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Mistral AI's 1.05T MoE frontier multimodal model with 52B active parameters, 512K context, and configurable reasoning traces.
Overview. Mistral Large 4 is Mistral AI's frontier multimodal model, built on a granular Mixture-of-Experts architecture containing 1.05 trillion total parameters and an integrated 1.6B vision encoder. Running with 52 billion active parameters per token, it supports combined text and visual inputs across a 512K-token context window with up to 262K output tokens. Developers reach for it when demanding frontier analytical capability and visual comprehension without sacrificing European data sovereignty.
Key strengths. The model demonstrates high analytical aptitude across mathematics, research, and code synthesis, reinforced by configurable chain-of-thought reasoning that can be toggled to high effort. Its 52B active parameter execution profile and native context caching deliver responsive interactive generation across massive prompts. Multimodal image understanding allows developers to inspect rich visual data and technical diagrams directly within complex reasoning pipelines.
When to use it. Route to Mistral Large 4 for multi-step mathematical derivations, extensive codebase reviews, and document research requiring combined visual and textual processing. It fits interactive reasoning workflows where deep chain-of-thought traces are needed on demand without slowing conversational execution. Use it within the Smart Router whenever workloads require frontier reasoning under strict European compliance standards.
Limitations. Mistral AI has not published the training cutoff date for this release, and the model does not support audio inputs or outputs. Furthermore, while technical documentation mentions a 1M-token ceiling, current API endpoints cap generation at a 512K context window.
Sovereignty note. Mistral Large 4 is made by Mistral AI, a French company under EU jurisdiction, so the model's creator is not subject to the US CLOUD Act. Whether a request stays sovereign depends on the Inference Provider and region that serve it: check the Provider Endpoints below before you send sensitive data.
| Inference Provider | Sovereignty | Runs in | In / out per 1M | Status | Pin |
|---|---|---|---|---|---|
| L1 | €0.67 / 2.06 | Online |
10 use cases classified by the curator. The note says why.
512K context capacity combined with flagship instruct capabilities ensures high-fidelity grounded generation across wide retrieval sets.
A 512K token context window enables single-prompt question answering across extensive document archives without retrieval degradation.
Built-in tool-calling capability delivers precise JSON adherence and schema execution for production API integrations.
A 512K context window holds long regulations and policy documents in one request. Mistral AI is a French company, so the model's creator is not subject to the US CLOUD Act; whether a request stays sovereign depends on the Inference Provider and region that serve it.
Massive 512K context window allows ingesting dozens of complete academic papers simultaneously for holistic synthesis.
512K context window handles full-length books, annual reports, and transcripts natively without chunking pipelines.
512K context window allows loading entire repositories or expansive pull requests into memory for contextual code inspection.
Reads images through its integrated 1.6B vision encoder (Mistral's documentation; vision_input is creator-attested). No image benchmark in the catalogue yet, so the fit is acceptable rather than strong.
Tool-calling capabilities and strong instruct adherence facilitate predictable schema extraction from long-form text inputs.
Sourced numbers, not our own evaluations. Percentiles are against the whole Stav catalogue.
Computed from aa-intelligence-index, gpqa-diamond, aa-engineering-index, aa-coding-index, livecodebench, terminal-bench-v4, aa-lcr.
curl https://api.stav.ai/v1/chat/completions \
-H "Authorization: Bearer $STAV_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "mistral-ai/mistral-large-4",
"messages": [{"role": "user", "content": "Hello!"}]
}'→ X-Stav-Request-Id · X-Stav-Model · X-Stav-Provider → X-Stav-Sovereignty-Level — the rung the request was served at