GPT-5.4 Mini vs Qwen2.5 VL 7B Instruct

Compare GPT-5.4 Mini and Qwen2.5 VL 7B Instruct side-by-side. See how these vision models stack up in Open Prompt, Image Captioning, and OCR.

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OpenAIGPT-5.4 Mini
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QwenQwen2.5 VL 7B Instruct
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Models in this comparison

GPT-5.4 Mini vs Qwen2.5 VL 7B Instruct: Overview

GPT-5.4 Mini

GPT-5.4 mini is a fast, cost-efficient model developed by OpenAI and released on March 17, 2026, optimized for high-throughput workloads and subagent orchestration. It supports text and image inputs within a 400,000-token context window, making it ideal for processing extensive visual datasets and large codebases in a single request. Designed for low-latency production environments, the model integrates with key API features including function calling, web search, and tool-based computer use, allowing it to assist in automated workflows that require navigating digital interfaces.

Compared to the previous GPT-5 mini, this version runs more than twice as fast while approaching the performance levels of the flagship GPT-5.4 on reasoning and coding benchmarks. While the larger GPT-5.4 introduces native, state-of-the-art computer-use capabilities, GPT-5.4 mini provides a scalable alternative for interpreting screenshots and reasoning over dense UI layouts. For vision tasks on Playground, it excels at extracting structured information from visual documents and assisting in agentic tasks that involve real-time interpretation of software interfaces alongside text.

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-5.4 Mini vs Qwen2.5 VL 7B Instruct Comparison Table

PropertyGPT-5.4 MiniQwen2.5 VL 7B Instruct
OrganizationOpenAIQwen
Categoryclosedopen
Modalitymultimodalmultimodal
Release DateMar 2026Jan 2025
Context Window400K33K
Parameters7B
LicenseProprietaryApache 2.0
Pricing per 1M tokens
Input $/1M$0.750
Output $/1M$4.50
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
74.63%
52.24%
Avg Response Time7.87s47.64s
Defect Detection
80%(12/15)
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
78.9%(15/19)
57.9%(11/19)