MiniMax-M1 is a large-scale, open-weight reasoning model with 456B total parameters and 45.9B active per token, using a hybrid Mixture-of-Experts (MoE) architecture and a custom lightning attention mechanism. It supports context windows up to 1 million tokens while maintaining competitive FLOP efficiency, 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, often outperforming other open models like DeepSeek R1 and Qwen3-235B.
$0.275market price $0.55
$1.10market price $2.20
Throughput is how fast the model writes (tokens per second, higher is better). Time to first token is how long before the answer starts (lower is better).
Share of requests served without a server error. Client errors (4xx) do not count.
Works with the OpenAI SDK pointed at our base URL. Every parameter is in the API reference.
Tokens processed per day on NagaAI.
MiniMax-M1 is a large-scale, open-weight reasoning model with 456B total parameters and 45.9B active per token, using a hybrid Mixture-of-Experts (MoE) architecture and a custom lightning attention mechanism.
On NagaAI, MiniMax M1 costs $0.275 per 1M input tokens and $1.10 per 1M output tokens. You pay only for what you use, with no subscription.
MiniMax M1 reads up to 1M tokens in one request, counting the prompt and the answer together.
Yes. MiniMax M1 can call your functions as tools and can answer in a JSON schema you give it.
It takes text as input and returns text.
Create an API key, copy the model ID from the top of this page and send requests with the OpenAI or Anthropic SDK pointed at NagaAI. The API section on this page has a ready example.
MiniMax M3.1 FlashMinimaxMiniMax M3MinimaxMiniMax M2.7MinimaxMiniMax M2.5MinimaxMiniMax M2.1Minimax$0.15 / $0.60 per 1MMiniMax M2Minimax$0.128 / $0.51 per 1MNot enough throughput data
Not enough TTFT data