MiniMax M1 vs R1
How do these models stack up? Below is an expert side-by-side comparison of specifications, context window capacity, live pricing per million tokens, and standardized benchmark scores for MiniMax M1 and R1.
MiniMax M1
MiniMax-M1 is a large-scale, open-weight reasoning model designed for extended context and high-efficiency inference. It leverages a hybrid Mixture-of-Experts (MoE) architecture paired with a custom "lightning attention" mechanism, allowing it...
R1
DeepSeek R1 is here: Performance on par with [OpenAI o1](/openai/o1), but open-sourced and with fully open reasoning tokens. It's 671B parameters in size, with 37B active in an inference pass....
Technical Specifications
| Specification | MiniMax M1 | R1 |
|---|---|---|
| Provider | MiniMax | DeepSeek |
| Context Window | 1,000,000 tokens | 163,840 tokens |
| Agent Suitability | N/A | N/A |
| Time to First Token (TTFT) | N/A | N/A |
| Deployment Model | managed api | self hostable |
| Production Stability | beta | stable |
| API Available | Yes | Yes |
| Released Date | 2025-06-17 | 2025-01-20 |
API Pricing Comparison
Input Price per Million Tokens
MiniMax M1
$0.40
R1
$0.70
Output Price per Million Tokens
MiniMax M1
$2.20
R1
$2.50
Want to test both models live?
Run side-by-side prompt prompts in our dynamic Sandbox. Check execution speeds, latency metrics, and compute actual costs in real-time.
Benchmark Performance Metrics
Scores show the raw performance percentages verified across key evaluation suites. Higher bars indicate superior accuracy and capability in that domain.
MiniMax M1 Quirks & Gotchas
No developer gotchas reported.
R1 Quirks & Gotchas
No developer gotchas reported.