Mistral Small 4 with reasoning enabled (reasoning_effort=high). A hybrid MoE model with deep step-by-step reasoning for complex prompts, coding, and multi-step problem solving.
Added Mar 17, 2026
Model weightsContext Window
262.1K
Max Output
16.4K
Avg output tokens (7d)
628 tokens
Input Price (Auto)
$0.40/1M
Output Price (Auto)
$1.40/1M
Cache Read (Auto)
$0.20/1M
Capabilities
Benchmarks
Benchmarks
Performance metrics and benchmarks
Sourced from Artificial Analysis.
Intelligence Index
19.7
Coding Index
26.6
Agentic Index
4.6
Reasoning
GPQA Diamond
Graduate-level scientific reasoning
76.9%
Better than 64% of models compared
HLE
Humanity's Last Exam
9.9%
Better than 57% of models compared
IFBench
Instruction-following benchmark
48.2%
Better than 57% of models compared
T²-Bench Telecom
Conversational AI agents in dual-control scenarios
41.2%
Better than 47% of models compared
AA-LCR
Long context reasoning evaluation
47.3%
Better than 52% of models compared
GDPval-AA
Economically valuable tasks
4.5%
CritPt
Research-level physics reasoning
0.3%
Coding
SciCode
Python programming for scientific computing
38.0%
Better than 63% of models compared
Terminal-Bench Hard
Agentic coding and terminal use
17.4%
Better than 56% of models compared
Knowledge
AA-Omniscience Accuracy
Proportion of correctly answered questions
21.7%
AA-Omniscience Hallucination Rate
Rate of incorrect answers among non-correct responses
66.5%
Last updated Aug 16, 2026
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