MiniMax M1
MiniMax-M1 is a hybrid MoE reasoning model with 40K thinking budget. World's first open-weight, large-scale hybrid-attention model with lightning attention for efficient test-time compute scaling. Excels at complex tasks requiring extensive reasoning.
Added Jan 8, 2025
Pricing
Auto routing · per 1M tokens- Input
- $0.14
- Output
- $1.33
Specifications
- Context window
- 1M
- Max output
- 131.1K
- Parameters
- 456B / 45.9B
- Total / active
- Avg output (7d)
- 266 tokens
- Longer than 22% of models
Benchmarks
Benchmarks
Sourced from Artificial Analysis.
Intelligence Index
10.0
Agentic work
T²-Bench Telecom (legacy)
Legacy fallback · Conversational AI agents in dual-control scenarios
31.6%
Better than 39% of models compared
Reasoning
HLE
Humanity's Last Exam
7.8%
Better than 45% of models compared
IFBench
Instruction-following benchmark
41.2%
Better than 41% of models compared
Coding
Terminal-Bench Hard (legacy)
Legacy fallback · Agentic coding and terminal use
2.3%
Better than 20% of models compared
LiveCodeBench
Contamination-free coding benchmark
65.7%
Better than 74% of models compared
Math
AIME 2025
American Invitational Mathematics Examination 2025
13.7%
Better than 17% of models compared
AIME
American Invitational Mathematics Examination
81.3%
Better than 89% of models compared
Math-500
Diverse mathematical problem solving benchmark
97.2%
Better than 88% of models compared
Knowledge
MMLU-Pro
Professional and academic subject knowledge
80.8%
Better than 71% of models compared
Legacy benchmarks
GPQA Diamond (legacy)
Graduate-level scientific reasoning
68.2%
Better than 45% of models compared
Last updated Oct 3, 2026
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