DeepSeek-R1 Base vs MiniMax-M2 Chat: which model should you choose?
We compared DeepSeek-R1 Base and MiniMax-M2 Chat on price, context window and 12 benchmarks. MiniMax-M2 Chat has the stronger average benchmark score; MiniMax-M2 Chat 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
MiniMax-M2 Chat
MiniMax-M2 Chat is a multimodal model from MiniMax. It offers a 200,000-token context window, costs $5.02 per million input tokens and $18.95 per million output tokens, and averages 80.2 across the index's benchmark suite.
- Input
- $5.02 / 1M
- Output
- $18.95 / 1M
- Context
- 200,000
- Avg score
- 80.2
- Open weights
- Yes
Specification comparison
| Spec | DeepSeek-R1 Base | MiniMax-M2 Chat | Edge |
|---|---|---|---|
| Input price / 1M | $5.05 | $5.02 | B |
| Output price / 1M | $26.24 | $18.95 | B |
| Context window | 400,000 | 200,000 | A |
| Max output | 65,536 | 8,192 | A |
| Average score | 80.1 | 80.2 | B |
| Arena Elo | 1442.9 | 1443.0 | B |
| Open weights | Yes | Yes | Tie |
Benchmark comparison
DeepSeek-R1 Base wins 5, MiniMax-M2 Chat wins 7, and 0 benchmarks are tied. The table shows the modelled score for each model and the gap.
| Benchmark | DeepSeek-R1 Base | MiniMax-M2 Chat | Gap | Winner |
|---|---|---|---|---|
| MMLU-Pro | 81.0 | 84.3 | 3.3 | MiniMax-M2 Chat |
| GPQA Diamond | 76.2 | 77.8 | 1.6 | MiniMax-M2 Chat |
| SWE-bench Verified | 71.2 | 66.4 | 4.8 | DeepSeek-R1 Base |
| HumanEval | 83.4 | 84.2 | 0.8 | MiniMax-M2 Chat |
| MATH-500 | 84.9 | 86.0 | 1.1 | MiniMax-M2 Chat |
| AIME 2025 | 73.8 | 78.3 | 4.5 | MiniMax-M2 Chat |
| LiveCodeBench | 76.0 | 70.4 | 5.6 | DeepSeek-R1 Base |
| IFEval | 88.2 | 88.0 | 0.2 | DeepSeek-R1 Base |
| TruthfulQA | 82.2 | 80.3 | 1.9 | DeepSeek-R1 Base |
| HellaSwag | 90.0 | 87.8 | 2.2 | DeepSeek-R1 Base |
| MMMU | 74.2 | 79.2 | 5.0 | MiniMax-M2 Chat |
| Arena Elo | 1442.9 | 1443.0 | 0.1 | MiniMax-M2 Chat |
Price analysis
On a blended 3:1 input-to-output basis, DeepSeek-R1 Base costs about $10.35 per million tokens and MiniMax-M2 Chat costs about $8.50. MiniMax-M2 Chat is roughly 18% cheaper than DeepSeek-R1 Base. 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 200,000 for MiniMax-M2 Chat. That matters for long documents, large codebases and multi-step agent traces.
Verdict
Across 12 shared benchmarks, DeepSeek-R1 Base wins 5, MiniMax-M2 Chat wins 7, and 0 are tied. On the index average, MiniMax-M2 Chat leads DeepSeek-R1 Base by 0.1 points (80.2 versus 80.1).
The price picture favours MiniMax-M2 Chat. 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 MiniMax-M2 Chat for quality-sensitive work, and check whether its price holds at your expected volume before committing.
Choose DeepSeek-R1 Base if
- You need the larger 400,000-token context window.
- You need the larger 65,536-token output limit.
Choose MiniMax-M2 Chat if
- You want the higher average benchmark score in this index.
- Unit cost matters and you want the cheaper blended price.
FAQ
Is DeepSeek-R1 Base better than MiniMax-M2 Chat?
On the index average, MiniMax-M2 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 5 benchmarks, MiniMax-M2 Chat wins 7.
Which is cheaper, DeepSeek-R1 Base or MiniMax-M2 Chat?
MiniMax-M2 Chat is cheaper on a blended 3:1 basis at about $8.50 per million tokens, versus $10.35 for DeepSeek-R1 Base.
Which has the larger context window?
DeepSeek-R1 Base has the larger context window at 400,000 tokens. MiniMax-M2 Chat has 200,000 tokens.
Are these models open weights?
DeepSeek-R1 Base is open weights; MiniMax-M2 Chat is open weights.
Which should I choose for production?
If you need a single default, start with MiniMax-M2 Chat for quality-sensitive work, and check whether its price holds at your expected volume before committing.