Kimi K1.5 Chat vs Qwen3 Instruct: which model should you choose?
We compared Kimi K1.5 Chat and Qwen3 Instruct on price, context window and 12 benchmarks. Kimi K1.5 Chat has the stronger average benchmark score; Qwen3 Instruct is cheaper on a blended 3:1 basis.
Kimi K1.5 Chat
Kimi K1.5 Chat is a multimodal model from Moonshot AI. It offers a 128,000-token context window, costs $5.11 per million input tokens and $26.59 per million output tokens, and averages 80.2 across the index's benchmark suite.
- Input
- $5.11 / 1M
- Output
- $26.59 / 1M
- Context
- 128,000
- Avg score
- 80.2
- Open weights
- Yes
Qwen3 Instruct
Qwen3 Instruct is a multimodal model from Alibaba Cloud. It offers a 1,000,000-token context window, costs $5.06 per million input tokens and $25.39 per million output tokens, and averages 79.8 across the index's benchmark suite.
- Input
- $5.06 / 1M
- Output
- $25.39 / 1M
- Context
- 1,000,000
- Avg score
- 79.8
- Open weights
- Yes
Specification comparison
| Spec | Kimi K1.5 Chat | Qwen3 Instruct | Edge |
|---|---|---|---|
| Input price / 1M | $5.11 | $5.06 | B |
| Output price / 1M | $26.59 | $25.39 | B |
| Context window | 128,000 | 1,000,000 | B |
| Max output | 32,768 | 128,000 | B |
| Average score | 80.2 | 79.8 | A |
| Arena Elo | 1422.6 | 1419.2 | A |
| Open weights | Yes | Yes | Tie |
Benchmark comparison
Kimi K1.5 Chat wins 8, Qwen3 Instruct wins 4, and 0 benchmarks are tied. The table shows the modelled score for each model and the gap.
| Benchmark | Kimi K1.5 Chat | Qwen3 Instruct | Gap | Winner |
|---|---|---|---|---|
| MMLU-Pro | 81.3 | 80.3 | 1.0 | Kimi K1.5 Chat |
| GPQA Diamond | 78.1 | 74.1 | 4.0 | Kimi K1.5 Chat |
| SWE-bench Verified | 69.9 | 68.5 | 1.4 | Kimi K1.5 Chat |
| HumanEval | 84.3 | 85.1 | 0.8 | Qwen3 Instruct |
| MATH-500 | 81.7 | 86.1 | 4.4 | Qwen3 Instruct |
| AIME 2025 | 81.4 | 78.5 | 2.9 | Kimi K1.5 Chat |
| LiveCodeBench | 75.7 | 71.1 | 4.6 | Kimi K1.5 Chat |
| IFEval | 88.2 | 89.1 | 0.9 | Qwen3 Instruct |
| TruthfulQA | 80.3 | 79.7 | 0.6 | Kimi K1.5 Chat |
| HellaSwag | 87.2 | 87.0 | 0.2 | Kimi K1.5 Chat |
| MMMU | 73.6 | 78.7 | 5.1 | Qwen3 Instruct |
| Arena Elo | 1422.6 | 1419.2 | 3.4 | Kimi K1.5 Chat |
Price analysis
On a blended 3:1 input-to-output basis, Kimi K1.5 Chat costs about $10.48 per million tokens and Qwen3 Instruct costs about $10.14. Qwen3 Instruct is roughly 3% cheaper than Kimi K1.5 Chat. At high volume, that gap compounds quickly, so price should carry real weight unless the quality difference is decisive.
Qwen3 Instruct has the larger context window at 1,000,000 tokens, compared with 128,000 for Kimi K1.5 Chat. That matters for long documents, large codebases and multi-step agent traces.
Verdict
Across 12 shared benchmarks, Kimi K1.5 Chat wins 8, Qwen3 Instruct wins 4, and 0 are tied. On the index average, Kimi K1.5 Chat leads Qwen3 Instruct by 0.4 points (80.2 versus 79.8).
The price picture favours Qwen3 Instruct. The context picture favours Qwen3 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 Chat for quality-sensitive work and Qwen3 Instruct for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.
Choose Kimi K1.5 Chat if
- You want the higher average benchmark score in this index.
Choose Qwen3 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.
FAQ
Is Kimi K1.5 Chat better than Qwen3 Instruct?
On the index average, Kimi K1.5 Chat leads Qwen3 Instruct by 0.4 points (80.2 versus 79.8). The better choice depends on price, context and your task. Kimi K1.5 Chat wins 8 benchmarks, Qwen3 Instruct wins 4.
Which is cheaper, Kimi K1.5 Chat or Qwen3 Instruct?
Qwen3 Instruct is cheaper on a blended 3:1 basis at about $10.14 per million tokens, versus $10.48 for Kimi K1.5 Chat.
Which has the larger context window?
Qwen3 Instruct has the larger context window at 1,000,000 tokens. Kimi K1.5 Chat has 128,000 tokens.
Are these models open weights?
Kimi K1.5 Chat is open weights; Qwen3 Instruct is open weights.
Which should I choose for production?
If you need a single default, start with Kimi K1.5 Chat for quality-sensitive work and Qwen3 Instruct for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.