Ministral 3 14B Instruct 2512 is the compact model from Mistral AI, the French publisher: 14 billion parameters, 128 K of context, built for production at scale. Built in Europe, served in Europe — the complete sovereign chain, from the first weight to the last token. Why it matters →
Parameters
14 bn
Context
128 K
Publisher
Mistral AI
Served throughput
~140 t/s
The only extraterritorial link in an open-weights model is its publisher. With Ministral, even that link is European: design, publication and inference remain entirely under Union law.
Half the size of Qwen3.8-27B, it responds faster and costs half as much — the right choice for high-traffic chatbots, classification and data extraction.
Entire case files — contracts, reports, ticket histories — processed in a single call, with no chunking and no loss of coherence.
Structured JSON output and declared tools, with the excellent instruction-following of the Mistral lineage — ideal for agents and business automation.
| Need | Ministral 3 14B | Qwen3.8-27B |
|---|---|---|
| High-volume chatbot / support | Recommended | — |
| Extraction and classification | Recommended | — |
| Complex reasoning, mathematics | — | Recommended |
| Code generation and review | Good | Recommended |
| Very long documents (> 32 K tokens) | Recommended | With extension |
Indicative guidance — both models share the same API: switching model means changing one string. See the Qwen3.8-27B page →
OpenAI-compatible like the whole catalogue: only the model name changes. Switch from one to the other in production, with no redeployment.
from openai import OpenAI
client = OpenAI(
base_url="https://api.europeangpu.com/v1",
api_key=EGPU_API_KEY)
r = client.chat.completions.create(
model="ministral-3-14b-instruct-2512",
messages=[{"role": "user",
"content": "Classify these 400 tickets"}])No subscription, no minimum. For deep reasoning, see Qwen3.8-27B →