DeepSeek-R1 Chat vs Kimi K2 Chat: which model should you choose?
We compared DeepSeek-R1 Chat and Kimi K2 Chat on price, context window and 12 benchmarks. DeepSeek-R1 Chat has the stronger average benchmark score; DeepSeek-R1 Chat is cheaper on a blended 3:1 basis.
DeepSeek-R1 Chat
DeepSeek-R1 Chat is a multimodal model from DeepSeek. It offers a 400,000-token context window, costs $5.62 per million input tokens and $21.49 per million output tokens, and averages 82.4 across the index's benchmark suite.
- Input
- $5.62 / 1M
- Output
- $21.49 / 1M
- Context
- 400,000
- Avg score
- 82.4
- 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-R1 Chat | Kimi K2 Chat | Edge |
|---|---|---|---|
| Input price / 1M | $5.62 | $5.74 | A |
| Output price / 1M | $21.49 | $22.24 | A |
| Context window | 400,000 | 400,000 | Tie |
| Max output | 128,000 | 4,096 | A |
| Average score | 82.4 | 82.1 | A |
| Arena Elo | 1433.3 | 1445.6 | B |
| Open weights | Yes | Yes | Tie |
Benchmark comparison
DeepSeek-R1 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-R1 Chat | Kimi K2 Chat | Gap | Winner |
|---|---|---|---|---|
| MMLU-Pro | 83.4 | 82.7 | 0.7 | DeepSeek-R1 Chat |
| GPQA Diamond | 80.0 | 77.4 | 2.6 | DeepSeek-R1 Chat |
| SWE-bench Verified | 71.7 | 70.0 | 1.7 | DeepSeek-R1 Chat |
| HumanEval | 84.9 | 88.1 | 3.2 | Kimi K2 Chat |
| MATH-500 | 89.1 | 84.8 | 4.3 | DeepSeek-R1 Chat |
| AIME 2025 | 78.6 | 84.1 | 5.5 | Kimi K2 Chat |
| LiveCodeBench | 79.3 | 74.5 | 4.8 | DeepSeek-R1 Chat |
| IFEval | 89.5 | 88.0 | 1.5 | DeepSeek-R1 Chat |
| TruthfulQA | 82.3 | 82.8 | 0.5 | Kimi K2 Chat |
| HellaSwag | 90.6 | 91.6 | 1.0 | Kimi K2 Chat |
| MMMU | 77.1 | 79.6 | 2.5 | Kimi K2 Chat |
| Arena Elo | 1433.3 | 1445.6 | 12.3 | Kimi K2 Chat |
Price analysis
On a blended 3:1 input-to-output basis, DeepSeek-R1 Chat costs about $9.59 per million tokens and Kimi K2 Chat costs about $9.86. DeepSeek-R1 Chat is roughly 3% cheaper than Kimi K2 Chat. At high volume, that gap compounds quickly, so price should carry real weight unless the quality difference is decisive.
Both models list a 400,000-token context window. That is enough for long documents, large codebases and multi-step agent traces.
Verdict
Across 12 shared benchmarks, DeepSeek-R1 Chat wins 6, Kimi K2 Chat wins 6, and 0 are tied. On the index average, DeepSeek-R1 Chat leads Kimi K2 Chat by 0.3 points (82.4 versus 82.1).
The price picture favours DeepSeek-R1 Chat. 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-R1 Chat for quality-sensitive work, and check whether its price holds at your expected volume before committing.
Choose DeepSeek-R1 Chat 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 128,000-token output limit.
Choose Kimi K2 Chat if
- You prefer Moonshot AI's tooling or already use it.
FAQ
Is DeepSeek-R1 Chat better than Kimi K2 Chat?
On the index average, DeepSeek-R1 Chat leads Kimi K2 Chat by 0.3 points (82.4 versus 82.1). The better choice depends on price, context and your task. DeepSeek-R1 Chat wins 6 benchmarks, Kimi K2 Chat wins 6.
Which is cheaper, DeepSeek-R1 Chat or Kimi K2 Chat?
DeepSeek-R1 Chat is cheaper on a blended 3:1 basis at about $9.59 per million tokens, versus $9.86 for Kimi K2 Chat.
Which has the larger context window?
Both models list a 400,000-token context window.
Are these models open weights?
DeepSeek-R1 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-R1 Chat for quality-sensitive work, and check whether its price holds at your expected volume before committing.