Model comparison
DeepSeek-V3.1 vs MiniMax-M1 (M1-40k / M1-80k)
- Input
- $0.21 / 1M tokens
- Output
- $0.79 / 1M tokens
- Context
- 128K
- Released
- 2025-08-21
- Input
- $0.40 / 1M tokens (0-200k context); $1.30 / 1M tokens (200k-1M context)
- Output
- $2.20 / 1M tokens
- Context
- 1M
- Released
- 2025-06-16
DeepSeek-V3.1 scores higher on 6 of the 6 benchmarks both models report; DeepSeek-V3.1 is cheaper on input tokens ($0.2 vs $0.4 per 1M, 1.9×); MiniMax-M1 (M1-40k / M1-80k) has the larger context window (1M vs 128K).
Specs and pricing side by side
| Spec | DeepSeek-V3.1 | MiniMax-M1 (M1-40k / M1-80k) |
|---|---|---|
| Maker | DeepSeek | MiniMax |
| Released | 2025-08-21 | 2025-06-16 |
| Context window | 128K | 1M |
| Max output | not published | 80k tokens (M1-80k); 40k tokens (M1-40k) |
| Input price | $0.21 / 1M tokens | $0.40 / 1M tokens (0-200k context); $1.30 / 1M tokens (200k-1M context) |
| Output price | $0.79 / 1M tokens | $2.20 / 1M tokens |
| Parameters | 671B total / 37B active (MoE) | 456B total / 45.9B active (MoE, 32 experts) |
| License | MIT | Apache 2.0 |
| Weights | Open weights | Open weightssame |
| Modalities | Text | Textsame |
| Knowledge cutoff | not published | June 2024 |
Benchmarks both models report
| Benchmark | DeepSeek-V3.1 | MiniMax-M1 (M1-40k / M1-80k) |
|---|---|---|
| MMLU-Pro | 84.8% | 81.1% |
| GPQA-Diamond | 80.1% | 70% |
| AIME 2025 | 88.4% | 76.9% |
| AIME 2024 | 93.1% | 86% |
| LiveCodeBench | 74.8% | 65% |
| SWE-bench Verified | 66% | 56% |
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 DeepSeek-V3.1 if…
- You care about MMLU-Pro, GPQA-Diamond, AIME 2025 (it scores higher on 6 shared benchmarks).
- Cost matters: input tokens cost 1.9× less ($0.2 vs $0.4 per 1M).
- Your use case is: coding agents and software-engineering tasks (SWE-bench-style fix/patch workflows).
Pick MiniMax-M1 (M1-40k / M1-80k) if…
- You need the longer context (1M vs 128K).
- Your use case is: long-document and whole-codebase analysis that needs hundreds of thousands of tokens of context.
FAQ
Which is better, DeepSeek-V3.1 or MiniMax-M1 (M1-40k / M1-80k)?
DeepSeek-V3.1 scores higher on 6 of the 6 benchmarks both models report; DeepSeek-V3.1 is cheaper on input tokens ($0.2 vs $0.4 per 1M, 1.9×); MiniMax-M1 (M1-40k / M1-80k) has the larger context window (1M 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?
DeepSeek-V3.1: $0.21 / 1M tokens input, $0.79 / 1M tokens output. MiniMax-M1 (M1-40k / M1-80k): $0.40 / 1M tokens (0-200k context); $1.30 / 1M tokens (200k-1M context) input, $2.20 / 1M tokens output (list API prices as published by each maker).
What are the context windows of DeepSeek-V3.1 and MiniMax-M1 (M1-40k / M1-80k)?
DeepSeek-V3.1 offers 128K; MiniMax-M1 (M1-40k / M1-80k) offers 1M with up to 80k tokens (M1-80k); 40k tokens (M1-40k) output.
Is DeepSeek-V3.1 newer than MiniMax-M1 (M1-40k / M1-80k)?
DeepSeek-V3.1 was released 2025-08-21; MiniMax-M1 (M1-40k / M1-80k) was released 2025-06-16.
Go deeper
- DeepSeek-V3.1: full specs, pricing and benchmarks
- MiniMax-M1 (M1-40k / M1-80k): full specs, pricing and benchmarks
- deepseek v3 1 terminus vs deepseek v3 1
- kimi k2 thinking vs deepseek v3 1
- glm 4 5 air vs minimax m1
- minimax m1 vs deepseek r1 0528
- minimax m2 1 vs deepseek v3 1
- minimax m1 vs deepseek v3
- All model comparisons