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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.

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GoogleGemini 3 Pro

Gemini 3 Pro is deprecated and can no longer be run. Details and evals are still available on its model page.

QwenQwen3.5 27B
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Gemini 3 Pro vs Qwen3.5 27B: Overview

Gemini 3 Pro

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

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

PropertyGemini 3 ProQwen3.5 27B
OrganizationGoogleQwen
Categoryclosedopen
Modalitymultimodalmultimodal
Release DateNov 2025Feb 2026
Context Window1.0M262K
Parameters27B
LicenseProprietaryApache 2.0
Pricing per 1M tokens
Input $/1M$0.195
Output $/1M$1.56
Vision Tasks
CaptioningDemo
Object Detection
OCRDemo
Vision Language
Visual Question AnsweringDemo
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 Time1.98s
Median input tokensincl. image tokens1.2K
Median output tokens7
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 Time8.51s
Median input tokensincl. image tokens126
Median output tokens107
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