DeepSeek-R1 vs Kimi K2: which model should you choose?
We compared DeepSeek-R1 and Kimi K2 on price, context window and 12 benchmarks. Kimi K2 has the stronger average benchmark score; Kimi K2 is cheaper on a blended 3:1 basis.
DeepSeek-R1
DeepSeek-R1 is a multimodal model from DeepSeek. It offers a 128,000-token context window, costs $5.83 per million input tokens and $25.23 per million output tokens, and averages 82.8 across the index's benchmark suite.
- Input
- $5.83 / 1M
- Output
- $25.23 / 1M
- Context
- 128,000
- Avg score
- 82.8
- 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-R1 | Kimi K2 | Edge |
|---|---|---|---|
| Input price / 1M | $5.83 | $5.85 | A |
| Output price / 1M | $25.23 | $21.72 | B |
| Context window | 128,000 | 128,000 | Tie |
| Max output | 4,096 | 65,536 | B |
| Average score | 82.8 | 83.0 | B |
| Arena Elo | 1454.1 | 1458.5 | B |
| Open weights | Yes | Yes | Tie |
Benchmark comparison
DeepSeek-R1 wins 5, Kimi K2 wins 7, and 0 benchmarks are tied. The table shows the modelled score for each model and the gap.
| Benchmark | DeepSeek-R1 | Kimi K2 | Gap | Winner |
|---|---|---|---|---|
| MMLU-Pro | 83.8 | 83.3 | 0.5 | DeepSeek-R1 |
| GPQA Diamond | 77.8 | 77.5 | 0.3 | DeepSeek-R1 |
| SWE-bench Verified | 72.2 | 70.2 | 2.0 | DeepSeek-R1 |
| HumanEval | 86.4 | 87.1 | 0.7 | Kimi K2 |
| MATH-500 | 89.1 | 85.3 | 3.8 | DeepSeek-R1 |
| AIME 2025 | 81.1 | 85.1 | 4.0 | Kimi K2 |
| LiveCodeBench | 77.4 | 78.5 | 1.1 | Kimi K2 |
| IFEval | 91.0 | 91.5 | 0.5 | Kimi K2 |
| TruthfulQA | 82.9 | 84.8 | 1.9 | Kimi K2 |
| HellaSwag | 89.8 | 92.3 | 2.5 | Kimi K2 |
| MMMU | 79.5 | 77.5 | 2.0 | DeepSeek-R1 |
| Arena Elo | 1454.1 | 1458.5 | 4.4 | Kimi K2 |
Price analysis
On a blended 3:1 input-to-output basis, DeepSeek-R1 costs about $10.68 per million tokens and Kimi K2 costs about $9.82. Kimi K2 is roughly 8% cheaper than DeepSeek-R1. 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-R1 wins 5, Kimi K2 wins 7, and 0 are tied. On the index average, Kimi K2 leads DeepSeek-R1 by 0.2 points (83.0 versus 82.8).
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 Kimi K2 for quality-sensitive work, and check whether its price holds at your expected volume before committing.
Choose DeepSeek-R1 if
- You prefer DeepSeek's tooling or already use it.
Choose Kimi K2 if
- You want the higher average benchmark score in this index.
- Unit cost matters and you want the cheaper blended price.
- You need the larger 65,536-token output limit.
FAQ
Is DeepSeek-R1 better than Kimi K2?
On the index average, Kimi K2 leads DeepSeek-R1 by 0.2 points (83.0 versus 82.8). The better choice depends on price, context and your task. DeepSeek-R1 wins 5 benchmarks, Kimi K2 wins 7.
Which is cheaper, DeepSeek-R1 or Kimi K2?
Kimi K2 is cheaper on a blended 3:1 basis at about $9.82 per million tokens, versus $10.68 for DeepSeek-R1.
Which has the larger context window?
Both models list a 128,000-token context window.
Are these models open weights?
DeepSeek-R1 is open weights; Kimi K2 is open weights.
Which should I choose for production?
If you need a single default, start with Kimi K2 for quality-sensitive work, and check whether its price holds at your expected volume before committing.