Mistral Large 4
Mistral Large 4 is an open-weight multimodal mixture-of-experts model with 1.05T total and 49B active parameters, built for reasoning, coding, tool use, and structured output. It supports a 512K-token context window.
- Reasoning
- Vision
- Tool Calling
- Structured Output
Added Oct 6, 2026
Pricing
Auto routing · per 1M tokens- Input
- $0.68
- Output
- $2.09
- Cache read
- $0.070
Specifications
- Context window
- 524.3K
- Max output
- N/A
- Parameters
- 1.1T
- Avg output (7d)
- 3.3K tokens
- Longer than 98% of models
Benchmarks
Benchmarks
Sourced from Artificial Analysis.
Intelligence Index
38.4
Agentic work
AutomationBench-AA
Workflow automation with guardrail penalties
59.9%
Better than 83% of models compared
AutomationBench-AA Tasks Completed
Fully completed workflows without guardrail violations
23.6%
Better than 71% of models compared
Harvey LAB-AA v1.1
Legal tasks fully passed without material hallucinations
3.1%
Better than 54% of models compared
AA-Briefcase v1.1
Agentic knowledge work (Elo)
1391 Elo
Better than 75% of models compared
GDPval-AA v2.1
Economically valuable tasks (Elo)
1434 Elo
Better than 77% of models compared
Document reasoning
GDP.pdf
Professional PDF reasoning: all-pass rate
18.6%
Better than 66% of models compared
AA-LCR v1.1
Long context reasoning with updated grading
81.3%
Better than 89% of models compared
MLCR-AA
Medical long-context reasoning
7.8%
Better than 23% of models compared
Reasoning
HLE
Humanity's Last Exam
35.0%
Better than 81% of models compared
Coding
Terminal-Bench v4.0
Practical coding and terminal tasks
26.8%
Better than 76% of models compared
SciCode
Python programming for scientific computing
54.2%
Better than 72% of models compared
Legacy benchmarks
AA-LCR (unversioned / legacy)
Long context reasoning evaluation
81.3%
Better than 89% of models compared
Last updated Oct 27, 2026
Artificial AnalysisProviders
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Reliability
Last 3 days