Gemini 3 Flash vs Qwen2.5 VL 7B Instruct
Compare Gemini 3 Flash and Qwen2.5 VL 7B Instruct side-by-side. See how these vision models stack up in Open Prompt, OCR, and Image Captioning.
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Gemini 3 Flash vs Qwen2.5 VL 7B Instruct: 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.
Qwen2.5-VL-7B-Instruct is a 7-billion parameter vision-language model from Alibaba’s QwenLM team, released on January 26, 2025 under the Apache 2.0 license. It is the instruction-tuned variant of the 7B scale in the Qwen2.5-VL family, designed to process multimodal inputs such as text, images, charts, documents, and video. The model enables structured outputs—including JSON for structured content and bounding boxes for visual localization. Weights are publicly available on Hugging Face and GitHub, making it suitable for both research and applied multimodal use.
Gemini 3 Flash vs Qwen2.5 VL 7B Instruct Comparison Table
| Property | Gemini 3 Flash | Qwen2.5 VL 7B Instruct |
|---|---|---|
| Organization | Qwen | |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Dec 2025 | Jan 2025 |
| Context Window | 1.0M | 33K |
| Parameters | 7B | |
| License | Proprietary | Apache 2.0 |
| Pricing per 1M tokens | ||
| Input $/1M | $0.500 | |
| Output $/1M | $3.00 | |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Object Detection | Demo | |
| OCR | Demo | Demo |
| Vision Language | ||
| Visual Question Answering | Demo | Demo |
| Classification | Demo | |
| 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% | 52.24% |
| Avg Response Time | 9.85s | 47.64s |
| Median input tokensincl. image tokens | 1.1K | |
| Median output tokens | 290 | |
| Est. cost / taskon this benchmark | $0.0014 | |
| Defect Detection | 73.3%(11/15) | 60%(9/15) |
| Document Understanding | 88.9%(8/9) | 77.8%(7/9) |
| Object Counting | 30%(3/10) | 0%(0/10) |
| Object Understanding | 85.7%(12/14) | 57.1%(8/14) |
| Spatial Understanding | 84.2%(16/19) | 57.9%(11/19) |
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