Model comparison
MiniMax-M1 (M1-40k / M1-80k) vs DeepSeek-V3
- 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
- Input
- $0.27 / 1M tokens (cache miss)
- Output
- $1.10 / 1M tokens
- Context
- 128K tokens
- Released
- 2024-12-26
MiniMax-M1 (M1-40k / M1-80k) scores higher on 5 of the 5 benchmarks both models report; DeepSeek-V3 is cheaper on input tokens ($0.3 vs $0.4 per 1M, 1.5×); MiniMax-M1 (M1-40k / M1-80k) has the larger context window (1M vs 128K).
Specs and pricing side by side
| Spec | MiniMax-M1 (M1-40k / M1-80k) | DeepSeek-V3 |
|---|---|---|
| Maker | MiniMax | DeepSeek |
| Released | 2025-06-16 | 2024-12-26 |
| Context window | 1M | 128K tokens |
| Max output | 80k tokens (M1-80k); 40k tokens (M1-40k) | not published |
| Input price | $0.40 / 1M tokens (0-200k context); $1.30 / 1M tokens (200k-1M context) | $0.27 / 1M tokens (cache miss) |
| Output price | $2.20 / 1M tokens | $1.10 / 1M tokens |
| Parameters | 456B total / 45.9B active (MoE, 32 experts) | 671B total / 37B active per token (Mixture-of-Experts) |
| License | Apache 2.0 | DeepSeek Model License (commercial use permitted); code under MIT |
| Weights | Open weights | Open weightssame |
| Modalities | Text | text |
| Knowledge cutoff | June 2024 | not published |
Benchmarks both models report
| Benchmark | MiniMax-M1 (M1-40k / M1-80k) | DeepSeek-V3 |
|---|---|---|
| AIME 2024 | 86% | 39.2% |
| LiveCodeBench | 65% | 37.6% |
| SWE-bench Verified | 56% | 42% |
| MMLU-Pro | 81.1% | 75.9% |
| GPQA Diamond | 70% | 59.1% |
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-M1 (M1-40k / M1-80k) if…
- You care about AIME 2024, LiveCodeBench, SWE-bench Verified (it scores higher on 5 shared benchmarks).
- You need the longer context (1M vs 128K).
- You need Text input, which DeepSeek-V3 lacks.
- Your use case is: long-document and whole-codebase analysis that needs hundreds of thousands of tokens of context.
Pick DeepSeek-V3 if…
- Cost matters: input tokens cost 1.5× less ($0.3 vs $0.4 per 1M).
- You need text input, which MiniMax-M1 (M1-40k / M1-80k) lacks.
- Your use case is: general-purpose chat and instruction following at low cost.
FAQ
Which is better, MiniMax-M1 (M1-40k / M1-80k) or DeepSeek-V3?
MiniMax-M1 (M1-40k / M1-80k) scores higher on 5 of the 5 benchmarks both models report; DeepSeek-V3 is cheaper on input tokens ($0.3 vs $0.4 per 1M, 1.5×); 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?
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. DeepSeek-V3: $0.27 / 1M tokens (cache miss) input, $1.10 / 1M tokens output (list API prices as published by each maker).
What are the context windows of MiniMax-M1 (M1-40k / M1-80k) and DeepSeek-V3?
MiniMax-M1 (M1-40k / M1-80k) offers 1M with up to 80k tokens (M1-80k); 40k tokens (M1-40k) output; DeepSeek-V3 offers 128K tokens.
Is MiniMax-M1 (M1-40k / M1-80k) newer than DeepSeek-V3?
MiniMax-M1 (M1-40k / M1-80k) was released 2025-06-16; DeepSeek-V3 was released 2024-12-26.