GLM-4.6 Instruct vs Kimi K1.5 Instruct: which model should you choose?
We compared GLM-4.6 Instruct 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 Instruct is cheaper on a blended 3:1 basis.
GLM-4.6 Instruct
GLM-4.6 Instruct is a multimodal model from Zhipu AI. It offers a 1,000,000-token context window, costs $5.15 per million input tokens and $24.32 per million output tokens, and averages 80.4 across the index's benchmark suite.
- Input
- $5.15 / 1M
- Output
- $24.32 / 1M
- Context
- 1,000,000
- Avg score
- 80.4
- 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 Instruct | Kimi K1.5 Instruct | Edge |
|---|---|---|---|
| Input price / 1M | $5.15 | $5.15 | Tie |
| Output price / 1M | $24.32 | $28.20 | A |
| Context window | 1,000,000 | 200,000 | A |
| Max output | 128,000 | 4,096 | A |
| Average score | 80.4 | 80.5 | B |
| Arena Elo | 1426.6 | 1431.8 | B |
| Open weights | Yes | Yes | Tie |
Benchmark comparison
GLM-4.6 Instruct wins 7, Kimi K1.5 Instruct wins 5, and 0 benchmarks are tied. The table shows the modelled score for each model and the gap.
| Benchmark | GLM-4.6 Instruct | Kimi K1.5 Instruct | Gap | Winner |
|---|---|---|---|---|
| MMLU-Pro | 80.6 | 83.0 | 2.4 | Kimi K1.5 Instruct |
| GPQA Diamond | 75.8 | 77.4 | 1.6 | Kimi K1.5 Instruct |
| SWE-bench Verified | 70.5 | 67.8 | 2.7 | GLM-4.6 Instruct |
| HumanEval | 85.3 | 84.0 | 1.3 | GLM-4.6 Instruct |
| MATH-500 | 84.9 | 82.6 | 2.3 | GLM-4.6 Instruct |
| AIME 2025 | 74.5 | 80.4 | 5.9 | Kimi K1.5 Instruct |
| LiveCodeBench | 72.6 | 71.1 | 1.5 | GLM-4.6 Instruct |
| IFEval | 87.4 | 86.2 | 1.2 | GLM-4.6 Instruct |
| TruthfulQA | 83.8 | 84.3 | 0.5 | Kimi K1.5 Instruct |
| HellaSwag | 90.6 | 90.2 | 0.4 | GLM-4.6 Instruct |
| MMMU | 78.9 | 78.6 | 0.3 | GLM-4.6 Instruct |
| Arena Elo | 1426.6 | 1431.8 | 5.2 | Kimi K1.5 Instruct |
Price analysis
On a blended 3:1 input-to-output basis, GLM-4.6 Instruct costs about $9.94 per million tokens and Kimi K1.5 Instruct costs about $10.91. GLM-4.6 Instruct is roughly 9% 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.
GLM-4.6 Instruct has the larger context window at 1,000,000 tokens, compared with 200,000 for Kimi K1.5 Instruct. That matters for long documents, large codebases and multi-step agent traces.
Verdict
Across 12 shared benchmarks, GLM-4.6 Instruct wins 7, Kimi K1.5 Instruct wins 5, and 0 are tied. On the index average, Kimi K1.5 Instruct leads GLM-4.6 Instruct by 0.1 points (80.5 versus 80.4).
The price picture favours GLM-4.6 Instruct. The context picture favours GLM-4.6 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 Instruct for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.
Choose GLM-4.6 Instruct if
- Unit cost matters and you want the cheaper blended price.
- You need the larger 1,000,000-token context window.
- You need the larger 128,000-token output limit.
Choose Kimi K1.5 Instruct if
- You want the higher average benchmark score in this index.
FAQ
Is GLM-4.6 Instruct better than Kimi K1.5 Instruct?
On the index average, Kimi K1.5 Instruct leads GLM-4.6 Instruct by 0.1 points (80.5 versus 80.4). The better choice depends on price, context and your task. GLM-4.6 Instruct wins 7 benchmarks, Kimi K1.5 Instruct wins 5.
Which is cheaper, GLM-4.6 Instruct or Kimi K1.5 Instruct?
GLM-4.6 Instruct is cheaper on a blended 3:1 basis at about $9.94 per million tokens, versus $10.91 for Kimi K1.5 Instruct.
Which has the larger context window?
GLM-4.6 Instruct has the larger context window at 1,000,000 tokens. Kimi K1.5 Instruct has 200,000 tokens.
Are these models open weights?
GLM-4.6 Instruct 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 Instruct for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.