GPT-5 Mini vs Qwen2.5 VL 7B Instruct

Compare GPT-5 Mini and Qwen2.5 VL 7B Instruct side-by-side. See how these vision models stack up in Image Captioning, OCR, and Open Prompt.

Compare GPT-5 Mini vs Qwen2.5 VL 7B Instruct live

Run the same image across every model that supports a task and compare their outputs side-by-side.

Extract and compare text from images across multiple models.

Open OCR in the full playground
OpenAIGPT-5 Mini
Run to compare this model.
QwenQwen2.5 VL 7B Instruct
Run to compare this model.

Models in this comparison

GPT-5 Mini vs Qwen2.5 VL 7B Instruct: Overview

GPT-5 Mini

GPT-5 Mini, released by OpenAI on August 7, 2025, is a mid-tier variant of the GPT-5 family that balances cost, speed, and capability. It is multimodal, supporting both text and image inputs, and offers a substantial input context window of ~400,000 tokens with output lengths up to ~128,000 tokens. While less powerful than the full GPT-5, it inherits its safety tuning, instruction-following improvements, and multimodal reasoning, making it a practical choice for developers who need large context handling without the expense of premium models.

GPT-5 Mini is optimized for affordability while retaining strong reasoning performance. Benchmarks show it outperforming earlier models such as GPT-4o on many multimodal and medical VQA tasks, though it lags behind GPT-5 on the most complex problems. Ideal use cases include prototyping, scalable content generation, document analysis, and mid-range reasoning tasks where efficiency and context capacity matter more than top-tier accuracy.

Qwen2.5 VL 7B Instruct

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.

GPT-5 Mini vs Qwen2.5 VL 7B Instruct Comparison Table

PropertyGPT-5 MiniQwen2.5 VL 7B Instruct
OrganizationOpenAIQwen
Categoryclosedopen
Modalitymultimodalmultimodal
Release DateAug 2025Jan 2025
Context Window400K33K
Parameters7B
LicenseProprietaryApache 2.0
Pricing per 1M tokens
Input $/1M$0.250
Output $/1M$2.00
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
73.13%
52.24%
Avg Response Time11.72s47.64s
Median input tokensincl. image tokens1.4K
Median output tokens143
Est. cost / taskon this benchmark$0.0006
Defect Detection
80%(12/15)
60%(9/15)
Document Understanding
77.8%(7/9)
77.8%(7/9)
Object Counting
10%(1/10)
0%(0/10)
Object Understanding
85.7%(12/14)
57.1%(8/14)
Spatial Understanding
89.5%(17/19)
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