MiniMax 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.
Added Jun 1, 2026
Model weightsContext Window
512.0K
Max Output
80.0K
Avg output tokens (7d)
1.5K tokens
Input Price (Auto)
$0.23/1M
Output Price (Auto)
$0.96/1M
Cache Read (Auto)
$0.050/1M
Capabilities
Benchmarks
Benchmarks
Performance metrics and benchmarks
Sourced from Artificial Analysis.
Intelligence Index
35.7
Coding Index
58.6
Agentic Index
31.0
Reasoning
GPQA Diamond
Graduate-level scientific reasoning
92.9%
Better than 96% of models compared
HLE
Humanity's Last Exam
39.0%
Better than 90% of models compared
IFBench
Instruction-following benchmark
82.9%
Better than 99% of models compared
T²-Bench Telecom
Conversational AI agents in dual-control scenarios
88.9%
Better than 83% of models compared
AA-LCR
Long context reasoning evaluation
83.0%
Better than 97% of models compared
GDPval-AA
Economically valuable tasks
40.4%
CritPt
Research-level physics reasoning
3.7%
Coding
SciCode
Python programming for scientific computing
47.1%
Better than 42% of models compared
Terminal-Bench Hard
Agentic coding and terminal use
42.4%
Better than 89% of models compared
Knowledge
AA-Omniscience Accuracy
Proportion of correctly answered questions
16.7%
AA-Omniscience Hallucination Rate
Rate of incorrect answers among non-correct responses
18.4%
Last updated Sep 7, 2026
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