Mistral Small 4 is a hybrid MoE model that unifies instruct, reasoning, and coding behavior in a single multimodal model. It supports text and image input, native function calling, JSON output, and per-request reasoning effort controls.
Added Mar 16, 2026
Model weightsContext Window
262.1K
Max Output
16.4K
Avg output tokens (7d)
333 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
12.3
Reasoning
GPQA Diamond
Graduate-level scientific reasoning
57.1%
Better than 32% of models compared
HLE
Humanity's Last Exam
3.8%
Better than 11% of models compared
IFBench
Instruction-following benchmark
32.8%
Better than 21% of models compared
T²-Bench Telecom
Conversational AI agents in dual-control scenarios
18.4%
Better than 16% of models compared
AA-LCR
Long context reasoning evaluation
24.3%
Better than 32% of models compared
CritPt
Research-level physics reasoning
0.3%
Coding
SciCode
Python programming for scientific computing
28.1%
Better than 36% of models compared
Terminal-Bench Hard
Agentic coding and terminal use
10.6%
Better than 45% of models compared
Knowledge
AA-Omniscience Accuracy
Proportion of correctly answered questions
16.6%
AA-Omniscience Hallucination Rate
Rate of incorrect answers among non-correct responses
78.0%
Last updated Aug 16, 2026
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