Pixtral 12B 24.09

Open multimodal 12B model handling interleaved images and text with a 128K context window.

4.6 (5)

Recenzat de Daniel Nikulshyn·Actualizat mai 2026

Prezentare

Pixtral 12B 24.09 is a multimodal model from Mistral AI that processes both images and text within a single sequence, supporting variable image sizes and aspect ratios. It uses a 12-billion-parameter language decoder paired with a vision encoder, enabling tasks like visual question answering, document understanding, chart interpretation, and image captioning. The model accepts up to a 128K token context, allowing multiple images to be interleaved with long-form text in one prompt. Released under an open license, it can be deployed locally or through inference providers, making it suitable for developers building vision-language applications, research workflows, and multimodal agents.

Funcții cheie

12B parameter vision-language model
Interleaved image and text inputs
128K token context length
Native variable image size support
Open-weight release
Suitable for OCR, VQA, and captioning

Pro și contra

Pro

Open weights for self-hosting
Handles multiple images per prompt
Large 128K context window
Flexible image resolutions and aspect ratios

Contra

Requires significant GPU resources
Smaller than frontier closed models
Limited tooling compared to proprietary APIs

Recenzii

4.6

Medie din 5 evaluări.

Conectează-te pentru a lăsa o recenzie.

Sanjay Gupta

Does the job

Pretty happy overall. Open-weight release just works and large 128K context window. but no dealbreakers — I'd recommend it to a friend without hesitating.

Fatima Zahra

Does the job

Pretty happy overall. Open-weight release just works and handles multiple images per prompt. but no dealbreakers — I'd recommend it to a friend without hesitating.

Naomi Suzuki

Years in this space

I've evaluated a lot of these over the years. What stands out here is interleaved image and text inputs — handled better than most — and handles multiple images per prompt. Smaller than frontier closed models is my one real gripe. Worth the time if this is your use case.

Tariq Aziz

Solid for our team

We rolled this out across the team last quarter and open weights for self-hosting. Open-weight release fits neatly into how we already work, and interleaved image and text inputs removed a step we used to do by hand. Smaller than frontier closed models, which is the main caveat, but it has held up under daily use.

Aaliyah Johnson

Years in this space

I've evaluated a lot of these over the years. What stands out here is 12B parameter vision-language model — handled better than most — and open weights for self-hosting. Smaller than frontier closed models is my one real gripe. Worth the time if this is your use case.

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