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.

Performance

On our capability index, GLM-5.3 scores 78.8 out of 100 and Kimi K2.6 84.4. Kimi K2.6 leads by 5.6 points.

On the 3 benchmarks reported for both, GLM-5.3 wins 2 times and Kimi K2.6 1:

• GPQA Diamond (PhD-level science questions): GLM-5.3 90.9%, Kimi K2.6 90.8%
• OTIS Mock AIME (competition mathematics problems): GLM-5.3 91.1%, Kimi K2.6 96.1%
• FrontierMath (very hard research-level mathematics): GLM-5.3 68.8%, Kimi K2.6 57.2%

Pricing

GLM-5.3 costs $1.2 per million input tokens and $4.18 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 $0.032 with GLM-5.3 versus $0.027 with Kimi K2.6. Kimi K2.6 is 1.2 times cheaper, a gap that adds up quickly over 1,000 tasks: $5.27 difference.

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

Under the hood

GLM-5.3 reads more in one go: 1,000,000 tokens of context (roughly 1,500 pages), versus 262,144 for Kimi K2.6.

Reasoning mode: GLM-5.3 yes, Kimi K2.6 yes. Tool calling: GLM-5.3 yes, Kimi K2.6 yes. Image input: GLM-5.3 no, Kimi K2.6 yes.

Both publish their weights: you can host them yourself.

In short

Our reading of the data:

• Choose GLM-5.3 for a larger context window and a more recent release.
• Choose Kimi K2.6 for a higher capability index, a lower cost and image input.

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