DeepSeek-V3.2 Base vs MiniMax-M2 Chat: which model should you choose?
We compared DeepSeek-V3.2 Base and MiniMax-M2 Chat on price, context window and 12 benchmarks. DeepSeek-V3.2 Base and MiniMax-M2 Chat are effectively tied on the index average; MiniMax-M2 Chat is cheaper on a blended 3:1 basis.
DeepSeek-V3.2 Base
DeepSeek-V3.2 Base is a multimodal model from DeepSeek. It offers a 128,000-token context window, costs $5.05 per million input tokens and $27.10 per million output tokens, and averages 80.2 across the index's benchmark suite.
- Input
- $5.05 / 1M
- Output
- $27.10 / 1M
- Context
- 128,000
- Avg score
- 80.2
- 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-V3.2 Base | MiniMax-M2 Chat | Edge |
|---|---|---|---|
| Input price / 1M | $5.05 | $5.02 | B |
| Output price / 1M | $27.10 | $18.95 | B |
| Context window | 128,000 | 200,000 | B |
| Max output | 128,000 | 8,192 | A |
| Average score | 80.2 | 80.2 | Tie |
| Arena Elo | 1417.9 | 1443.0 | B |
| Open weights | Yes | Yes | Tie |
Benchmark comparison
DeepSeek-V3.2 Base wins 4, MiniMax-M2 Chat wins 7, and 1 benchmarks are tied. The table shows the modelled score for each model and the gap.
| Benchmark | DeepSeek-V3.2 Base | MiniMax-M2 Chat | Gap | Winner |
|---|---|---|---|---|
| MMLU-Pro | 84.3 | 84.3 | 0.0 | Tie |
| GPQA Diamond | 77.1 | 77.8 | 0.7 | MiniMax-M2 Chat |
| SWE-bench Verified | 69.1 | 66.4 | 2.7 | DeepSeek-V3.2 Base |
| HumanEval | 82.8 | 84.2 | 1.4 | MiniMax-M2 Chat |
| MATH-500 | 83.7 | 86.0 | 2.3 | MiniMax-M2 Chat |
| AIME 2025 | 74.3 | 78.3 | 4.0 | MiniMax-M2 Chat |
| LiveCodeBench | 73.1 | 70.4 | 2.7 | DeepSeek-V3.2 Base |
| IFEval | 86.9 | 88.0 | 1.1 | MiniMax-M2 Chat |
| TruthfulQA | 83.5 | 80.3 | 3.2 | DeepSeek-V3.2 Base |
| HellaSwag | 90.2 | 87.8 | 2.4 | DeepSeek-V3.2 Base |
| MMMU | 76.7 | 79.2 | 2.5 | MiniMax-M2 Chat |
| Arena Elo | 1417.9 | 1443.0 | 25.1 | MiniMax-M2 Chat |
Price analysis
On a blended 3:1 input-to-output basis, DeepSeek-V3.2 Base costs about $10.56 per million tokens and MiniMax-M2 Chat costs about $8.50. MiniMax-M2 Chat is roughly 20% cheaper than DeepSeek-V3.2 Base. At high volume, that gap compounds quickly, so price should carry real weight unless the quality difference is decisive.
MiniMax-M2 Chat has the larger context window at 200,000 tokens, compared with 128,000 for DeepSeek-V3.2 Base. That matters for long documents, large codebases and multi-step agent traces.
Verdict
Across 12 shared benchmarks, DeepSeek-V3.2 Base wins 4, MiniMax-M2 Chat wins 7, and 1 are tied. The two models are effectively tied on the index average at 80.2.
The price picture favours MiniMax-M2 Chat. The context picture favours MiniMax-M2 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 MiniMax-M2 Chat; it is cheaper on a blended basis and has the larger context window. Test it on your own traffic before committing.
Choose DeepSeek-V3.2 Base if
- You need the larger 128,000-token output limit.
Choose MiniMax-M2 Chat if
- Unit cost matters and you want the cheaper blended price.
- You need the larger 200,000-token context window.
FAQ
Is DeepSeek-V3.2 Base better than MiniMax-M2 Chat?
The two models are effectively tied on the index average at 80.2. The better choice depends on price, context and your task. DeepSeek-V3.2 Base wins 4 benchmarks, MiniMax-M2 Chat wins 7.
Which is cheaper, DeepSeek-V3.2 Base or MiniMax-M2 Chat?
MiniMax-M2 Chat is cheaper on a blended 3:1 basis at about $8.50 per million tokens, versus $10.56 for DeepSeek-V3.2 Base.
Which has the larger context window?
MiniMax-M2 Chat has the larger context window at 200,000 tokens. DeepSeek-V3.2 Base has 128,000 tokens.
Are these models open weights?
DeepSeek-V3.2 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; it is cheaper on a blended basis and has the larger context window. Test it on your own traffic before committing.