Model comparison
MiniMax-M1 (M1-40k / M1-80k) vs DeepSeek-R1-0528
- 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
- $0.50 / 1M tokens
- Output
- $2.15 / 1M tokens
- Context
- 164K
- Released
- 2025-05-28
DeepSeek-R1-0528 scores higher on 5 of the 5 benchmarks both models report; MiniMax-M1 (M1-40k / M1-80k) is cheaper on input tokens ($0.4 vs $0.5 per 1M, 1.3×); MiniMax-M1 (M1-40k / M1-80k) has the larger context window (1M vs 164K).
Specs and pricing side by side
| Spec | MiniMax-M1 (M1-40k / M1-80k) | DeepSeek-R1-0528 |
|---|---|---|
| Maker | MiniMax | DeepSeek |
| Released | 2025-06-16 | 2025-05-28 |
| Context window | 1M | 164K |
| Max output | 80k tokens (M1-80k); 40k tokens (M1-40k) | 32K tokens (64K max generation length) |
| Input price | $0.40 / 1M tokens (0-200k context); $1.30 / 1M tokens (200k-1M context) | $0.50 / 1M tokens |
| Output price | $2.20 / 1M tokens | $2.15 / 1M tokens |
| Parameters | 456B total / 45.9B active (MoE, 32 experts) | 671B total / 37B active (685B on Hugging Face incl. MTP module) |
| License | Apache 2.0 | MIT |
| Weights | Open weights | Open weightssame |
| Modalities | Text | Textsame |
| Knowledge cutoff | June 2024 | Not officially disclosed |
Benchmarks both models report
| Benchmark | MiniMax-M1 (M1-40k / M1-80k) | DeepSeek-R1-0528 |
|---|---|---|
| AIME 2024 | 86% | 91.4% |
| AIME 2025 | 76.9% | 87.5% |
| LiveCodeBench | 65% | 73.3% |
| MMLU-Pro | 81.1% | 85% |
| GPQA Diamond | 70% | 81% |
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…
- Cost matters: input tokens cost 1.3× less ($0.4 vs $0.5 per 1M).
- You need the longer context (1M vs 164K).
- Your use case is: long-document and whole-codebase analysis that needs hundreds of thousands of tokens of context.
Pick DeepSeek-R1-0528 if…
- You care about AIME 2024, AIME 2025, LiveCodeBench (it scores higher on 5 shared benchmarks).
- Your use case is: competition-style math and multi-step logical reasoning.
FAQ
Which is better, MiniMax-M1 (M1-40k / M1-80k) or DeepSeek-R1-0528?
DeepSeek-R1-0528 scores higher on 5 of the 5 benchmarks both models report; MiniMax-M1 (M1-40k / M1-80k) is cheaper on input tokens ($0.4 vs $0.5 per 1M, 1.3×); MiniMax-M1 (M1-40k / M1-80k) has the larger context window (1M vs 164K). The better pick depends on whether you value benchmark scores, price per token, context length or openness for your workload.
How much cheaper is MiniMax-M1 (M1-40k / M1-80k)?
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-R1-0528: $0.50 / 1M tokens input, $2.15 / 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-R1-0528?
MiniMax-M1 (M1-40k / M1-80k) offers 1M with up to 80k tokens (M1-80k); 40k tokens (M1-40k) output; DeepSeek-R1-0528 offers 164K with up to 32K tokens (64K max generation length) output.
Is MiniMax-M1 (M1-40k / M1-80k) newer than DeepSeek-R1-0528?
MiniMax-M1 (M1-40k / M1-80k) was released 2025-06-16; DeepSeek-R1-0528 was released 2025-05-28.
Go deeper
- MiniMax-M1 (M1-40k / M1-80k): full specs, pricing and benchmarks
- DeepSeek-R1-0528: full specs, pricing and benchmarks
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