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Mistral AI's dense 8B Instruct model with text and image input, a 262,144-token context window and open Apache-2.0 weights.
Overview. Ministral 3 8B Instruct 2512 is a dense, instruction-tuned model from Mistral AI, released in December 2025 as part of the Ministral 3 family. It accepts text and image input, returns text, and ships with a 262,144-token context window. Developers can reach for it when they want a compact multimodal model whose weights are published openly.
Key strengths. The model pairs a language model of about 8.4B parameters with a 0.4B vision encoder, so image analysis comes with the model and needs no separate pipeline. Its 262,144-token context window is large for a model of this size. The Apache-2.0 weights are public and ungated. The Stav catalogue holds no benchmark scores for this checkpoint, so we make no benchmark claims here.
When to use it. Use it for chat and assistant workloads that mix text and images, such as describing screenshots or extracting details from photos. It also suits long-context prompts where a larger model is more than the task needs. The Smart Router can treat it as a fit for simple to medium tasks. Choose it when you want permissively licensed weights you can download and run yourself.
Limitations. Output is text only, and there is no audio input or output. Mistral publishes a separate Reasoning checkpoint, so this Instruct model is not that variant, and Base and Reasoning results should not be read as this model's results. The vendor's shipped system prompt gives a knowledge update date of October 2023, so recent events may be missing.
Sovereignty note. Mistral AI is the creator, and the weights are released under Apache-2.0, so you can run Inference on infrastructure you control. This model's training-on-input policy and endpoint data residency have not been confirmed, so check the endpoint you select.
| Inference Provider | Sovereignty | Runs in | In / out per 1M | Status | Pin |
|---|---|---|---|---|---|
| L1 | €0.15 / 0.15 | Online |
29 use cases classified by the curator. The note says why.
Query rewriting is a short, latency-sensitive task where a small, cheap model is the ideal choice. Tool-calling support allows structured rewrite outputs.
Routing and intent triage are short, low-complexity tasks where a cheap, fast small model is the right fit. Tool-calling supports structured routing output, and sovereignty is a plus for EU teams.
The large context window fits many retrieved passages for grounded answer generation, and the cost profile suits high-volume RAG. Faithfulness is unbenchmarked.
Small, cheap models handle label-style classification well at volume. Accuracy is unbenchmarked, so validate on your own labels.
Low-cost small model from a French creator with no CLOUD Act exposure, which suits EU support deployments. Tool-calling allows ticket and CRM lookups, though accuracy is unbenchmarked.
A 262,144-token context supports question answering over long documents without chunking at low cost. Retrieval precision on very long inputs at this scale needs validation.
Short-form drafting is within reach of a compact model, and the low cost suits high-volume use. Not a leader for nuanced tone.
Tool-calling enables schema-constrained extraction output, and the compact size keeps per-document cost low. No extraction benchmarks are available.
Tool-calling and a long context help, but there are no coding benchmarks (e.g. SWE-Bench) and the model sits in a small-parameter tier. Larger models are better for autonomous multi-file coding.
No vision capability is listed and no agentic benchmark evidence exists. Browser automation needs reliable multi-step tool use and screen grounding, which this model does not document.
Explaining charts requires image input, which this model does not list.
Chart extraction requires image input, and this model lists only tool-calling.
Compact models generally trail larger ones in creative depth and style control, and there is no evidence in the catalogue to suggest otherwise.
No vision capability is documented, so the model cannot read scanned documents or images.
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, ifbench, aa-multilingual-index, 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/ministral-8b-2512",
"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