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 Object Detection, Classification, Open Prompt, OCR, and Image Captioning.
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Models in this comparison
Gemini 3 Flash vs Qwen3.5 27B on Vision Evals
Gemini 3 Flash scores higher on 5 of the six Vision Evals tasks.
The widest gap is Reasoning, where Gemini 3 Flash leads 64.9% to 31.8%.
Overall, Gemini 3 Flash averages 74.9% (#10 of 25) against 64.3% (#22 of 25) for Qwen3.5 27B.
Qwen3.5 27B is cheaper ($0.0007 vs $0.0021 per sample), while Gemini 3 Flash is faster (4.1s vs 7.4s per sample).
Gemini 3 Flash vs Qwen3.5 27B Comparison Table
Evals updated August 6, 2026Pricing updated August 9, 2026
| Property | Gemini 3 Flash | Qwen3.5 27B |
|---|---|---|
| Organization | Qwen | |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Dec 2025 | Feb 2026 |
| Context Window | 1.0M | 262K |
| Parameters | 27B | |
| License | Proprietary | Apache 2.0 |
| Pricing per 1M tokens | ||
| Input $/1M | $0.500 | $0.195 |
| Output $/1M | $3.00 | $1.56 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Chart Question Answering | ||
| Classification | Demo | Demo |
| Document Question Answering | ||
| Image Tagging | ||
| Multi-Label Classification | ||
| Object Detection | Demo | Demo |
| OCR | Demo | Demo |
| Vision Language | ||
| Visual Question Answering | Demo | Demo |
| Model Features | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort | ||
| Overall | 74.9% | 64.3% |
| Avg cost / sample | $0.0021 | $0.0007 |
| Avg speed / sample | 4.10s | 7.38s |
| By task | ||
| Object Detection | 38.6% $0.0031 | 58.8% $0.0013 |
| Counting | 67.6% $0.0012 | 54.0% $0.0002 |
| Identification | 93.8% $0.0009 | 78.1% $0.0002 |
| OCR | 87.6% $0.0024 | 84.5% $0.0009 |
| Data Extraction | 96.9% $0.0008 | 78.3% $0.0002 |
| Reasoning (low) | 64.9% $0.0020 | 31.8% $0.0002 |
| Reasoning (high) | 74.2% $0.0040 | 61.6% $0.0065 |
Gemini 3 Flash vs Qwen3.5 27B: Overview
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 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.
Frequently Asked Questions
On Roboflow's Vision Evals, Gemini 3 Flash performed better. It scores higher on 5 of the six vision tasks and averages 74.9% (#10 of 25) against 64.3% (#22 of 25) for Qwen3.5 27B. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
Yes. On the Vision Evals Reasoning benchmark at low effort, Gemini 3 Flash leads with 64.9% against 31.8%. This is the widest gap between the two models across the benchmark's tasks.
Qwen3.5 27B is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0007 per sample against $0.0021. Gemini 3 Flash is priced at $0.50 per 1M input tokens and $3.00 per 1M output; Qwen3.5 27B is priced at $0.20 per 1M input tokens and $1.56 per 1M output. Actual costs depend on your image sizes, prompts, and output length.
Gemini 3 Flash is faster. Across Roboflow's Vision Evals it averaged 4.1s per inference against 7.4s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.
Yes. The comparison demo on this page runs both models on the same image side by side for object detection and image classification in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.