DeepSeek-R1 vs Kimi K2 Instruct: which model should you choose?
We compared DeepSeek-R1 and Kimi K2 Instruct on price, context window and 12 benchmarks. DeepSeek-R1 has the stronger average benchmark score; Kimi K2 Instruct 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 Instruct
Kimi K2 Instruct is a multimodal model from Moonshot AI. It offers a 1,000,000-token context window, costs $5.80 per million input tokens and $23.99 per million output tokens, and averages 82.3 across the index's benchmark suite.
- Input
- $5.80 / 1M
- Output
- $23.99 / 1M
- Context
- 1,000,000
- Avg score
- 82.3
- Open weights
- Yes
Specification comparison
| Spec | DeepSeek-R1 | Kimi K2 Instruct | Edge |
|---|---|---|---|
| Input price / 1M | $5.83 | $5.80 | B |
| Output price / 1M | $25.23 | $23.99 | B |
| Context window | 128,000 | 1,000,000 | B |
| Max output | 4,096 | 65,536 | B |
| Average score | 82.8 | 82.3 | A |
| Arena Elo | 1454.1 | 1458.6 | B |
| Open weights | Yes | Yes | Tie |
Benchmark comparison
DeepSeek-R1 wins 6, Kimi K2 Instruct wins 6, and 0 benchmarks are tied. The table shows the modelled score for each model and the gap.
| Benchmark | DeepSeek-R1 | Kimi K2 Instruct | Gap | Winner |
|---|---|---|---|---|
| MMLU-Pro | 83.8 | 86.5 | 2.7 | Kimi K2 Instruct |
| GPQA Diamond | 77.8 | 78.4 | 0.6 | Kimi K2 Instruct |
| SWE-bench Verified | 72.2 | 75.1 | 2.9 | Kimi K2 Instruct |
| HumanEval | 86.4 | 87.3 | 0.9 | Kimi K2 Instruct |
| MATH-500 | 89.1 | 88.0 | 1.1 | DeepSeek-R1 |
| AIME 2025 | 81.1 | 77.3 | 3.8 | DeepSeek-R1 |
| LiveCodeBench | 77.4 | 74.1 | 3.3 | DeepSeek-R1 |
| IFEval | 91.0 | 87.5 | 3.5 | DeepSeek-R1 |
| TruthfulQA | 82.9 | 82.8 | 0.1 | DeepSeek-R1 |
| HellaSwag | 89.8 | 90.2 | 0.4 | Kimi K2 Instruct |
| MMMU | 79.5 | 78.3 | 1.2 | DeepSeek-R1 |
| Arena Elo | 1454.1 | 1458.6 | 4.5 | Kimi K2 Instruct |
Price analysis
On a blended 3:1 input-to-output basis, DeepSeek-R1 costs about $10.68 per million tokens and Kimi K2 Instruct costs about $10.35. Kimi K2 Instruct is roughly 3% 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 Instruct has the larger context window at 1,000,000 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 6, Kimi K2 Instruct wins 6, and 0 are tied. On the index average, DeepSeek-R1 leads Kimi K2 Instruct by 0.5 points (82.8 versus 82.3).
The price picture favours Kimi K2 Instruct. The context picture favours Kimi K2 Instruct. 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 Instruct 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 Instruct if
- Unit cost matters and you want the cheaper blended price.
- You need the larger 1,000,000-token context window.
- You need the larger 65,536-token output limit.
FAQ
Is DeepSeek-R1 better than Kimi K2 Instruct?
On the index average, DeepSeek-R1 leads Kimi K2 Instruct by 0.5 points (82.8 versus 82.3). The better choice depends on price, context and your task. DeepSeek-R1 wins 6 benchmarks, Kimi K2 Instruct wins 6.
Which is cheaper, DeepSeek-R1 or Kimi K2 Instruct?
Kimi K2 Instruct is cheaper on a blended 3:1 basis at about $10.35 per million tokens, versus $10.68 for DeepSeek-R1.
Which has the larger context window?
Kimi K2 Instruct has the larger context window at 1,000,000 tokens. DeepSeek-R1 has 128,000 tokens.
Are these models open weights?
DeepSeek-R1 is open weights; Kimi K2 Instruct 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 Instruct for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.