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Models/Pixtral 12B
Mistral AIOpen Weights

Pixtral 12B

Vision LLM • Released 2024-03-10 • Last Verified 2025-02-01

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Pixtral 12B is an advanced generative vision and image manipulation model developed by Mistral AI. Built on high-capacity diffusion and latent vision transformer architecture, Pixtral 12B delivers precise text-guided image synthesis, regional editing, style adaptation, and fine-grained visual coherence across commercial and artistic workflows.

Context Window128k
LicenseApache-2.0 / MNPL
Deployment
Cloud API Local Run
API AvailableYes (REST/SDK)
💡

Plain English Summary (What is this model & who is it for?)

Think of Pixtral 12B as your personal AI photo artist and editor. You can type simple text instructions (like 'change lighting to sunset' or 'remove background objects'), and the AI modifies your photo or generates brand-new images instantly without needing complex software like Photoshop.

💡 Real-World Use Cases & Practical Examples

📄 Document & Receipt Parsing: Extract total amounts, dates, and line items from invoices and receipts.
📊 Chart & Graph Analysis: Upload financial reports to extract trends and summary insights.
🔍 Visual Inspection: Identify products, serial numbers, or visual damage in uploaded photos.
🖼️ Image Captioning: Generate detailed alt-text and descriptions for accessibility and SEO.

🚀 How to Run & Use This Model (Step-by-Step Guide)

Simple setup instructions for everyday users and developers.

1

Download a One-Click App (No Coding Required)

Download a free local AI launcher like LM Studio (lmstudio.ai) or Ollama (ollama.com) on your Mac, Windows, or Linux PC.

2

Load the Model

In LM Studio, search for "Pixtral 12B". In Ollama, open your terminal and run "ollama run pixtral-12b".

3

Start Chatting or Generating

Type your text instructions or upload files into the app. The AI runs 100% privately on your hardware without internet requirement!

4

Developer API Integration

Developers can integrate Pixtral 12B directly via Python (using Hugging Face transformers/diffusers) or connect via local OpenAI-compatible REST server (http://localhost:11434).

Benchmark Performance

MMLU81

Hardware Requirements for Local Running

Requires dedicated GPU with 16GB+ VRAM recommended for fast local inference.

Strengths

  • •High accuracy
  • •Fast inference

Limitations & Weaknesses

  • •Closed source API
Integration Code (vision-language)
from transformers import AutoProcessor, AutoModelForVision2Seq
from PIL import Image
import torch

model_id = "pixtral-12b"
model = AutoModelForVision2Seq.from_pretrained(model_id, torch_dtype=torch.float16, device_map="auto")
processor = AutoProcessor.from_pretrained(model_id)

image = Image.open("sample.jpg")
inputs = processor(text="Analyze the contents of this image:", images=image, return_tensors="pt").to("cuda")

outputs = model.generate(**inputs, max_new_tokens=150)
print(processor.batch_decode(outputs, skip_special_tokens=True)[0])

Pricing Overview

Free Open Weights / API

Prices subject to provider tiers and volume discounts. Check documentation for current token rates.

Model Tags

#mistral#open-weights#european

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