GPT-5.4 vs Qwen2.5 VL 7B Instruct

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

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

GPT-5.4

GPT-5.4 is a proprietary multimodal large language model developed by OpenAI and released on March 5, 2026. It is designed for professional workloads such as advanced software development, research, and agentic automation. The model combines the general reasoning capabilities of the GPT-5 series with software engineering improvements derived from GPT-5.3-Codex. In the API and Codex environments it supports context windows of up to 1 million tokens, enabling long-context reasoning and large-scale code or document workflows.

Compared with GPT-5.2, GPT-5.4 reduces false individual claims by 33% and lowers overall response errors by 18%, improving factual reliability across complex tasks. It is also the first general-purpose OpenAI release with native computer-use capabilities, allowing agents to interact with desktops, browsers, and external applications to complete multi-step workflows. The model family includes three variants: GPT-5.4 (standard), GPT-5.4 Pro for higher-performance workloads, and GPT-5.4 Thinking, a reasoning-oriented version in ChatGPT that presents an upfront plan before generating its response. The API also introduces a Tool Search system that allows models to retrieve tool definitions dynamically, reducing token usage in tool-heavy integrations.

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 vs Qwen2.5 VL 7B Instruct Comparison Table

PropertyGPT-5.4Qwen2.5 VL 7B Instruct
OrganizationOpenAIQwen
Categoryclosedopen
Modalitymultimodalmultimodal
Release DateMar 2026Jan 2025
Context Window1.1M33K
Parameters7B
LicenseProprietaryApache 2.0
Pricing per 1M tokens
Input $/1M$2.50
Output $/1M$15.00
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
77.61%
52.24%
Avg Response Time7.16s47.64s
Median input tokensincl. image tokens1.4K
Median output tokens108
Est. cost / taskon this benchmark$0.0052
Defect Detection
86.7%(13/15)
60%(9/15)
Document Understanding
88.9%(8/9)
77.8%(7/9)
Object Counting
40%(4/10)
0%(0/10)
Object Understanding
85.7%(12/14)
57.1%(8/14)
Spatial Understanding
78.9%(15/19)
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