Ministral 14B is a powerful 14B parameter model from Mistral AI with vision capabilities, offering frontier performance in a compact size.
Added Dec 4, 2025
Model weightsContext Window
262.1K
Max Output
32.8K
Avg output tokens (7d)
416 tokens
Input Price (Auto)
$0.20/1M
Output Price (Auto)
$0.20/1M
Cache Read (Auto)
$0.10/1M
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 12% of models compared
AA-Briefcase
Agentic knowledge work (Elo)
150 Elo
Better than 12% of models compared
GDPval-AA v2
Economically valuable tasks (Elo)
432 Elo
Better than 17% of models compared
Document reasoning
GDP.pdf
Professional PDF reasoning: all-pass rate
1.2%
Better than 13% of models compared
AA-LCR v1.1
Long context reasoning with updated grading
26.3%
Better than 29% of models compared
Reasoning
HLE
Humanity's Last Exam
4.6%
Better than 25% 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 17% 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 29% of models compared
GDPval-AA (unversioned / legacy)
Economically valuable tasks
0.0%
Last updated Sep 13, 2026
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