MiniMax M1 80K
Built by MiniMax · China · minimax.io
MiniMax-M1: The World's First Open-Weight, Large-Scale Hybrid Attention Inference Model MiniMax-M1 adopts a Mixture of Experts (MoE) architecture and integrates the Flash Attention mechanism. The model contains a total of 456 billion parameters, with 45.9 billion parameters activated per token. Natively, the M1 model supports a context length of 1 million tokens—8 times that of DeepSeek R1. Additionally, by combining the CISPO algorithm with an efficient hybrid attention design for reinforcement learning training, MiniMax-M1 achieves industry-leading performance in long-context reasoning and real-world software engineering scenarios.
How to use
Just add the suffix. Hard questions still come back from this model; only easy ones drop to something cheaper, and nothing pricier than it gets used.
model: "minimax/minimax-m1-80k:auto"
Sets no behavior — whatever your key already stores stays in effect.
Without the suffix, the bare name always goes to this model — no routing, so nothing saved.
Context
Max output
Input $/1M
Output $/1M
Features
Providers
NovitaProviders
Price, latency, and uptime per provider serving this model. Warp tries them in order of how each host has just been behaving, moving to the next on failure. Click a row for regions and data policies.
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Performance
Measured latency (lower is better) and throughput (higher is better) per provider.
Speed
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Parameter support
What Warp actually does with each parameter when you call this model. The answer differs by serving provider and by API surface.