DeepSeek-V3.2 Chat vs Kimi K2 Chat: which model should you choose?
We compared DeepSeek-V3.2 Chat and Kimi K2 Chat on price, context window and 12 benchmarks. DeepSeek-V3.2 Chat has the stronger average benchmark score; Kimi K2 Chat is cheaper on a blended 3:1 basis.
DeepSeek-V3.2 Chat
DeepSeek-V3.2 Chat is a multimodal model from DeepSeek. It offers a 1,000,000-token context window, costs $5.80 per million input tokens and $26.43 per million output tokens, and averages 82.5 across the index's benchmark suite.
- Input
- $5.80 / 1M
- Output
- $26.43 / 1M
- Context
- 1,000,000
- Avg score
- 82.5
- Open weights
- Yes
Kimi K2 Chat
Kimi K2 Chat is a multimodal model from Moonshot AI. It offers a 400,000-token context window, costs $5.74 per million input tokens and $22.24 per million output tokens, and averages 82.1 across the index's benchmark suite.
- Input
- $5.74 / 1M
- Output
- $22.24 / 1M
- Context
- 400,000
- Avg score
- 82.1
- Open weights
- Yes
Specification comparison
| Spec | DeepSeek-V3.2 Chat | Kimi K2 Chat | Edge |
|---|---|---|---|
| Input price / 1M | $5.80 | $5.74 | B |
| Output price / 1M | $26.43 | $22.24 | B |
| Context window | 1,000,000 | 400,000 | A |
| Max output | 8,192 | 4,096 | A |
| Average score | 82.5 | 82.1 | A |
| Arena Elo | 1462.2 | 1445.6 | A |
| Open weights | Yes | Yes | Tie |
Benchmark comparison
DeepSeek-V3.2 Chat wins 6, Kimi K2 Chat wins 6, and 0 benchmarks are tied. The table shows the modelled score for each model and the gap.
| Benchmark | DeepSeek-V3.2 Chat | Kimi K2 Chat | Gap | Winner |
|---|---|---|---|---|
| MMLU-Pro | 82.5 | 82.7 | 0.2 | Kimi K2 Chat |
| GPQA Diamond | 78.0 | 77.4 | 0.6 | DeepSeek-V3.2 Chat |
| SWE-bench Verified | 69.6 | 70.0 | 0.4 | Kimi K2 Chat |
| HumanEval | 87.9 | 88.1 | 0.2 | Kimi K2 Chat |
| MATH-500 | 90.2 | 84.8 | 5.4 | DeepSeek-V3.2 Chat |
| AIME 2025 | 81.0 | 84.1 | 3.1 | Kimi K2 Chat |
| LiveCodeBench | 77.9 | 74.5 | 3.4 | DeepSeek-V3.2 Chat |
| IFEval | 88.6 | 88.0 | 0.6 | DeepSeek-V3.2 Chat |
| TruthfulQA | 86.3 | 82.8 | 3.5 | DeepSeek-V3.2 Chat |
| HellaSwag | 89.2 | 91.6 | 2.4 | Kimi K2 Chat |
| MMMU | 76.0 | 79.6 | 3.6 | Kimi K2 Chat |
| Arena Elo | 1462.2 | 1445.6 | 16.6 | DeepSeek-V3.2 Chat |
Price analysis
On a blended 3:1 input-to-output basis, DeepSeek-V3.2 Chat costs about $10.96 per million tokens and Kimi K2 Chat costs about $9.86. Kimi K2 Chat is roughly 10% cheaper than DeepSeek-V3.2 Chat. At high volume, that gap compounds quickly, so price should carry real weight unless the quality difference is decisive.
DeepSeek-V3.2 Chat has the larger context window at 1,000,000 tokens, compared with 400,000 for Kimi K2 Chat. That matters for long documents, large codebases and multi-step agent traces.
Verdict
Across 12 shared benchmarks, DeepSeek-V3.2 Chat wins 6, Kimi K2 Chat wins 6, and 0 are tied. On the index average, DeepSeek-V3.2 Chat leads Kimi K2 Chat by 0.4 points (82.5 versus 82.1).
The price picture favours Kimi K2 Chat. The context picture favours DeepSeek-V3.2 Chat. 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 DeepSeek-V3.2 Chat for quality-sensitive work and Kimi K2 Chat for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.
Choose DeepSeek-V3.2 Chat if
- You want the higher average benchmark score in this index.
- You need the larger 1,000,000-token context window.
- You need the larger 8,192-token output limit.
Choose Kimi K2 Chat if
- Unit cost matters and you want the cheaper blended price.
FAQ
Is DeepSeek-V3.2 Chat better than Kimi K2 Chat?
On the index average, DeepSeek-V3.2 Chat leads Kimi K2 Chat by 0.4 points (82.5 versus 82.1). The better choice depends on price, context and your task. DeepSeek-V3.2 Chat wins 6 benchmarks, Kimi K2 Chat wins 6.
Which is cheaper, DeepSeek-V3.2 Chat or Kimi K2 Chat?
Kimi K2 Chat is cheaper on a blended 3:1 basis at about $9.86 per million tokens, versus $10.96 for DeepSeek-V3.2 Chat.
Which has the larger context window?
DeepSeek-V3.2 Chat has the larger context window at 1,000,000 tokens. Kimi K2 Chat has 400,000 tokens.
Are these models open weights?
DeepSeek-V3.2 Chat is open weights; Kimi K2 Chat is open weights.
Which should I choose for production?
If you need a single default, start with DeepSeek-V3.2 Chat for quality-sensitive work and Kimi K2 Chat for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.