Gemini 3.1 Pro vs Qwen VL Max
Compare Gemini 3.1 Pro and Qwen VL Max 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 Qwen VL Max: 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.
Qwen-VL-Max is a proprietary vision-language model developed by Alibaba’s QwenLM team. Released on February 1, 2025, it is the flagship offering in the Qwen-VL family and sits above the VL-Plus tier in capability.
The model supports text and image inputs and provides a context window of up to 131,072 tokens (with a maximum input size of 129,024 tokens), according to Alibaba Cloud Model Studio. While the parameter count for VL-Max has not been publicly disclosed, the broader Qwen2.5-VL series includes open-weight models scaling up to 72B parameters.
Qwen-VL-Max is optimized for advanced multimodal applications such as document parsing, visual reasoning, multilingual analysis, and structured data extraction. Unlike the open Qwen2.5-VL variants, VL-Max is not available as open weights.
Gemini 3.1 Pro vs Qwen VL Max Comparison Table
| Property | Gemini 3.1 Pro | Qwen VL Max |
|---|---|---|
| Organization | Qwen | |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Feb 2026 | Feb 2025 |
| Context Window | 1.0M | 131K |
| Parameters | ||
| License | Proprietary | Proprietary |
| 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% | |
| Avg Response Time | 6.13s | |
| Median input tokensincl. image tokens | 1.1K | |
| Median output tokens | 11 | |
| Est. cost / taskon this benchmark | $0.0024 | |
| Defect Detection | 73.3%(11/15) | |
| Document Understanding | 88.9%(8/9) | |
| Object Counting | 44.4%(4/9) | |
| Object Understanding | 92.9%(13/14) | |
| Spatial Understanding | 73.7%(14/19) | |
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