DeepSeek-V3.2 Instruct vs Kimi K2: which model should you choose?
We compared DeepSeek-V3.2 Instruct and Kimi K2 on price, context window and 12 benchmarks. DeepSeek-V3.2 Instruct has the stronger average benchmark score; Kimi K2 is cheaper on a blended 3:1 basis.
DeepSeek-V3.2 Instruct
DeepSeek-V3.2 Instruct is a frontier model from DeepSeek. It offers a 128,000-token context window, costs $5.91 per million input tokens and $29.01 per million output tokens, and averages 83.7 across the index's benchmark suite.
- Input
- $5.91 / 1M
- Output
- $29.01 / 1M
- Context
- 128,000
- Avg score
- 83.7
- Open weights
- Yes
Kimi K2
Kimi K2 is a multimodal model from Moonshot AI. It offers a 128,000-token context window, costs $5.85 per million input tokens and $21.72 per million output tokens, and averages 83.0 across the index's benchmark suite.
- Input
- $5.85 / 1M
- Output
- $21.72 / 1M
- Context
- 128,000
- Avg score
- 83.0
- Open weights
- Yes
Specification comparison
| Spec | DeepSeek-V3.2 Instruct | Kimi K2 | Edge |
|---|---|---|---|
| Input price / 1M | $5.91 | $5.85 | B |
| Output price / 1M | $29.01 | $21.72 | B |
| Context window | 128,000 | 128,000 | Tie |
| Max output | 4,096 | 65,536 | B |
| Average score | 83.7 | 83.0 | A |
| Arena Elo | 1454.4 | 1458.5 | B |
| Open weights | Yes | Yes | Tie |
Benchmark comparison
DeepSeek-V3.2 Instruct wins 6, Kimi K2 wins 6, and 0 benchmarks are tied. The table shows the modelled score for each model and the gap.
| Benchmark | DeepSeek-V3.2 Instruct | Kimi K2 | Gap | Winner |
|---|---|---|---|---|
| MMLU-Pro | 84.6 | 83.3 | 1.3 | DeepSeek-V3.2 Instruct |
| GPQA Diamond | 80.9 | 77.5 | 3.4 | DeepSeek-V3.2 Instruct |
| SWE-bench Verified | 75.2 | 70.2 | 5.0 | DeepSeek-V3.2 Instruct |
| HumanEval | 90.2 | 87.1 | 3.1 | DeepSeek-V3.2 Instruct |
| MATH-500 | 86.3 | 85.3 | 1.0 | DeepSeek-V3.2 Instruct |
| AIME 2025 | 83.9 | 85.1 | 1.2 | Kimi K2 |
| LiveCodeBench | 75.2 | 78.5 | 3.3 | Kimi K2 |
| IFEval | 91.3 | 91.5 | 0.2 | Kimi K2 |
| TruthfulQA | 84.3 | 84.8 | 0.5 | Kimi K2 |
| HellaSwag | 88.8 | 92.3 | 3.5 | Kimi K2 |
| MMMU | 80.4 | 77.5 | 2.9 | DeepSeek-V3.2 Instruct |
| Arena Elo | 1454.4 | 1458.5 | 4.1 | Kimi K2 |
Price analysis
On a blended 3:1 input-to-output basis, DeepSeek-V3.2 Instruct costs about $11.69 per million tokens and Kimi K2 costs about $9.82. Kimi K2 is roughly 16% cheaper than DeepSeek-V3.2 Instruct. 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, DeepSeek-V3.2 Instruct wins 6, Kimi K2 wins 6, and 0 are tied. On the index average, DeepSeek-V3.2 Instruct leads Kimi K2 by 0.7 points (83.7 versus 83.0).
The price picture favours Kimi K2. 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 DeepSeek-V3.2 Instruct for quality-sensitive work and Kimi K2 for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.
Choose DeepSeek-V3.2 Instruct if
- You want the higher average benchmark score in this index.
Choose Kimi K2 if
- Unit cost matters and you want the cheaper blended price.
- You need the larger 65,536-token output limit.
FAQ
Is DeepSeek-V3.2 Instruct better than Kimi K2?
On the index average, DeepSeek-V3.2 Instruct leads Kimi K2 by 0.7 points (83.7 versus 83.0). The better choice depends on price, context and your task. DeepSeek-V3.2 Instruct wins 6 benchmarks, Kimi K2 wins 6.
Which is cheaper, DeepSeek-V3.2 Instruct or Kimi K2?
Kimi K2 is cheaper on a blended 3:1 basis at about $9.82 per million tokens, versus $11.69 for DeepSeek-V3.2 Instruct.
Which has the larger context window?
Both models list a 128,000-token context window.
Are these models open weights?
DeepSeek-V3.2 Instruct is open weights; Kimi K2 is open weights.
Which should I choose for production?
If you need a single default, start with DeepSeek-V3.2 Instruct for quality-sensitive work and Kimi K2 for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.