GLM-4.6 vs Kimi K1.5: which model should you choose?
We compared GLM-4.6 and Kimi K1.5 on price, context window and 12 benchmarks. Kimi K1.5 has the stronger average benchmark score; GLM-4.6 is cheaper on a blended 3:1 basis.
GLM-4.6
GLM-4.6 is a multimodal model from Zhipu AI. It offers a 128,000-token context window, costs $5.10 per million input tokens and $18.45 per million output tokens, and averages 80.2 across the index's benchmark suite.
- Input
- $5.10 / 1M
- Output
- $18.45 / 1M
- Context
- 128,000
- Avg score
- 80.2
- Open weights
- Yes
Kimi K1.5
Kimi K1.5 is a multimodal model from Moonshot AI. It offers a 128,000-token context window, costs $5.19 per million input tokens and $21.20 per million output tokens, and averages 80.7 across the index's benchmark suite.
- Input
- $5.19 / 1M
- Output
- $21.20 / 1M
- Context
- 128,000
- Avg score
- 80.7
- Open weights
- Yes
Specification comparison
| Spec | GLM-4.6 | Kimi K1.5 | Edge |
|---|---|---|---|
| Input price / 1M | $5.10 | $5.19 | A |
| Output price / 1M | $18.45 | $21.20 | A |
| Context window | 128,000 | 128,000 | Tie |
| Max output | 8,192 | 4,096 | A |
| Average score | 80.2 | 80.7 | B |
| Arena Elo | 1440.7 | 1432.2 | A |
| Open weights | Yes | Yes | Tie |
Benchmark comparison
GLM-4.6 wins 5, Kimi K1.5 wins 7, and 0 benchmarks are tied. The table shows the modelled score for each model and the gap.
| Benchmark | GLM-4.6 | Kimi K1.5 | Gap | Winner |
|---|---|---|---|---|
| MMLU-Pro | 84.0 | 82.7 | 1.3 | GLM-4.6 |
| GPQA Diamond | 78.3 | 74.4 | 3.9 | GLM-4.6 |
| SWE-bench Verified | 67.4 | 68.2 | 0.8 | Kimi K1.5 |
| HumanEval | 83.8 | 87.1 | 3.3 | Kimi K1.5 |
| MATH-500 | 83.4 | 85.3 | 1.9 | Kimi K1.5 |
| AIME 2025 | 80.2 | 80.8 | 0.6 | Kimi K1.5 |
| LiveCodeBench | 71.6 | 71.1 | 0.5 | GLM-4.6 |
| IFEval | 86.4 | 87.9 | 1.5 | Kimi K1.5 |
| TruthfulQA | 82.5 | 84.7 | 2.2 | Kimi K1.5 |
| HellaSwag | 89.4 | 87.2 | 2.2 | GLM-4.6 |
| MMMU | 75.4 | 77.8 | 2.4 | Kimi K1.5 |
| Arena Elo | 1440.7 | 1432.2 | 8.5 | GLM-4.6 |
Price analysis
On a blended 3:1 input-to-output basis, GLM-4.6 costs about $8.44 per million tokens and Kimi K1.5 costs about $9.19. GLM-4.6 is roughly 8% cheaper than Kimi K1.5. At high volume, that gap compounds quickly, so price should carry real weight unless the quality difference is decisive.
Both models list a 128,000-token context window. That is enough for long documents, large codebases and multi-step agent traces.
Verdict
Across 12 shared benchmarks, GLM-4.6 wins 5, Kimi K1.5 wins 7, and 0 are tied. On the index average, Kimi K1.5 leads GLM-4.6 by 0.5 points (80.7 versus 80.2).
The price picture favours GLM-4.6. The context picture is effectively a tie. Those two dimensions often matter more than a few benchmark points once a model is good enough for the task.
If you need a single default, start with Kimi K1.5 for quality-sensitive work and GLM-4.6 for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.
Choose GLM-4.6 if
- Unit cost matters and you want the cheaper blended price.
- You need the larger 8,192-token output limit.
Choose Kimi K1.5 if
- You want the higher average benchmark score in this index.
FAQ
Is GLM-4.6 better than Kimi K1.5?
On the index average, Kimi K1.5 leads GLM-4.6 by 0.5 points (80.7 versus 80.2). The better choice depends on price, context and your task. GLM-4.6 wins 5 benchmarks, Kimi K1.5 wins 7.
Which is cheaper, GLM-4.6 or Kimi K1.5?
GLM-4.6 is cheaper on a blended 3:1 basis at about $8.44 per million tokens, versus $9.19 for Kimi K1.5.
Which has the larger context window?
Both models list a 128,000-token context window.
Are these models open weights?
GLM-4.6 is open weights; Kimi K1.5 is open weights.
Which should I choose for production?
If you need a single default, start with Kimi K1.5 for quality-sensitive work and GLM-4.6 for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.