Model comparison
GLM-4.7 vs Kimi K2-Instruct-0905
- Input
- $1.40 / 1M tokens
- Output
- $2.20 / 1M tokens
- Context
- 200K
- Released
- 2025-12-22
- Input
- $0.60 / 1M tokens
- Output
- $2.50 / 1M tokens
- Context
- 256K
- Released
- 2025-09-09
GLM-4.7 scores higher on 3 of the 3 benchmarks both models report; Kimi K2-Instruct-0905 is cheaper on input tokens ($0.6 vs $1.4 per 1M, 2.3×); Kimi K2-Instruct-0905 has the larger context window (256K vs 200K).
Specs and pricing side by side
| Spec | GLM-4.7 | Kimi K2-Instruct-0905 |
|---|---|---|
| Maker | Z.ai (Zhipu / GLM) | Moonshot AI (Kimi) |
| Released | 2025-12-22 | 2025-09-09 |
| Context window | 200K | 256K |
| Max output | 128K | ~33K tokens |
| Input price | $1.40 / 1M tokens | $0.60 / 1M tokens |
| Output price | $2.20 / 1M tokens | $2.50 / 1M tokens |
| Parameters | 355B total / 32B active (MoE) | 1T total / 32B active (MoE) |
| License | MIT | Modified MIT License |
| Weights | Open weights | Open weightssame |
| Modalities | Text | Textsame |
| Knowledge cutoff | not published | Not publicly disclosed |
Benchmarks both models report
| Benchmark | GLM-4.7 | Kimi K2-Instruct-0905 |
|---|---|---|
| SWE-bench Verified | 73.8% | 69.2% |
| SWE-bench Multilingual | 66.7% | 55.9% |
| LiveCodeBench v6 | 84.9% | 53.7% |
Scores are as published by each maker or its cited source; harness, reasoning effort and date can differ between rows, so treat small gaps as ties. Sources are listed on each model page.
Which one should you pick?
Pick GLM-4.7 if…
- You care about SWE-bench Verified, SWE-bench Multilingual, LiveCodeBench v6 (it scores higher on 3 shared benchmarks).
- Your use case is: autonomous and semi-autonomous coding agents that resolve real GitHub issues across multiple files.
Pick Kimi K2-Instruct-0905 if…
- Cost matters: input tokens cost 2.3× less ($0.6 vs $1.4 per 1M).
- You need the longer context (256K vs 200K).
- Your use case is: autonomous coding agents that edit, build, and test across multi-file repositories.
FAQ
Which is better, GLM-4.7 or Kimi K2-Instruct-0905?
GLM-4.7 scores higher on 3 of the 3 benchmarks both models report; Kimi K2-Instruct-0905 is cheaper on input tokens ($0.6 vs $1.4 per 1M, 2.3×); Kimi K2-Instruct-0905 has the larger context window (256K vs 200K). The better pick depends on whether you value benchmark scores, price per token, context length or openness for your workload.
How much cheaper is Kimi K2-Instruct-0905?
GLM-4.7: $1.40 / 1M tokens input, $2.20 / 1M tokens output. Kimi K2-Instruct-0905: $0.60 / 1M tokens input, $2.50 / 1M tokens output (list API prices as published by each maker).
What are the context windows of GLM-4.7 and Kimi K2-Instruct-0905?
GLM-4.7 offers 200K with up to 128K output; Kimi K2-Instruct-0905 offers 256K with up to ~33K tokens output.
Is GLM-4.7 newer than Kimi K2-Instruct-0905?
GLM-4.7 was released 2025-12-22; Kimi K2-Instruct-0905 was released 2025-09-09.