Gemini 3.1 Pro vs Qwen2.5 VL 7B Instruct
Compare Gemini 3.1 Pro and Qwen2.5 VL 7B Instruct side-by-side. See how these vision models stack up in Image Captioning, Open Prompt, and OCR.
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Gemini 3.1 Pro vs Qwen2.5 VL 7B Instruct: 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.
Qwen2.5-VL-7B-Instruct is a 7-billion parameter vision-language model from Alibaba’s QwenLM team, released on January 26, 2025 under the Apache 2.0 license. It is the instruction-tuned variant of the 7B scale in the Qwen2.5-VL family, designed to process multimodal inputs such as text, images, charts, documents, and video. The model enables structured outputs—including JSON for structured content and bounding boxes for visual localization. Weights are publicly available on Hugging Face and GitHub, making it suitable for both research and applied multimodal use.
Gemini 3.1 Pro vs Qwen2.5 VL 7B Instruct Comparison Table
| Property | Gemini 3.1 Pro | Qwen2.5 VL 7B Instruct |
|---|---|---|
| Organization | Qwen | |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Feb 2026 | Jan 2025 |
| Context Window | 1.0M | 33K |
| Parameters | 7B | |
| License | Proprietary | Apache 2.0 |
| Pricing per 1M tokens | ||
| Input $/1M | $2.00 | |
| Output $/1M | $12.00 | |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Object Detection | Demo | |
| OCR | Demo | Demo |
| Vision Language | ||
| Visual Question Answering | Demo | Demo |
| Classification | Demo | |
| Model Features | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
| Foundation Vision | ||
Vision Evalspass/fail results · 66 prompts Score key:≥75%40–74%<40% | ||
| Overall Score | 75.76% | 52.24% |
| Avg Response Time | 6.13s | 47.64s |
| Median input tokensincl. image tokens | 1.1K | |
| Median output tokens | 11 | |
| Est. cost / taskon this benchmark | $0.0024 | |
| Defect Detection | 73.3%(11/15) | 60%(9/15) |
| Document Understanding | 88.9%(8/9) | 77.8%(7/9) |
| Object Counting | 44.4%(4/9) | 0%(0/10) |
| Object Understanding | 92.9%(13/14) | 57.1%(8/14) |
| Spatial Understanding | 73.7%(14/19) | 57.9%(11/19) |
Output tokens (incl. reasoning) and est. cost / task are measured on this benchmark from a single low-temperature run, and shown only for models whose run covered at least 90% of prompts. Methodology