minimax-m1
by minimaxMiniMax-M1 is a large-scale, open-weight reasoning model with 456B total parameters and 45.9B active per token, leveraging a hybrid Mixture-of-Experts (MoE) architecture and a custom "lightning attention" mechanism. It supports context windows up to 1 million tokens and is optimized for long-context understanding, software engineering, agentic tool use, and mathematical reasoning. The model is trained via a custom reinforcement learning pipeline (CISPO) and demonstrates strong performance on FullStackBench, SWE-bench, MATH, GPQA, and TAU-Bench.
Pricing
Pay-as-you-go rates for this model. More details can be found here.
Input Tokens (1M)
$0.40
Output Tokens (1M)
$0.96
Capabilities
Input Modalities
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Output Modalities
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Rate Limits
Requests per minute (RPM) and per day (RPD) by tier. More about tiers here
| Tier | RPM | RPD |
|---|---|---|
| Free | — | — |
| Tier 1 | 10 | — |
| Tier 2 | 15 | — |
| Tier 3 | 25 | — |
| Tier 4 | 50 | — |
Usage Analytics
Token usage across the last 30 active days