Gemma 4 12B vs Pixtral 12B
Compare Gemma 4 12B and Pixtral 12B side-by-side.
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Gemma 4 12B vs Pixtral 12B Comparison Table
Evals updated July 10, 2026Pricing updated July 21, 2026
| Property | Gemma 4 12B | Pixtral 12B |
|---|---|---|
| Organization | Mistral | |
| Category | open | open |
| Modality | multimodal | multimodal |
| Release Date | Jun 2026 | Sep 2024 |
| Context Window | — | 128K |
| Parameters | 12B | 12B |
| License | Apache 2.0 | Apache 2.0 |
| Vision Tasks | ||
| Captioning | Demo | |
| OCR | Demo | |
| Vision Language | ||
| Visual Question Answering | Demo | |
| Model Features | ||
| Multimodal Vision | ||
Gemma 4 12B vs Pixtral 12B: Overview
Gemma 4 12B is an open-weight multimodal model from Google in the Gemma 4 family. It is intended for text and image understanding tasks such as visual question answering, OCR, captioning, and document understanding, with a smaller parameter footprint than the larger Gemma 4 variants.
This entry is connected to Roboflow Playground vision evals for comparison. No runnable Playground workflow is configured yet, so the model page is used for discovery and benchmark context rather than direct hosted inference.
Pixtral-12B is a vision-language model introduced by Mistral AI in September 2024 under the Apache 2.0 license, designed to process both text and images in a unified context. With ~12 billion parameters in its decoder and an additional ~400 million in a custom-trained vision encoder, it supports long-context reasoning up to 128k tokens and accepts multiple images per input. Its architecture is optimized for handling variable image sizes and aspect ratios, making it flexible for diverse multimodal tasks.
As Mistral’s first VLM, Pixtral-12B delivers strong performance not only on image-text reasoning benchmarks but also in text-only applications, positioning it as a versatile alternative to models like GPT-4V and LLaVA. Its open availability via Hugging Face and major cloud providers such as Amazon Bedrock and SageMaker makes it accessible for research and production. Typical use cases include document analysis, visual QA, data extraction, and multimodal assistants requiring both textual and visual understanding.