MiniMax M2.1 builds on M2 with enhanced context understanding and improved complex tool use. 230B parameter MoE model (10B active) optimized for agentic workflows and long-horizon tasks.
Added Jan 20, 2025
Model weightsContext Window
200.0K
Max Output
131.1K
Input Price (Auto)
$0.35/1M
Output Price (Auto)
$1.39/1M
Cache Read (Auto)
$0.17/1M
Capabilities
Benchmarks
Benchmarks
Performance metrics and benchmarks
Sourced from Artificial Analysis.
Intelligence Index
31.4
Reasoning
GPQA Diamond
Graduate-level scientific reasoning
83.0%
Better than 82% of models compared
HLE
Humanity's Last Exam
22.2%
Better than 85% of models compared
IFBench
Instruction-following benchmark
69.9%
Better than 84% of models compared
T²-Bench Telecom
Conversational AI agents in dual-control scenarios
85.4%
Better than 77% of models compared
AA-LCR
Long context reasoning evaluation
59.0%
Better than 74% of models compared
CritPt
Research-level physics reasoning
0.3%
Coding
SciCode
Python programming for scientific computing
40.7%
Better than 80% of models compared
Terminal-Bench Hard
Agentic coding and terminal use
28.8%
Better than 72% of models compared
LiveCodeBench
Contamination-free coding benchmark
81.0%
Better than 93% of models compared
Math
AIME 2025
American Invitational Mathematics Examination 2025
82.7%
Better than 79% of models compared
Knowledge
MMLU-Pro
Professional and academic subject knowledge
87.5%
Better than 98% of models compared
AA-Omniscience Accuracy
Proportion of correctly answered questions
21.2%
AA-Omniscience Hallucination Rate
Rate of incorrect answers among non-correct responses
68.5%
Last updated Jun 28, 2026
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