Gemini 3 Pro vs Qwen3.5 27B
Compare Gemini 3 Pro and Qwen3.5 27B side-by-side. See how these vision models stack up in OCR, Image Captioning, and Open Prompt.
Compare Gemini 3 Pro vs Qwen3.5 27B live
Run the same image across every model that supports a task and compare their outputs side-by-side.
Extract and compare text from images across multiple models.
Upload an image
Drag and drop an image here, or click to browse
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 Qwen3.5 27B: 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.
Qwen3.5-27B is a multimodal dense hybrid model developed by Alibaba Cloud’s Qwen team and released in February 2026 as a high-precision entry in the Qwen3.5 "Medium" series. Unlike its Mixture-of-Experts (MoE) siblings, the 27B model utilizes a dense architecture combining Gated Delta Networks with a feed-forward structure, activating its full parameter suite for every inference to maximize reliability. This design provides the highest instruction-following and coding accuracy in its class, with a notable IFEval score of 95.0. The model features a native 262K-token context window, extensible to 1M tokens via YaRN (RoPE scaling), and is released under the Apache-2.0 license.
Optimized for agentic workflows, Qwen3.5-27B employs an early-fusion architecture that treats visual and textual data as a unified stream for deep cross-modal reasoning. This unified approach allows the model to excel in technical analysis and software engineering, matching GPT-5-mini with a 72.4% score on SWE-bench Verified. While the larger MoE variants in the family lead in raw knowledge benchmarks, the 27B model offers a stable and high-density alternative for structured data extraction and spatial perception, contributing to the Qwen3.5 family’s generational leap in OCR accuracy over the previous Qwen3-VL series.
Gemini 3 Pro vs Qwen3.5 27B Comparison Table
| Property | Gemini 3 Pro | Qwen3.5 27B |
|---|---|---|
| Organization | Qwen | |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Nov 2025 | Feb 2026 |
| Context Window | 1.0M | 262K |
| Parameters | 27B | |
| License | Proprietary | Apache 2.0 |
| Pricing per 1M tokens | ||
| Input $/1M | $0.195 | |
| Output $/1M | $1.56 | |
| 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 | 71.64% | |
| Avg Response Time | 1.98s | |
| Median input tokensincl. image tokens | 1.2K | |
| Median output tokens | 7 | |
| Est. cost / taskon this benchmark | $0.0002 | |
| Defect Detection | 80%(12/15) | |
| Document Understanding | 77.8%(7/9) | |
| Object Counting | 40%(4/10) | |
| Object Understanding | 78.6%(11/14) | |
| Spatial Understanding | 73.7%(14/19) | |
| OCR | ||
| Overall Score | 85.59% | |
| Avg Response Time | 8.51s | |
| Median input tokensincl. image tokens | 126 | |
| Median output tokens | 107 | |
| Est. cost / taskon this benchmark | $0.0002 | |
| Focused Scene OCR | 84.8%(84/99) | |
| Handwritten Math | 100%(10/10) | |
| License Plate Recognition | 93.3%(28/30) | |
| Text Recognition | 80%(24/30) | |
| VQA & Extraction | 83.3%(50/60) | |
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