GLM-5.1 vs Kimi K2 Thinking
Benchmarks, API pricing and specs, head to head. Data updated 2026-06-10.
GLM-5.1
Z.ai (Zhipu) · Apr 2026
The April 2026 GLM refresh — the first open-weights model to take #1 on SWE-bench Pro, edging out Claude Opus 4.6.
Kimi K2 Thinking
67.6Moonshot AI · Nov 2025
A trillion-parameter open reasoning agent that can chain 200–300 tool calls — the open-weights agentic standout of late 2025.
The verdict
These two models don't yet share verified results on the benchmarks we track, so judge them on specs, pricing and intended use. Kimi K2 Thinking is about 2.0x cheaper per blended million tokens (3:1 input:output mix). Kimi K2 Thinking also takes 262K of context versus 200K for GLM-5.1.
Specs & pricing
| GLM-5.1 | Kimi K2 Thinking | |
|---|---|---|
| modhub Index | — | 67.6 |
| Input price / 1M | $1.4 | $0.6 |
| Output price / 1M | $4.4 | $2.5 |
| Context window | 200K | 262K |
| Max output | 128K | — |
| Open weights | yes (MIT) | yes (Modified MIT) |
| Reasoning model | yes | yes |
| Multimodal input | text | text |
| Knowledge cutoff | Feb 2026 | Apr 2025 |
| Released | Apr 2026 | Nov 2025 |
| Example monthly cost* | $20.60 | $9.75 |
* 10M input + 1.5M output tokens per month at list prices, no caching. Green = better value on that row.
Frequently asked questions
- Which is better, GLM-5.1 or Kimi K2 Thinking?
- These two models don't yet share verified results on the benchmarks we track, so judge them on specs, pricing and intended use. Kimi K2 Thinking is about 2.0x cheaper per blended million tokens (3:1 input:output mix). Kimi K2 Thinking also takes 262K of context versus 200K for GLM-5.1.
- Which is cheaper, GLM-5.1 or Kimi K2 Thinking?
- GLM-5.1 costs $1.4/$4.4 per million input/output tokens, while Kimi K2 Thinking costs $0.6/$2.5. For a typical workload of 10M input and 1.5M output tokens per month, that's $20.60 versus $9.75.
- Which model is better for coding, GLM-5.1 or Kimi K2 Thinking?
- We don't yet track SWE-bench Verified results for both models; check their individual pages for coding-related scores.