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Grok 4 vs Qwen2.5 VL 7B Instruct

Compare Grok 4 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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GrokGrok 4
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Grok 4 vs Qwen2.5 VL 7B Instruct: Overview

Grok 4

Grok 4, released by xAI on July 9, 2025, is the fourth-generation model in the Grok family and the most advanced to date. It is multimodal, supporting text, vision, tool use, and real-time web search, with a reported 256,000-token context window for long-form reasoning and document analysis. Its training data extends through November 2024, making it the most up-to-date Grok model at launch.

The lineup includes Grok 4 Generalist for broad tasks, Grok 4 Heavy for higher-capacity reasoning, and Grok 4 Code optimized for programming and debugging. A notable feature is its always-on “Think” mode, designed for deeper multi-step reasoning. While xAI has not disclosed parameter counts, Grok 4 is positioned to compete with frontier models like GPT-5 and Claude 4, balancing real-time knowledge via web integration with structured tool use. It is best suited for coding, complex reasoning, and multimodal AI assistants.

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.

Grok 4 vs Qwen2.5 VL 7B Instruct Comparison Table

PropertyGrok 4Qwen2.5 VL 7B Instruct
OrganizationxAIQwen
Categoryclosedopen
Modalitymultimodalmultimodal
Release DateJul 2025Jan 2025
Context Window256K33K
Parameters7B
LicenseProprietaryApache 2.0
Vision Tasks
CaptioningDemoDemo
Object Detection
OCRDemoDemo
Vision Language
Visual Question AnsweringDemoDemo
Classification
Model Features
LLMs with Vision Capabilities
Multimodal Vision
Foundation Vision
Vision Evalspass/fail results · 67 prompts
Score key:≥75%40–74%<40%
Visual Understanding
Overall Score
52.24%
52.24%
Avg Response Time85.24s47.64s
Defect Detection
80%(12/15)
60%(9/15)
Document Understanding
44.4%(4/9)
77.8%(7/9)
Object Counting
10%(1/10)
0%(0/10)
Object Understanding
57.1%(8/14)
57.1%(8/14)
Spatial Understanding
52.6%(10/19)
57.9%(11/19)