MiniMax: MiniMax M1

Text input Text output
Author's Description

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.

Key Specifications
Cost
$$$$$
Context
1M
Released
Jun 17, 2025
Speed
Ability
Reliability
Supported Parameters

This model supports the following parameters:

Include Reasoning Top P Temperature Reasoning Max Tokens
Features

This model supports the following features:

Reasoning
Performance Summary

MiniMax-M1 demonstrates exceptional reliability, consistently providing usable responses with minimal technical failures, ranking in the 99th percentile. However, this reliability comes with a trade-off in speed, as the model tends to have longer response times, placing it in the 3rd percentile across benchmarks. Its pricing is positioned at premium levels, ranking in the 17th percentile. Across specific benchmarks, MiniMax-M1 exhibits strong performance in accuracy, particularly excelling in Email Classification with perfect 100% accuracy, making it the most accurate model at its price point and among models of comparable speed. It also shows high accuracy in General Knowledge (99.8%), Ethics (99.0%), Coding (91.0%), and Reasoning (92.0%). Its primary weakness lies in Instruction Following, where its accuracy drops to 62.6%. While its cost per query varies, it generally falls within the 8th to 25th percentile for cost-effectiveness on individual benchmarks. The consistently high duration across all benchmarks reinforces its slower processing speed. Overall, MiniMax-M1 is a highly reliable and accurate model, particularly for knowledge-intensive and complex reasoning tasks, but users should account for its longer response times and premium pricing.

Model Pricing

Current Pricing

Feature Price (per 1M tokens)
Prompt $0.3
Completion $1.65

Price History

Available Endpoints
Provider Endpoint Name Context Length Pricing (Input) Pricing (Output)
Minimax
Minimax | minimax/minimax-m1 1M $0.3 / 1M tokens $1.65 / 1M tokens
Novita
Novita | minimax/minimax-m1 1M $0.55 / 1M tokens $2.2 / 1M tokens
Benchmark Results
Benchmark Category Reasoning Free Executions Accuracy Cost Duration
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