Gemini 3.1 Pro vs PaliGemma
Compare Gemini 3.1 Pro and PaliGemma side-by-side.
Compare Gemini 3.1 Pro vs PaliGemma live
Run the same image across every model that supports a task and compare their outputs side-by-side.
These models don't share enough common tasks for a side-by-side demo. See the comparison table below for their capabilities.
Models in this comparison
Gemini 3.1 Pro vs PaliGemma Comparison Table
Evals updated July 10, 2026Pricing updated July 21, 2026
| Property | Gemini 3.1 Pro | PaliGemma |
|---|---|---|
| Organization | ||
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Feb 2026 | May 2024 |
| Context Window | 1.0M | — |
| Parameters | 3B | |
| License | Proprietary | Custom |
| Pricing per 1M tokens | ||
| Input $/1M | $2.00 | |
| Output $/1M | $12.00 | |
| Vision Tasks | ||
| Captioning | Demo | |
| Vision Language | ||
| Visual Question Answering | Demo | |
| Classification | Demo | |
| Object Detection | Demo | |
| OCR | Demo | |
| Model Features | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
| Foundation Vision | ||
Vision Evalsground-truth scores across 6 vision tasks | ||
| Overall | 84.6% | Not evaluated |
| Object Detection | 56.9% | – |
| Counting | 71.6% | – |
| Identification | 100.0% | – |
| OCR | 92.6% | – |
| Data Extraction | 94.8% | – |
| Reasoning | 91.3% | – |
| Avg cost / sample | $0.0068 | – |
| Avg speed / sample | 5.9s | – |
Gemini 3.1 Pro vs PaliGemma: Overview
Gemini 3.1 Pro is a proprietary multimodal model from Google’s Gemini 3 series, released in early 2026 and designed for advanced reasoning across large multimodal datasets. It accepts text, images, audio, video, and documents, supporting up to a 1-million-token input context with up to 64k output tokens. Compared with Gemini 3 Pro, it improves long-context synthesis and multi-step reasoning, enabling more reliable analysis of large documents, datasets, and software codebases.
The model also advances visual understanding and grounding, allowing it to interpret UI screenshots, diagrams, and real-world scenes while referencing specific regions within images or video. These capabilities make Gemini 3.1 Pro well suited for multimodal workflows involving document processing, interface analysis, robotics research, and complex visual reasoning.
PaliGemma is a vision-language model released in May 2024 by Google, built by pairing the SigLIP-So400m vision encoder with the Gemma 2B language model. It is designed primarily as a compact, transfer-friendly base model for fine-tuning to downstream vision-language tasks, rather than as a chat-optimized assistant. PaliGemma draws architectural inspiration from the PaLI-3 model at Google Research, applying a similar encoder-decoder approach at a smaller and more accessible parameter scale.
PaliGemma accepts an image together with a text prompt and generates text output, supporting image captioning, visual question answering, optical character recognition, object detection, referring expression segmentation, and a range of related vision-language tasks when fine-tuned on task-specific data. The model is released at three input resolutions (224, 448, and 896 pixels), with higher resolutions providing stronger performance on tasks requiring fine visual detail such as OCR and document understanding. Google released pretrained (PT) checkpoints intended as fine-tuning bases, along with Mix variants that have been fine-tuned on a mixture of downstream tasks for direct use without additional training. PaliGemma is distributed under the Gemma license, a custom license from Google that permits commercial use subject to the terms of the Gemma Prohibited Use Policy. It was succeeded by PaliGemma 2 in December 2024, which extends the architecture to larger Gemma 2 language backbones at 3B, 10B, and 28B parameter sizes.