Gemini 3 Flash vs Qwen3.5 27B

Compare Gemini 3 Flash and Qwen3.5 27B side-by-side. See how these vision models stack up in Open Prompt, OCR, and Image Captioning.

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GoogleGemini 3 Flash
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QwenQwen3.5 27B
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Gemini 3 Flash vs Qwen3.5 27B: Overview

Gemini 3 Flash

Gemini 3 Flash is a proprietary multimodal large language model developed by Google through Google DeepMind, designed to deliver fast, cost-efficient reasoning across real-time products and developer workflows. Released in December 2025, it is the Flash-tier variant of the Gemini 3 family, balancing low latency with reasoning quality approaching Pro models.

The model supports text, images, audio, and video, with an exceptionally large context window of roughly one million input tokens and outputs up to ~65k tokens. It emphasizes rapid responses for coding, summarization, analysis, and agentic tasks, and exposes configurable “thinking levels” via API to trade speed for deeper reasoning. Today, Gemini 3 Flash positions itself as a high-throughput, production-ready model, serving as the default in the Gemini app and Google Search’s AI Mode, optimized for scalable, interactive AI applications.

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 Flash vs Qwen3.5 27B Comparison Table

PropertyGemini 3 FlashQwen3.5 27B
OrganizationGoogleQwen
Categoryclosedopen
Modalitymultimodalmultimodal
Release DateDec 2025Feb 2026
Context Window1.0M262K
Parameters27B
LicenseProprietaryApache 2.0
Pricing per 1M tokens
Input $/1M$0.500$0.195
Output $/1M$3.00$1.56
Vision Tasks
CaptioningDemoDemo
Object DetectionDemo
OCRDemoDemo
Vision Language
Visual Question AnsweringDemoDemo
ClassificationDemo
Model Features
LLMs with Vision Capabilities
Multimodal Vision
Foundation Vision
Vision Evalspass/fail results · 67 prompts
Score key:≥75%40–74%<40%
Overall Score
74.63%
71.64%
Avg Response Time9.85s1.98s
Median input tokensincl. image tokens1.1K1.2K
Median output tokens2907
Est. cost / taskon this benchmark$0.0014$0.0002
Defect Detection
73.3%(11/15)
80%(12/15)
Document Understanding
88.9%(8/9)
77.8%(7/9)
Object Counting
30%(3/10)
40%(4/10)
Object Understanding
85.7%(12/14)
78.6%(11/14)
Spatial Understanding
84.2%(16/19)
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