GLM-4.6 vs Kimi K1.5 Instruct: which model should you choose?
We compared GLM-4.6 and Kimi K1.5 Instruct on price, context window and 12 benchmarks. Kimi K1.5 Instruct 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 Instruct
Kimi K1.5 Instruct is a multimodal model from Moonshot AI. It offers a 200,000-token context window, costs $5.15 per million input tokens and $28.20 per million output tokens, and averages 80.5 across the index's benchmark suite.
- Input
- $5.15 / 1M
- Output
- $28.20 / 1M
- Context
- 200,000
- Avg score
- 80.5
- Open weights
- Yes
Specification comparison
| Spec | GLM-4.6 | Kimi K1.5 Instruct | Edge |
|---|---|---|---|
| Input price / 1M | $5.10 | $5.15 | A |
| Output price / 1M | $18.45 | $28.20 | A |
| Context window | 128,000 | 200,000 | B |
| Max output | 8,192 | 4,096 | A |
| Average score | 80.2 | 80.5 | B |
| Arena Elo | 1440.7 | 1431.8 | A |
| Open weights | Yes | Yes | Tie |
Benchmark comparison
GLM-4.6 wins 6, Kimi K1.5 Instruct wins 6, and 0 benchmarks are tied. The table shows the modelled score for each model and the gap.
| Benchmark | GLM-4.6 | Kimi K1.5 Instruct | Gap | Winner |
|---|---|---|---|---|
| MMLU-Pro | 84.0 | 83.0 | 1.0 | GLM-4.6 |
| GPQA Diamond | 78.3 | 77.4 | 0.9 | GLM-4.6 |
| SWE-bench Verified | 67.4 | 67.8 | 0.4 | Kimi K1.5 Instruct |
| HumanEval | 83.8 | 84.0 | 0.2 | Kimi K1.5 Instruct |
| MATH-500 | 83.4 | 82.6 | 0.8 | GLM-4.6 |
| AIME 2025 | 80.2 | 80.4 | 0.2 | Kimi K1.5 Instruct |
| LiveCodeBench | 71.6 | 71.1 | 0.5 | GLM-4.6 |
| IFEval | 86.4 | 86.2 | 0.2 | GLM-4.6 |
| TruthfulQA | 82.5 | 84.3 | 1.8 | Kimi K1.5 Instruct |
| HellaSwag | 89.4 | 90.2 | 0.8 | Kimi K1.5 Instruct |
| MMMU | 75.4 | 78.6 | 3.2 | Kimi K1.5 Instruct |
| Arena Elo | 1440.7 | 1431.8 | 8.9 | 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 Instruct costs about $10.91. GLM-4.6 is roughly 23% cheaper than Kimi K1.5 Instruct. At high volume, that gap compounds quickly, so price should carry real weight unless the quality difference is decisive.
Kimi K1.5 Instruct has the larger context window at 200,000 tokens, compared with 128,000 for GLM-4.6. That matters for long documents, large codebases and multi-step agent traces.
Verdict
Across 12 shared benchmarks, GLM-4.6 wins 6, Kimi K1.5 Instruct wins 6, and 0 are tied. On the index average, Kimi K1.5 Instruct leads GLM-4.6 by 0.3 points (80.5 versus 80.2).
The price picture favours GLM-4.6. The context picture favours Kimi K1.5 Instruct. 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 Instruct 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 Instruct if
- You want the higher average benchmark score in this index.
- You need the larger 200,000-token context window.
FAQ
Is GLM-4.6 better than Kimi K1.5 Instruct?
On the index average, Kimi K1.5 Instruct leads GLM-4.6 by 0.3 points (80.5 versus 80.2). The better choice depends on price, context and your task. GLM-4.6 wins 6 benchmarks, Kimi K1.5 Instruct wins 6.
Which is cheaper, GLM-4.6 or Kimi K1.5 Instruct?
GLM-4.6 is cheaper on a blended 3:1 basis at about $8.44 per million tokens, versus $10.91 for Kimi K1.5 Instruct.
Which has the larger context window?
Kimi K1.5 Instruct has the larger context window at 200,000 tokens. GLM-4.6 has 128,000 tokens.
Are these models open weights?
GLM-4.6 is open weights; Kimi K1.5 Instruct is open weights.
Which should I choose for production?
If you need a single default, start with Kimi K1.5 Instruct 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.