DeepSeek-V3.2 Base vs MiniMax-M2 Instruct: which model should you choose?
We compared DeepSeek-V3.2 Base and MiniMax-M2 Instruct on price, context window and 12 benchmarks. DeepSeek-V3.2 Base has the stronger average benchmark score; MiniMax-M2 Instruct 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 Instruct
MiniMax-M2 Instruct is a multimodal model from MiniMax. It offers a 128,000-token context window, costs $5.11 per million input tokens and $25.04 per million output tokens, and averages 79.4 across the index's benchmark suite.
- Input
- $5.11 / 1M
- Output
- $25.04 / 1M
- Context
- 128,000
- Avg score
- 79.4
- Open weights
- Yes
Specification comparison
| Spec | DeepSeek-V3.2 Base | MiniMax-M2 Instruct | Edge |
|---|---|---|---|
| Input price / 1M | $5.05 | $5.11 | A |
| Output price / 1M | $27.10 | $25.04 | B |
| Context window | 128,000 | 128,000 | Tie |
| Max output | 128,000 | 4,096 | A |
| Average score | 80.2 | 79.4 | A |
| Arena Elo | 1417.9 | 1442.2 | B |
| Open weights | Yes | Yes | Tie |
Benchmark comparison
DeepSeek-V3.2 Base wins 7, MiniMax-M2 Instruct wins 5, and 0 benchmarks are tied. The table shows the modelled score for each model and the gap.
| Benchmark | DeepSeek-V3.2 Base | MiniMax-M2 Instruct | Gap | Winner |
|---|---|---|---|---|
| MMLU-Pro | 84.3 | 82.1 | 2.2 | DeepSeek-V3.2 Base |
| GPQA Diamond | 77.1 | 73.7 | 3.4 | DeepSeek-V3.2 Base |
| SWE-bench Verified | 69.1 | 69.9 | 0.8 | MiniMax-M2 Instruct |
| HumanEval | 82.8 | 86.7 | 3.9 | MiniMax-M2 Instruct |
| MATH-500 | 83.7 | 82.1 | 1.6 | DeepSeek-V3.2 Base |
| AIME 2025 | 74.3 | 77.8 | 3.5 | MiniMax-M2 Instruct |
| LiveCodeBench | 73.1 | 71.1 | 2.0 | DeepSeek-V3.2 Base |
| IFEval | 86.9 | 87.3 | 0.4 | MiniMax-M2 Instruct |
| TruthfulQA | 83.5 | 80.0 | 3.5 | DeepSeek-V3.2 Base |
| HellaSwag | 90.2 | 88.5 | 1.7 | DeepSeek-V3.2 Base |
| MMMU | 76.7 | 74.1 | 2.6 | DeepSeek-V3.2 Base |
| Arena Elo | 1417.9 | 1442.2 | 24.3 | MiniMax-M2 Instruct |
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 Instruct costs about $10.09. MiniMax-M2 Instruct is roughly 4% 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.
Both models list a 128,000-token context window. That is enough for long documents, large codebases and multi-step agent traces.
Verdict
Across 12 shared benchmarks, DeepSeek-V3.2 Base wins 7, MiniMax-M2 Instruct wins 5, and 0 are tied. On the index average, DeepSeek-V3.2 Base leads MiniMax-M2 Instruct by 0.8 points (80.2 versus 79.4).
The price picture favours MiniMax-M2 Instruct. The context picture is effectively a tie. 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-V3.2 Base for quality-sensitive work and MiniMax-M2 Instruct for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.
Choose DeepSeek-V3.2 Base if
- You want the higher average benchmark score in this index.
- You need the larger 128,000-token output limit.
Choose MiniMax-M2 Instruct if
- Unit cost matters and you want the cheaper blended price.
FAQ
Is DeepSeek-V3.2 Base better than MiniMax-M2 Instruct?
On the index average, DeepSeek-V3.2 Base leads MiniMax-M2 Instruct by 0.8 points (80.2 versus 79.4). The better choice depends on price, context and your task. DeepSeek-V3.2 Base wins 7 benchmarks, MiniMax-M2 Instruct wins 5.
Which is cheaper, DeepSeek-V3.2 Base or MiniMax-M2 Instruct?
MiniMax-M2 Instruct is cheaper on a blended 3:1 basis at about $10.09 per million tokens, versus $10.56 for DeepSeek-V3.2 Base.
Which has the larger context window?
Both models list a 128,000-token context window.
Are these models open weights?
DeepSeek-V3.2 Base is open weights; MiniMax-M2 Instruct is open weights.
Which should I choose for production?
If you need a single default, start with DeepSeek-V3.2 Base for quality-sensitive work and MiniMax-M2 Instruct for high-volume, well-scoped tasks. Run both on a sample of your own traffic before locking in a choice.