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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 to process long sequences—up to 1 million tokens—while maintaining competitive FLOP efficiency. With 456 billion total parameters and 45.9B active per token, this variant is optimized for complex, multi-step reasoning tasks. Trained via a custom reinforcement learning pipeline (CISPO), M1 excels in long-context understanding, software engineering, agentic tool use, and mathematical reasoning. Benchmarks show strong performance across FullStackBench, SWE-bench, MATH, GPQA, and TAU-Bench, often outperforming other open models like DeepSeek R1 and Qwen3-235B.
Preço/1M
$0.85
461st mais barato
174% acima da mediana
Top 68%
Janela de Contexto
1.0M
28th maior
Top 11%
Entrada
$0.40
por 1M tokens
Saída
$2.20
por 1M tokens
Combinado
$0.85
por 1M tokens
Mais barato que 32% dos modelos. Preço mediano é $0.31/1M tokens.
Diário
$0.85
Mensal
$25.50
Janela de Contexto
1.0M
tokens
Maior que 89% dos modelos
Saída Máxima
40K
tokens
4% do contexto
Comparação de Janela de Contexto