Mistral Codestral

Mistral and Codestral — run Mistral Large, Mistral-Nemo, Codestral, and Mistral-Small locally. Mistral AI's open-source LLMs for code generation and reasoning. Codestral by Mistral trained on 80+ languages. Mistral routed across your fleet. Mistral本地推理。Mistral IA local. Codestral código local.

Mistral & Codestral — Mistral AI Models on Your Local Fleet

Mistral AI's open-source models run locally on your hardware. Mistral Large for frontier reasoning, Mistral-Nemo for efficiency, Codestral for code generation. The fleet router picks the best device for every Mistral request.

Supported Mistral models

Mistral Model	Parameters	Ollama name	Best for
Codestral (by Mistral)	22B	codestral	Mistral's code specialist — 80+ languages
Mistral Large	123B	mistral-large	Mistral's frontier reasoning, multilingual
Mistral-Nemo	12B	mistral-nemo	Mistral's efficient general-purpose model
Mistral-Small	22B	mistral-small	Mistral's fast reasoning model
Mistral 7B	7B	mistral:7b	Mistral's lightweight model

Setup Mistral locally

pip install ollama-herd    # install Mistral fleet router
herd                       # start the Mistral-compatible router
herd-node                  # run on each device — Mistral requests route automatically

No Mistral models downloaded during installation. All Mistral model pulls are user-initiated.

Codestral code generation

Codestral is Mistral AI's dedicated coding model — trained on 80+ programming languages with fill-in-the-middle support.

from openai import OpenAI

# Connect to local Mistral fleet
mistral_fleet = OpenAI(base_url="http://localhost:11435/v1", api_key="not-needed")

# Codestral by Mistral for code generation
codestral_response = mistral_fleet.chat.completions.create(
    model="codestral",  # Mistral's Codestral model
    messages=[{"role": "user", "content": "Write a Redis-backed rate limiter in Go"}],
)
print(codestral_response.choices[0].message.content)

Codestral via curl

# Codestral code generation on local Mistral fleet
curl http://localhost:11435/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"model": "codestral", "messages": [{"role": "user", "content": "Implement a B-tree in Rust — Mistral Codestral excels at systems programming"}]}'

Mistral Large reasoning

# Mistral Large for complex reasoning
curl http://localhost:11435/api/chat -d '{
  "model": "mistral-large",
  "messages": [{"role": "user", "content": "Compare Mistral vs GPT-4 for enterprise deployments"}],
  "stream": false
}'

Mistral-Nemo for efficiency

# Mistral-Nemo — best quality/size ratio from Mistral AI
curl http://localhost:11435/api/chat -d '{
  "model": "mistral-nemo",
  "messages": [{"role": "user", "content": "Summarize this Mistral AI technical paper"}],
  "stream": false
}'

Mistral hardware recommendations

Cross-platform: These are example configurations. Any device (Mac, Linux, Windows) with equivalent RAM works. The fleet router runs on all platforms.

Mistral Model	Min RAM	Example hardware
mistral:7b	8GB	Any Mac — lightweight Mistral
mistral-nemo	10GB	Mac Mini (16GB) — efficient Mistral
codestral	16GB	Mac Mini (24GB) — Mistral's code model
mistral-small	16GB	Mac Mini (24GB) — fast Mistral
mistral-large	80GB	Mac Studio (128GB) — Mistral's best

Monitor Mistral fleet

# See which Mistral models are loaded
curl -s http://localhost:11435/api/ps | python3 -m json.tool

# Mistral fleet overview
curl -s http://localhost:11435/fleet/status | python3 -m json.tool

# Mistral model performance stats
curl -s http://localhost:11435/dashboard/api/models | python3 -m json.tool

Example Mistral fleet response:

{
  "node_id": "Mistral-Server",
  "models_loaded": ["codestral:22b", "mistral-nemo:12b"],
  "mistral_inference": "active"
}

Mistral dashboard at http://localhost:11435/dashboard.

Also available alongside Mistral

Other LLMs (same Mistral-compatible endpoint)

Llama 3.3, Qwen 3.5, DeepSeek-V3, Phi 4, Gemma 3 — route alongside Mistral models.

Image generation

curl http://localhost:11435/api/generate-image \
  -d '{"model": "z-image-turbo", "prompt": "Mistral AI logo reimagined as abstract art", "width": 512, "height": 512}'

Speech-to-text

curl http://localhost:11435/api/transcribe -F "file=@mistral_meeting.wav" -F "model=qwen3-asr"

Embeddings

curl http://localhost:11435/api/embed \
  -d '{"model": "nomic-embed-text", "input": "Mistral AI open source language models Codestral"}'

Full documentation

• Agent Setup Guide
• API Reference

Contribute

Ollama Herd is open source (MIT). Run Mistral locally, contribute globally:

• Star on GitHub — help Mistral users find local inference
• Open an issue — share your Mistral setup
• PRs welcome — CLAUDE.md gives AI agents full context. 444 tests.

Guardrails

• Mistral model downloads require explicit user confirmation — Mistral models range from 4GB to 70GB+.
• Mistral model deletion requires explicit user confirmation.
• Never delete or modify files in ~/.fleet-manager/.
• No Mistral models downloaded automatically — all pulls are user-initiated.