Ministral 3 14B is a balanced model in the Ministral 3 family, designed for edge deployment. A powerful, efficient language model with vision capabilities, fine-tuned for instruction tasks. Features multilingual support, strong system prompt adherence, and native function calling. Apache 2.0 licensed.
Added Dec 2, 2025
Context Window
262.1K
Max Output
32.8K
Avg output tokens (7d)
156 tokens
Input Price (Auto)
$0.10/1M
Output Price (Auto)
$0.40/1M
Capabilities
Benchmarks
Benchmarks
Performance metrics and benchmarks
Sourced from Artificial Analysis.
Intelligence Index
6.0
Coding Index
14.4
Agentic Index
1.1
Agentic work
AutomationBench-AA
Workflow automation with guardrail penalties
0.6%
Better than 10% of models compared
AA-Briefcase
Agentic knowledge work (Elo)
131 Elo
Better than 11% of models compared
GDPval-AA v2
Economically valuable tasks (Elo)
234 Elo
Better than 15% of models compared
Document reasoning
GDP.pdf
Professional PDF reasoning: all-pass rate
1.2%
Better than 9% of models compared
AA-LCR v1.1
Long context reasoning with updated grading
26.3%
Better than 27% of models compared
Reasoning
HLE
Humanity's Last Exam
4.6%
Better than 23% of models compared
IFBench
Instruction-following benchmark
32.0%
Better than 19% 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
23.8%
Better than 4% of models compared
LiveCodeBench
Contamination-free coding benchmark
35.1%
Better than 42% of models compared
Math
AIME 2025
American Invitational Mathematics Examination 2025
30.0%
Better than 31% of models compared
Knowledge
MMLU-Pro
Professional and academic subject knowledge
69.3%
Better than 34% of models compared
AA-Omniscience Accuracy
Proportion of correctly answered questions
13.6%
AA-Omniscience Hallucination Rate
Rate of incorrect answers among non-correct responses
92.5%
Legacy benchmarks
GPQA Diamond (legacy)
Graduate-level scientific reasoning
57.2%
Better than 31% of models compared
Terminal-Bench Hard (legacy)
Agentic coding and terminal use
4.5%
Better than 29% of models compared
T²-Bench Telecom (legacy)
Conversational AI agents in dual-control scenarios
27.2%
Better than 33% of models compared
AA-LCR (unversioned / legacy)
Long context reasoning evaluation
26.3%
Better than 27% of models compared
GDPval-AA (unversioned / legacy)
Economically valuable tasks
0.0%
Last updated Oct 1, 2026
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