DeepSeek-R1 Base vs Kimi K1.5 Chat: which model should you choose?
We compared DeepSeek-R1 Base and Kimi K1.5 Chat on price, context window and 12 benchmarks. Kimi K1.5 Chat has the stronger average benchmark score; DeepSeek-R1 Base is cheaper on a blended 3:1 basis.
DeepSeek-R1 Base
DeepSeek-R1 Base is a multimodal model from DeepSeek. It offers a 400,000-token context window, costs $5.05 per million input tokens and $26.24 per million output tokens, and averages 80.1 across the index's benchmark suite.
- Input
- $5.05 / 1M
- Output
- $26.24 / 1M
- Context
- 400,000
- Avg score
- 80.1
- Open weights
- Yes
Kimi K1.5 Chat
Kimi K1.5 Chat is a multimodal model from Moonshot AI. It offers a 128,000-token context window, costs $5.11 per million input tokens and $26.59 per million output tokens, and averages 80.2 across the index's benchmark suite.
- Input
- $5.11 / 1M
- Output
- $26.59 / 1M
- Context
- 128,000
- Avg score
- 80.2
- Open weights
- Yes
Specification comparison
| Spec | DeepSeek-R1 Base | Kimi K1.5 Chat | Edge |
|---|---|---|---|
| Input price / 1M | $5.05 | $5.11 | A |
| Output price / 1M | $26.24 | $26.59 | A |
| Context window | 400,000 | 128,000 | A |
| Max output | 65,536 | 32,768 | A |
| Average score | 80.1 | 80.2 | B |
| Arena Elo | 1442.9 | 1422.6 | A |
| Open weights | Yes | Yes | Tie |
Benchmark comparison
DeepSeek-R1 Base wins 7, Kimi K1.5 Chat wins 4, and 1 benchmarks are tied. The table shows the modelled score for each model and the gap.
| Benchmark | DeepSeek-R1 Base | Kimi K1.5 Chat | Gap | Winner |
|---|---|---|---|---|
| MMLU-Pro | 81.0 | 81.3 | 0.3 | Kimi K1.5 Chat |
| GPQA Diamond | 76.2 | 78.1 | 1.9 | Kimi K1.5 Chat |
| SWE-bench Verified | 71.2 | 69.9 | 1.3 | DeepSeek-R1 Base |
| HumanEval | 83.4 | 84.3 | 0.9 | Kimi K1.5 Chat |
| MATH-500 | 84.9 | 81.7 | 3.2 | DeepSeek-R1 Base |
| AIME 2025 | 73.8 | 81.4 | 7.6 | Kimi K1.5 Chat |
| LiveCodeBench | 76.0 | 75.7 | 0.3 | DeepSeek-R1 Base |
| IFEval | 88.2 | 88.2 | 0.0 | Tie |
| TruthfulQA | 82.2 | 80.3 | 1.9 | DeepSeek-R1 Base |
| HellaSwag | 90.0 | 87.2 | 2.8 | DeepSeek-R1 Base |
| MMMU | 74.2 | 73.6 | 0.6 | DeepSeek-R1 Base |
| Arena Elo | 1442.9 | 1422.6 | 20.3 | DeepSeek-R1 Base |
Price analysis
On a blended 3:1 input-to-output basis, DeepSeek-R1 Base costs about $10.35 per million tokens and Kimi K1.5 Chat costs about $10.48. DeepSeek-R1 Base is roughly 1% cheaper than Kimi K1.5 Chat. At high volume, that gap compounds quickly, so price should carry real weight unless the quality difference is decisive.
DeepSeek-R1 Base has the larger context window at 400,000 tokens, compared with 128,000 for Kimi K1.5 Chat. That matters for long documents, large codebases and multi-step agent traces.
Verdict
Across 12 shared benchmarks, DeepSeek-R1 Base wins 7, Kimi K1.5 Chat wins 4, and 1 are tied. On the index average, Kimi K1.5 Chat leads DeepSeek-R1 Base by 0.1 points (80.2 versus 80.1).
The price picture favours DeepSeek-R1 Base. The context picture favours DeepSeek-R1 Base. 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 K1.5 Chat for quality-sensitive work and DeepSeek-R1 Base for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.
Choose DeepSeek-R1 Base if
- Unit cost matters and you want the cheaper blended price.
- You need the larger 400,000-token context window.
- You need the larger 65,536-token output limit.
Choose Kimi K1.5 Chat if
- You want the higher average benchmark score in this index.
FAQ
Is DeepSeek-R1 Base better than Kimi K1.5 Chat?
On the index average, Kimi K1.5 Chat leads DeepSeek-R1 Base by 0.1 points (80.2 versus 80.1). The better choice depends on price, context and your task. DeepSeek-R1 Base wins 7 benchmarks, Kimi K1.5 Chat wins 4.
Which is cheaper, DeepSeek-R1 Base or Kimi K1.5 Chat?
DeepSeek-R1 Base is cheaper on a blended 3:1 basis at about $10.35 per million tokens, versus $10.48 for Kimi K1.5 Chat.
Which has the larger context window?
DeepSeek-R1 Base has the larger context window at 400,000 tokens. Kimi K1.5 Chat has 128,000 tokens.
Are these models open weights?
DeepSeek-R1 Base is open weights; Kimi K1.5 Chat is open weights.
Which should I choose for production?
If you need a single default, start with Kimi K1.5 Chat for quality-sensitive work and DeepSeek-R1 Base for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.