DeepSeek-R1 vs Kimi K2 Chat: which model should you choose?
We compared DeepSeek-R1 and Kimi K2 Chat on price, context window and 12 benchmarks. DeepSeek-R1 has the stronger average benchmark score; Kimi K2 Chat 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 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 | Kimi K2 Chat | Edge |
|---|---|---|---|
| Input price / 1M | $5.83 | $5.74 | B |
| Output price / 1M | $25.23 | $22.24 | B |
| Context window | 128,000 | 400,000 | B |
| Max output | 4,096 | 4,096 | Tie |
| Average score | 82.8 | 82.1 | A |
| Arena Elo | 1454.1 | 1445.6 | A |
| Open weights | Yes | Yes | Tie |
Benchmark comparison
DeepSeek-R1 wins 8, Kimi K2 Chat wins 4, and 0 benchmarks are tied. The table shows the modelled score for each model and the gap.
| Benchmark | DeepSeek-R1 | Kimi K2 Chat | Gap | Winner |
|---|---|---|---|---|
| MMLU-Pro | 83.8 | 82.7 | 1.1 | DeepSeek-R1 |
| GPQA Diamond | 77.8 | 77.4 | 0.4 | DeepSeek-R1 |
| SWE-bench Verified | 72.2 | 70.0 | 2.2 | DeepSeek-R1 |
| HumanEval | 86.4 | 88.1 | 1.7 | Kimi K2 Chat |
| MATH-500 | 89.1 | 84.8 | 4.3 | DeepSeek-R1 |
| AIME 2025 | 81.1 | 84.1 | 3.0 | Kimi K2 Chat |
| LiveCodeBench | 77.4 | 74.5 | 2.9 | DeepSeek-R1 |
| IFEval | 91.0 | 88.0 | 3.0 | DeepSeek-R1 |
| TruthfulQA | 82.9 | 82.8 | 0.1 | DeepSeek-R1 |
| HellaSwag | 89.8 | 91.6 | 1.8 | Kimi K2 Chat |
| MMMU | 79.5 | 79.6 | 0.1 | Kimi K2 Chat |
| Arena Elo | 1454.1 | 1445.6 | 8.5 | DeepSeek-R1 |
Price analysis
On a blended 3:1 input-to-output basis, DeepSeek-R1 costs about $10.68 per million tokens and Kimi K2 Chat costs about $9.86. Kimi K2 Chat 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.
Kimi K2 Chat has the larger context window at 400,000 tokens, compared with 128,000 for DeepSeek-R1. That matters for long documents, large codebases and multi-step agent traces.
Verdict
Across 12 shared benchmarks, DeepSeek-R1 wins 8, Kimi K2 Chat wins 4, and 0 are tied. On the index average, DeepSeek-R1 leads Kimi K2 Chat by 0.7 points (82.8 versus 82.1).
The price picture favours Kimi K2 Chat. The context picture favours Kimi K2 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-R1 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-R1 if
- You want the higher average benchmark score in this index.
Choose Kimi K2 Chat if
- Unit cost matters and you want the cheaper blended price.
- You need the larger 400,000-token context window.
FAQ
Is DeepSeek-R1 better than Kimi K2 Chat?
On the index average, DeepSeek-R1 leads Kimi K2 Chat by 0.7 points (82.8 versus 82.1). The better choice depends on price, context and your task. DeepSeek-R1 wins 8 benchmarks, Kimi K2 Chat wins 4.
Which is cheaper, DeepSeek-R1 or Kimi K2 Chat?
Kimi K2 Chat is cheaper on a blended 3:1 basis at about $9.86 per million tokens, versus $10.68 for DeepSeek-R1.
Which has the larger context window?
Kimi K2 Chat has the larger context window at 400,000 tokens. DeepSeek-R1 has 128,000 tokens.
Are these models open weights?
DeepSeek-R1 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 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.