Building upon Mistral Small 3 (2501), Mistral Small 3.1 (2503) adds state-of-the-art vision understanding and enhances long context capabilities up to 128k tokens without compromising text performance. With 24 billion parameters, this model achieves top-tier capabilities in both text and vision tasks.
Added Apr 15, 2025
Context Window
128.0K
Max Output
131.1K
Avg output tokens (7d)
116 tokens
Input Price (Auto)
$0.10/1M
Output Price (Auto)
$0.30/1M
Cache Read (Auto)
$0.050/1M
Benchmarks
Benchmarks
Performance metrics and benchmarks
Sourced from Artificial Analysis.
Intelligence Index
7.4
Coding Index
26.3
Agentic work
AutomationBench-AA
Workflow automation with guardrail penalties
1.2%
Better than 18% of models compared
AA-Briefcase
Agentic knowledge work (Elo)
325 Elo
Better than 20% of models compared
GDPval-AA v2
Economically valuable tasks (Elo)
550 Elo
Better than 24% of models compared
Document reasoning
GDP.pdf
Professional PDF reasoning: all-pass rate
1.0%
Better than 9% of models compared
AA-LCR v1.1
Long context reasoning with updated grading
22.3%
Better than 26% of models compared
Reasoning
HLE
Humanity's Last Exam
4.3%
Better than 19% of models compared
IFBench
Instruction-following benchmark
29.9%
Better than 14% of models compared
Coding
Terminal-Bench v4.0
Practical coding and terminal tasks
0.0%
Better than 18% of models compared
SciCode
Python programming for scientific computing
27.8%
Better than 7% of models compared
LiveCodeBench
Contamination-free coding benchmark
21.2%
Better than 23% of models compared
Math
AIME 2025
American Invitational Mathematics Examination 2025
3.7%
Better than 6% of models compared
AIME
American Invitational Mathematics Examination
9.3%
Better than 29% of models compared
Math-500
Diverse mathematical problem solving benchmark
70.7%
Better than 30% of models compared
Knowledge
MMLU-Pro
Professional and academic subject knowledge
65.9%
Better than 28% of models compared
Legacy benchmarks
GPQA Diamond (legacy)
Graduate-level scientific reasoning
45.4%
Better than 20% of models compared
Terminal-Bench Hard (legacy)
Agentic coding and terminal use
7.6%
Better than 40% of models compared
T²-Bench Telecom (legacy)
Conversational AI agents in dual-control scenarios
25.1%
Better than 29% of models compared
AA-LCR (unversioned / legacy)
Long context reasoning evaluation
22.3%
Better than 26% of models compared
Last updated Sep 11, 2026
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