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
Context Window
1.0M
Max Output
131.1K
Avg output tokens (7d)
289 tokens
Input Price (Auto)
$0.14/1M
Output Price (Auto)
$1.33/1M
Benchmarks
Benchmarks
Performance metrics and 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 1, 2026
Artificial AnalysisProviders
Auto routing is available for this model. Explicit provider selection is not available.
Loading provider options…
Related text models
Compare MiniMax M1 with similar models from the same provider or model family.
MiniMax M3
minimax/minimax-m3MiniMax M3 is the non-thinking route for MiniMax's open-weights frontier model, built for coding, agent workflows, tool use, and multimodal understanding from step zero. It keeps native thinking disabled for faster direct answers. MiniMax reports 59.0% on SWE-Bench Pro and 66.0% on Terminal Bench 2.1, with Sparse Attention designed to scale context to 1M. It starts with a 512K context cap on NanoGPT for now.
MiniMax M3 Thinking
minimax/minimax-m3:thinkingMiniMax M3 Thinking is the adaptive-thinking version of MiniMax's open-weights frontier model for coding, agent workflows, tool use, long-context tasks, and native multimodal understanding. MiniMax reports 59.0% on SWE-Bench Pro and 66.0% on Terminal Bench 2.1, with Sparse Attention designed to scale context to 1M. It starts with a 512K context cap on NanoGPT for now.
MiniMax Latest
minimax/minimax-latestCompatibility alias that routes to the newest MiniMax text model. Currently routes to MiniMax M3 (adaptive thinking).
MiniMax M2.7
minimax/minimax-m2.7MiniMax M2.7 is the first model deeply involved in iterating on its own training. It excels in real-world software engineering (SWE-Pro 56.22%), end-to-end project delivery (VIBE-Pro 55.6%), and complex office workflows with strong tool-use compliance and agentic capabilities.
MiniMax M2.7 Turbo
minimax/minimax-m2.7-turboMiniMax M2.7 Turbo is the highspeed and higher priced route for M2.7.
MiniMax M2.5
minimax/minimax-m2.5MiniMax M2.5 is a productivity-focused flagship model that builds on M2.1 with stronger coding and real-world office workflow performance (Word, Excel, PowerPoint), plus better tool-use planning and token efficiency.