GLM-5.3-Flash versus Kimi K2.5

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-Flash and Kimi K2.5, one from Zhipu AI (Z.ai), the other from Moonshot AI. The first dates from September 18, 2026, the second from January 27, 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-5.3-Flash scores 71.9 out of 100 and Kimi K2.5 78.1. Kimi K2.5 leads by 6.2 points.

On the 2 benchmarks reported for both, GLM-5.3-Flash wins 2 times and Kimi K2.5 0:

• GPQA Diamond (PhD-level science questions): GLM-5.3-Flash 90.2%, Kimi K2.5 87.6%
• OTIS Mock AIME (competition mathematics problems): GLM-5.3-Flash 93.9%, Kimi K2.5 92.2%

Budget

GLM-5.3-Flash costs $0.30 per million input tokens and $1.05 per million output tokens; Kimi K2.5 costs $0.58 and $3.

For a typical agent task (read 20,000 tokens, write 2,000), expect $0.008 with GLM-5.3-Flash versus $0.018 with Kimi K2.5. GLM-5.3-Flash is 2.2 times cheaper, a gap that adds up quickly over 1,000 tasks: $9.53 difference.

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

Context and capabilities

GLM-5.3-Flash reads more in one go: 1,000,000 tokens of context (roughly 1,500 pages), versus 262,144 for Kimi K2.5.

Reasoning mode: GLM-5.3-Flash yes, Kimi K2.5 yes. Tool calling: GLM-5.3-Flash yes, Kimi K2.5 yes. Image input: GLM-5.3-Flash yes, Kimi K2.5 yes.

Both publish their weights: you can host them yourself.

In short

Our reading of the data:

• Choose GLM-5.3-Flash for a lower cost, a larger context window and a more recent release.
• Choose Kimi K2.5 for a higher capability index.

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