Gemini 3.6 Flash vs Qwen3.5-27B
Compare Gemini 3.6 Flash and Qwen3.5-27B side-by-side. See how these vision models stack up in Open Prompt, Classification, Image Captioning, OCR, and Object Detection.
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Models in this comparison
Gemini 3.6 Flash vs Qwen3.5-27B on Vision Evals
Gemini 3.6 Flash scores higher on all six Vision Evals tasks.
The widest gap is Reasoning, where Gemini 3.6 Flash leads 77.7% to 58.3%.
Overall, Gemini 3.6 Flash averages 83.0% (#6 of 52) against 71.4% (#21 of 52) for Qwen3.5-27B.
Gemini 3.6 Flash is both cheaper ($0.0032 vs $0.0041 per sample) and faster (14.7s vs 77.9s per sample).
Gemini 3.6 Flash vs Qwen3.5-27B Comparison Table
Evals updated September 3, 2026Pricing updated September 4, 2026
| Property | Gemini 3.6 Flash | Qwen3.5-27B |
|---|---|---|
| Organization | Qwen | |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Jul 2026 | Feb 2026 |
| Context Window | 1.0M | 262K |
| Parameters | 27B | |
| License | Proprietary | Apache 2.0 |
| Pricing per 1M tokens | ||
| Input $/1M | $0.750 | $0.195 |
| Output $/1M | $3.75 | $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 |
| Video Classification | ||
| Model Features | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort | ||
| Overall | 83.0% | 71.4% |
| Quantizationsself-hosted | ||
| Avg cost / sample | $0.0032 | $0.0041 |
| Avg speed / sample | 14.66s | 77.88s |
| By task | ||
| Object Detection (low) | 57.1% ±1.7, Mean of 3 runs, range 55.9 to 59.4 | 52.4% |
| Object Detection (high) | 70.7% ±0.4, Mean of 3 runs, range 70.3 to 71.2 | – |
| Counting (low) | 80.2% ±2.0, Mean of 3 runs, range 78.4 to 82.4 | 67.6% |
| Counting (high) | 79.3% ±2.7, Mean of 3 runs, range 77.0 to 82.4 | – |
| Identification (low) | 99.0% ±1.6, Mean of 3 runs, range 96.9 to 100.0 | 84.4% |
| Identification (high) | 100.0% ±0.0, Mean of 3 runs, range 100.0 to 100.0 | – |
| OCR (low) | 88.2% ±0.3, Mean of 3 runs, range 87.9 to 88.4 | 83.0% |
| OCR (high) | 89.5% ±0.0, Mean of 3 runs, range 89.5 to 89.6 | – |
| Data Extraction (low) | 95.9% ±1.0, Mean of 3 runs, range 94.8 to 96.9 | 82.7% |
| Data Extraction (high) | 94.8% ±1.0, Mean of 3 runs, range 93.8 to 95.9 | – |
| Reasoning (low) | 77.7% ±2.0, Mean of 3 runs, range 76.2 to 80.1 | 58.3% |
| Reasoning (high) | 81.0% ±2.0, Mean of 3 runs, range 79.5 to 83.4 | – |
Gemini 3.6 Flash vs Qwen3.5-27B: Overview
Gemini 3.6 Flash is a multimodal language model from Google DeepMind, positioned as the workhorse tier in the Gemini 3.x family. It accepts text, image, video, audio, and PDF inputs with a 1 million token context window and produces up to 64,000 output tokens. The model builds directly on Gemini 3.5 Flash, incorporating developer and customer feedback to improve token efficiency, coding quality, and knowledge work performance. According to the Artificial Analysis Index, it consumes 17% fewer output tokens than its predecessor, and on some benchmarks such as DeepSWE, token reduction reaches up to 65%. It supports function calling, structured output, search as a tool, and code execution, and includes computer use as a built-in capability in the Gemini API and Gemini Enterprise.
On coding benchmarks, Gemini 3.6 Flash scores 49% on DeepSWE versus 37% for 3.5 Flash, and 63.9% on MLE Bench versus 49.7%. Computer use performance on OSWorld-Verified improves from 78.4% to 83%, and knowledge work scores on GDPval-AA v2 rise from 1349 to 1421. The model carries a knowledge cutoff of March 2026 and ships with enhanced Frontier Safety safeguards covering chemical, biological, radiological, nuclear, and cyber offense domains, with training to minimize refusals for beneficial uses. It is a proprietary, closed-weights model available in preview through the Gemini API via Google AI Studio, Android Studio, Google Antigravity, Gemini Enterprise, and the Gemini app.
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 3.6 Flash performed better. It scores higher on all six vision tasks and averages 83.0% (#6 of 52) against 71.4% (#21 of 52) 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 3.6 Flash leads with 77.7% against 58.3%. This is the widest gap between the two models across the benchmark's tasks.
Gemini 3.6 Flash is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0032 per sample against $0.0041. Actual costs depend on your image sizes, prompts, and output length.
Gemini 3.6 Flash is faster. Across Roboflow's Vision Evals it averaged 14.7s per inference against 77.9s. 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 open prompts and image classification in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.