MiniMax M3
MiniMax 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.
- Vision
- Tool Calling
- Structured Output
Added Jun 1, 2026
Model weightsPricing
Auto routing · per 1M tokens- Input
- $0.23
- Output
- $0.96
- Cache read
- $0.050
Specifications
- Context window
- 512K
- Max output
- 80K
- Parameters
- 428B / 23B
- Total / active
- Avg output (7d)
- 548 tokens
- Longer than 49% of models
Benchmarks
Benchmarks
Sourced from Artificial Analysis.
Intelligence Index
29.2
Coding Index
58.6
Agentic Index
29.5
Agentic work
AutomationBench-AA
Workflow automation with guardrail penalties
21.3%
Better than 43% of models compared
AutomationBench-AA Tasks Completed
Fully completed workflows without guardrail violations
4.4%
Better than 10% of models compared
Harvey LAB-AA
Legal agentic work criterion pass rate
88.4%
Better than 51% of models compared
AA-Briefcase
Agentic knowledge work (Elo)
1091 Elo
Better than 56% of models compared
GDPval-AA v2
Economically valuable tasks (Elo)
1245 Elo
Better than 63% of models compared
Document reasoning
GDP.pdf
Professional PDF reasoning: all-pass rate
9.8%
Better than 39% of models compared
AA-LCR v1.1
Long context reasoning with updated grading
83.0%
Better than 95% of models compared
MLCR-AA
Medical long-context reasoning
17.2%
Better than 58% of models compared
Reasoning
HLE
Humanity's Last Exam
39.0%
Better than 86% of models compared
IFBench
Instruction-following benchmark
82.9%
Better than 99% of models compared
CritPt
Research-level physics reasoning
3.7%
Coding
Terminal-Bench v4.0
Practical coding and terminal tasks
2.0%
Better than 48% of models compared
SciCode
Python programming for scientific computing
47.1%
Better than 43% 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%
Legacy benchmarks
GPQA Diamond (legacy)
Graduate-level scientific reasoning
92.9%
Better than 96% of models compared
Terminal-Bench Hard (legacy)
Agentic coding and terminal use
42.4%
Better than 89% of models compared
T²-Bench Telecom (legacy)
Conversational AI agents in dual-control scenarios
88.9%
Better than 83% of models compared
AA-LCR (unversioned / legacy)
Long context reasoning evaluation
83.0%
Better than 95% of models compared
GDPval-AA (unversioned / legacy)
Economically valuable tasks
37.1%
Last updated Oct 15, 2026
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