Model comparison
DeepSeek-R1 vs MiniMax-Text-01
- Input
- $0.55 / 1M tokens (cache miss; $0.14 cache hit)
- Output
- $2.19 / 1M tokens
- Context
- 128K
- Released
- 2025-01-20
- Input
- $0.20 / 1M tokens
- Output
- $1.10 / 1M tokens
- Context
- 4M
- Released
- 2025-01-15
DeepSeek-R1 scores higher on 4 of the 4 benchmarks both models report; MiniMax-Text-01 is cheaper on input tokens ($0.2 vs $0.6 per 1M, 2.8×); MiniMax-Text-01 has the larger context window (4M vs 128K).
Specs and pricing side by side
| Spec | DeepSeek-R1 | MiniMax-Text-01 |
|---|---|---|
| Maker | DeepSeek | MiniMax |
| Released | 2025-01-20 | 2025-01-15 |
| Context window | 128K | 4M |
| Max output | 32,768 tokens (max generation length) | Not separately published |
| Input price | $0.55 / 1M tokens (cache miss; $0.14 cache hit) | $0.20 / 1M tokens |
| Output price | $2.19 / 1M tokens | $1.10 / 1M tokens |
| Parameters | 671B total / 37B active (Mixture-of-Experts) | 456B total / 45.9B active (MoE, 32 experts, Top-2 routing, 80 layers) |
| License | MIT | MiniMax Model License Agreement (open weights, custom) |
| Weights | Open weights | Open weightssame |
| Modalities | Text | Textsame |
| Knowledge cutoff | Not officially disclosed | Not officially stated |
Benchmarks both models report
| Benchmark | DeepSeek-R1 | MiniMax-Text-01 |
|---|---|---|
| GPQA Diamond | 71.5% | 54.4% |
| MMLU | 90.8% | 88.5% |
| MMLU-Pro | 84% | 75.7% |
| Arena-Hard | 92.3% | 89.1% |
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 DeepSeek-R1 if…
- You care about GPQA Diamond, MMLU, MMLU-Pro (it scores higher on 4 shared benchmarks).
- Your use case is: competition-style math and multi-step logical reasoning.
Pick MiniMax-Text-01 if…
- Cost matters: input tokens cost 2.8× less ($0.2 vs $0.6 per 1M).
- You need the longer context (4M vs 128K).
- Your use case is: long-document and whole-codebase analysis that needs hundreds of thousands to millions of tokens of context.
FAQ
Which is better, DeepSeek-R1 or MiniMax-Text-01?
DeepSeek-R1 scores higher on 4 of the 4 benchmarks both models report; MiniMax-Text-01 is cheaper on input tokens ($0.2 vs $0.6 per 1M, 2.8×); MiniMax-Text-01 has the larger context window (4M 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 MiniMax-Text-01?
DeepSeek-R1: $0.55 / 1M tokens (cache miss; $0.14 cache hit) input, $2.19 / 1M tokens output. MiniMax-Text-01: $0.20 / 1M tokens input, $1.10 / 1M tokens output (list API prices as published by each maker).
What are the context windows of DeepSeek-R1 and MiniMax-Text-01?
DeepSeek-R1 offers 128K with up to 32,768 tokens (max generation length) output; MiniMax-Text-01 offers 4M with up to Not separately published output.
Is DeepSeek-R1 newer than MiniMax-Text-01?
DeepSeek-R1 was released 2025-01-20; MiniMax-Text-01 was released 2025-01-15.