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Pixtral 12B 24.09
Free plan available
Multimodal AI for image-text tasks with variable image support and 128K context
Multimodal processing
Variable image support
128k context window
Text-image integration
Long-form analysis
Introduction Video
Description
Multimodal AI for image-text tasks with variable image support and 128K context
Pixtral-12B-2409 is a 12-billion-parameter multimodal model by Mistral AI, combining a 12B-parameter text decoder with a 400M-parameter vision encoder. It processes interleaved text and images natively, supporting variable image sizes and a 128K-token context window for long-form document analysis or multi-image workflows. The model excels in tasks like chart understanding, OCR, and multilingual reasoning, outperforming similar-sized open models (e.g., Qwen2-VL 7B, LLaVA-OV 7B) and even larger models like Llama-3.2 90B in benchmarks like MMMU (52.5%) and MathVista (58.0%)
Pixtral 12B 24.09 Key Features
- 128K Context Window: Handles long documents or multi-image inputs.
- Variable Image Support: Processes images at native resolution and aspect ratio via a vision encoder.
- Multilingual & Code Capabilities: Supports 80+ coding languages and nuanced multilingual understanding.
- Open Source: Apache 2.0 license for free modification and deployment.
- High Accuracy: Outperforms Claude 3 Haiku and Gemini-1.5 Flash 8B in multimodal benchmarks.
- Vision-to-Code: Generates HTML/CSS from sketches or diagrams
Pixtral 12B 24.09 Use Cases
- Image Captioning & OCR: Generate descriptions or extract text from images/documents.
- Data Analysis: Convert charts to Markdown tables or interactive dashboards.
- Document QA: Answer questions from technical manuals or financial reports.
- Academic Research: Summarize papers or analyze scientific diagrams.
- Automation: Integrate with workflows for invoice processing or customer support
Pros
- Supports interleaved text and images, suitable for complex tasks.
- High 128K-token context window for long-form documents and multi-image workflows.
- Outperforms similar-sized and larger models in benchmarks like MMMU and MathVista.
- Effective at chart understanding, OCR, and multilingual reasoning.
- Variable image support enhances versatility in image processing tasks.
Cons
- May require significant computational resources due to its 12-billion parameters.
- Potentially complex integration into existing workflows due to its multimodal nature.
- Limited information on real-world application performance from user reviews.
Key Features
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