Gemini 2.5 Pro vs Qwen3 VL 8B Instruct

Compare Gemini 2.5 Pro and Qwen3 VL 8B Instruct side-by-side. See how these vision models stack up in Open Prompt, OCR, and Image Captioning.

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GoogleGemini 2.5 Pro
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Gemini 2.5 Pro vs Qwen3 VL 8B Instruct: Overview

Gemini 2.5 Pro

Gemini 2.5 Pro, released on June 17, 2025, is Google DeepMind’s most capable model in the Gemini 2.5 family, optimized for deep reasoning, coding, and complex multimodal tasks. It accepts text, images, audio, video, and PDFs as input and outputs text. The model supports 1 million input tokens with an output capacity of up to 65K tokens, enabling large-scale comprehension of datasets, codebases, and technical documents. Its training knowledge extends to January 2025.

Pro outperforms earlier Gemini 2.0 models across benchmarks, including agentic coding tasks where it achieved ~63.8% on SWE-Bench Verified. It supports structured outputs, function calling, code execution, search grounding, and URL context, making it well-suited for enterprise, STEM, and developer workflows. However, it does not currently support image or audio generation in its stable release, and its higher computational cost and latency make it less efficient than Flash or Flash-Lite. It is available via the Gemini API, Google AI Studio, and Vertex AI.

Qwen3 VL 8B Instruct

Qwen3 VL 8B Instruct is an open-weight multimodal vision-language model developed by Qwen / Alibaba Cloud as part of the Qwen3-VL series, designed for instruction-following tasks that combine text with visual inputs such as images and video. Released around October 2025 under the Apache-2.0 license, it targets developers who need capable multimodal reasoning without the scale or cost of very large models.

The model contains roughly 8.8 billion dense parameters and supports text, image, and video understanding with strong spatial perception, visual reasoning, and emerging visual agent abilities such as GUI interaction. A standout feature is its native ~256K token context window, extendable to around 1M tokens, enabling long-document reading and extended video comprehension. In today’s landscape, it balances openness, long-context capacity, and solid multimodal performance against heavier proprietary models. Typical applications include multimodal assistants, document and video analysis, visual question answering, and research or product prototyping where transparency and deployability matter.

Gemini 2.5 Pro vs Qwen3 VL 8B Instruct Comparison Table

PropertyGemini 2.5 ProQwen3 VL 8B Instruct
OrganizationGoogleQwen
Categoryclosedopen
Modalitymultimodalmultimodal
Release DateJun 2025Oct 2025
Context Window1.0M256K
Parameters8.8B
LicenseProprietaryApache 2.0
Pricing per 1M tokens
Input $/1M$1.25$0.080
Output $/1M$10.00$0.500
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
70.15%
Avg Response Time11.87s
Median input tokensincl. image tokens294
Median output tokens565
Est. cost / taskon this benchmark$0.0060
Defect Detection
73.3%(11/15)
Document Understanding
88.9%(8/9)
Object Counting
20%(2/10)
Object Understanding
78.6%(11/14)
Spatial Understanding
78.9%(15/19)

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