Ministral 3B
Ministral 3B is a tiny, efficient 3B parameter model from Mistral AI with vision capabilities, designed for edge deployment.
Added Dec 4, 2025
Model weightsPricing
Auto routing · per 1M tokens- Input
- $0.10
- Output
- $0.10
Specifications
- Context window
- 131.1K
- Max output
- 32.8K
- Parameters
- 3B
- Avg output (7d)
- 235 tokens
- Longer than 19% of models
Benchmarks
Benchmarks
Sourced from Artificial Analysis.
Intelligence Index
4.8
Coding Index
4.8
Agentic Index
0.8
Agentic work
AutomationBench-AA
Workflow automation with guardrail penalties
0.7%
Better than 11% of models compared
AA-Briefcase
Agentic knowledge work (Elo)
0 Elo
Better than 3% of models compared
GDPval-AA v2
Economically valuable tasks (Elo)
32 Elo
Better than 10% of models compared
Document reasoning
GDP.pdf
Professional PDF reasoning: all-pass rate
0.0%
Better than 0% of models compared
AA-LCR v1.1
Long context reasoning with updated grading
17.0%
Better than 20% of models compared
Reasoning
HLE
Humanity's Last Exam
5.4%
Better than 32% of models compared
IFBench
Instruction-following benchmark
26.8%
Better than 10% of models compared
CritPt
Research-level physics reasoning
0.0%
Coding
Terminal-Bench v4.0
Practical coding and terminal tasks
0.0%
Better than 16% of models compared
SciCode
Python programming for scientific computing
15.3%
Better than 1% of models compared
LiveCodeBench
Contamination-free coding benchmark
24.7%
Better than 26% of models compared
Math
AIME 2025
American Invitational Mathematics Examination 2025
22.0%
Better than 24% of models compared
Knowledge
MMLU-Pro
Professional and academic subject knowledge
52.4%
Better than 16% of models compared
AA-Omniscience Accuracy
Proportion of correctly answered questions
9.0%
AA-Omniscience Hallucination Rate
Rate of incorrect answers among non-correct responses
80.2%
Legacy benchmarks
GPQA Diamond (legacy)
Graduate-level scientific reasoning
35.8%
Better than 12% of models compared
Terminal-Bench Hard (legacy)
Agentic coding and terminal use
0.0%
Better than 5% of models compared
T²-Bench Telecom (legacy)
Conversational AI agents in dual-control scenarios
24.9%
Better than 28% of models compared
AA-LCR (unversioned / legacy)
Long context reasoning evaluation
17.0%
Better than 20% of models compared
GDPval-AA (unversioned / legacy)
Economically valuable tasks
0.0%
Last updated Oct 3, 2026
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