Gemini 3.1 Pro vs Qwen3.6 Plus
Compare Gemini 3.1 Pro and Qwen3.6 Plus side-by-side. See how these vision models stack up in Image Captioning, Open Prompt, and OCR.
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Models in this comparison
Gemini 3.1 Pro vs Qwen3.6 Plus: 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.
Qwen3.6 Plus is a flagship model in Alibaba’s Qwen Plus series, designed for agentic workflows, coding, and multi-step reasoning. It supports a 1 million token context window and up to 65,536 output tokens, with built-in reasoning capabilities. The model is available as a hosted, proprietary API through Alibaba Cloud.
Compared to Qwen3.5, it improves reliability in multi-step execution and frontend code generation, with stronger performance on agentic coding tasks. It also supports document and image understanding, though its vision capabilities are more limited than dedicated Qwen-VL models. Qwen3.6 Plus is part of a broader Qwen ecosystem that includes both closed-source APIs and open-weight models.
Gemini 3.1 Pro vs Qwen3.6 Plus Comparison Table
| Property | Gemini 3.1 Pro | Qwen3.6 Plus |
|---|---|---|
| Organization | Qwen | |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Feb 2026 | Apr 2026 |
| Context Window | 1.0M | 1.0M |
| Parameters | ||
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $2.00 | $0.325 |
| Output $/1M | $12.00 | $1.95 |
| 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% | 68.66% |
| Avg Response Time | 6.13s | 34.17s |
| Median input tokensincl. image tokens | 1.1K | 1.2K |
| Median output tokens | 11 | 47 |
| Est. cost / taskon this benchmark | $0.0024 | $0.0005 |
| Defect Detection | 73.3%(11/15) | 86.7%(13/15) |
| Document Understanding | 88.9%(8/9) | 77.8%(7/9) |
| Object Counting | 44.4%(4/9) | 20%(2/10) |
| Object Understanding | 92.9%(13/14) | 78.6%(11/14) |
| Spatial Understanding | 73.7%(14/19) | 68.4%(13/19) |
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