MiniMax M1 vs o1
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 o1.
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...
o1
The latest and strongest model family from OpenAI, o1 is designed to spend more time thinking before responding. The o1 model series is trained with large-scale reinforcement learning to reason...
Technical Specifications
| Specification | MiniMax M1 | o1 |
|---|---|---|
| Provider | MiniMax | OpenAI |
| Context Window | 1,000,000 tokens | 200,000 tokens |
| Agent Suitability | N/A | 88/100 |
| Time to First Token (TTFT) | N/A | 2500 ms |
| Deployment Model | managed api | managed api |
| Production Stability | beta | stable |
| API Available | Yes | Yes |
| Released Date | 2025-06-17 | 2024-12-17 |
API Pricing Comparison
Input Price per Million Tokens
MiniMax M1
$0.40
o1
$15.00
Output Price per Million Tokens
MiniMax M1
$2.20
o1
$60.00
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.
o1 Quirks & Gotchas
- ▸Reasoning model — high latency by design, not for real-time use
- ▸Best for complex math/code reasoning where accuracy > speed
- ▸Use o3-mini when you need reasoning with lower latency