Model comparison
MiniMax M2.1 vs DeepSeek-V3.1
- Input
- $0.30 / 1M tokens
- Output
- $1.20 / 1M tokens
- Context
- 205K
- Released
- 2025-12-23
- Input
- $0.21 / 1M tokens
- Output
- $0.79 / 1M tokens
- Context
- 128K
- Released
- 2025-08-21
MiniMax M2.1 scores higher on 5 of the 6 benchmarks both models report (the other leads on 1); DeepSeek-V3.1 is cheaper on input tokens ($0.2 vs $0.3 per 1M, 1.4×); MiniMax M2.1 has the larger context window (205K vs 128K).
Specs and pricing side by side
| Spec | MiniMax M2.1 | DeepSeek-V3.1 |
|---|---|---|
| Maker | MiniMax | DeepSeek |
| Released | 2025-12-23 | 2025-08-21 |
| Context window | 205K | 128K |
| Max output | 196,608 tokens | not published |
| Input price | $0.30 / 1M tokens | $0.21 / 1M tokens |
| Output price | $1.20 / 1M tokens | $0.79 / 1M tokens |
| Parameters | 230B total / 10B active (MoE) | 671B total / 37B active (MoE) |
| License | Modified MIT | MIT |
| Weights | Open weights | Open weightssame |
| Modalities | Text | Textsame |
Not published by either maker: knowledge cutoff.
Benchmarks both models report
| Benchmark | MiniMax M2.1 | DeepSeek-V3.1 |
|---|---|---|
| SWE-bench Verified | 74% | 66% |
| SWE-bench Multilingual | 72.5% | 54.5% |
| BrowseComp | 47.4% | 30% |
| AIME 2025 | 83% | 88.4% |
| MMLU-Pro | 88% | 84.8% |
| GPQA Diamond | 83% | 80.1% |
Scores are as published by each maker or its cited source; harness, reasoning effort and date can differ between rows, so treat small gaps as ties. Sources are listed on each model page.
Which one should you pick?
Pick MiniMax M2.1 if…
- You care about SWE-bench Verified, SWE-bench Multilingual, BrowseComp (it scores higher on 5 shared benchmarks).
- You need the longer context (205K vs 128K).
- Your use case is: autonomous and assisted software engineering across many programming languages.
Pick DeepSeek-V3.1 if…
- You care about AIME 2025 (it scores higher on 1 shared benchmark).
- Cost matters: input tokens cost 1.4× less ($0.2 vs $0.3 per 1M).
- Your use case is: coding agents and software-engineering tasks (SWE-bench-style fix/patch workflows).
FAQ
Which is better, MiniMax M2.1 or DeepSeek-V3.1?
MiniMax M2.1 scores higher on 5 of the 6 benchmarks both models report (the other leads on 1); DeepSeek-V3.1 is cheaper on input tokens ($0.2 vs $0.3 per 1M, 1.4×); MiniMax M2.1 has the larger context window (205K vs 128K). The better pick depends on whether you value benchmark scores, price per token, context length or openness for your workload.
How much cheaper is DeepSeek-V3.1?
MiniMax M2.1: $0.30 / 1M tokens input, $1.20 / 1M tokens output. DeepSeek-V3.1: $0.21 / 1M tokens input, $0.79 / 1M tokens output (list API prices as published by each maker).
What are the context windows of MiniMax M2.1 and DeepSeek-V3.1?
MiniMax M2.1 offers 205K with up to 196,608 tokens output; DeepSeek-V3.1 offers 128K.
Is MiniMax M2.1 newer than DeepSeek-V3.1?
MiniMax M2.1 was released 2025-12-23; DeepSeek-V3.1 was released 2025-08-21.