Gemini 3 Pro vs Qwen3.6 Plus
Compare Gemini 3 Pro and Qwen3.6 Plus side-by-side. See how these vision models stack up in OCR, Image Captioning, and Open Prompt.
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Gemini 3 Pro is deprecated and can no longer be run. Details and evals are still available on its model page.
Models in this comparison
Gemini 3 Pro vs Qwen3.6 Plus: Overview
Gemini 3 Pro is Google DeepMind’s flagship multimodal frontier model, built for high-accuracy reasoning and large-scale context understanding across text, images, audio, video, code, and documents. It delivers major gains over Gemini 2.5 Pro, supported by a 1M-token window and strong performance on Google-reported benchmarks such as GPQA Diamond, MMMU-Pro, and Video-MMMU.
The model excels at structured outputs, tool use, and agentic coding, enabling complex multi-step workflows and analysis of entire books, codebases, or long videos in a single prompt. Positioned as Google’s top production model, it balances advanced reasoning with broad multimodal capabilities, making it well suited for research assistants, automation agents, coding systems, and enterprise-scale document and media analysis.
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 Pro vs Qwen3.6 Plus Comparison Table
| Property | Gemini 3 Pro | Qwen3.6 Plus |
|---|---|---|
| Organization | Qwen | |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Nov 2025 | Apr 2026 |
| Context Window | 1.0M | 1.0M |
| Parameters | ||
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $0.325 | |
| Output $/1M | $1.95 | |
| Vision Tasks | ||
| Captioning | Demo | |
| Object Detection | ||
| OCR | Demo | |
| Vision Language | ||
| Visual Question Answering | Demo | |
| Classification | ||
| Model Features | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
| Foundation Vision | ||
Vision Evalspass/fail results · 67 prompts Score key:≥75%40–74%<40% | ||
| Visual Understanding | ||
| Overall Score | 68.66% | |
| Avg Response Time | 34.17s | |
| Median input tokensincl. image tokens | 1.2K | |
| Median output tokens | 47 | |
| Est. cost / taskon this benchmark | $0.0005 | |
| Defect Detection | 86.7%(13/15) | |
| Document Understanding | 77.8%(7/9) | |
| Object Counting | 20%(2/10) | |
| Object Understanding | 78.6%(11/14) | |
| Spatial Understanding | 68.4%(13/19) | |
| OCR | ||
| Overall Score | 58.52% | |
| Avg Response Time | 5.49s | |
| Median input tokensincl. image tokens | 124 | |
| Median output tokens | 18 | |
| Est. cost / taskon this benchmark | $0.0001 | |
| Focused Scene OCR | 76.8%(76/99) | |
| Handwritten Math | 80%(8/10) | |
| License Plate Recognition | 13.3%(4/30) | |
| Text Recognition | 50%(15/30) | |
| VQA & Extraction | 51.7%(31/60) | |
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