Claude Haiku 4.5 vs Qwen2.5 VL 7B Instruct

Compare Claude Haiku 4.5 and Qwen2.5 VL 7B Instruct side-by-side. See how these vision models stack up in Image Captioning, Open Prompt, and OCR.

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AnthropicClaude Haiku 4.5
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QwenQwen2.5 VL 7B Instruct
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Claude Haiku 4.5 vs Qwen2.5 VL 7B Instruct: Overview

Claude Haiku 4.5

Claude Haiku 4.5 is Anthropic’s lightweight model in the Claude 4.5 series, released in October 2025 under a proprietary license. Designed for speed and cost efficiency, it delivers near-frontier performance while maintaining Anthropic’s AI Safety Level 2 standard. Haiku 4.5 supports both text and multimodal (text and image) inputs, integrates tool use and extended reasoning, and features a 200,000 token context window, making it adept at handling long or complex workflows. Though the parameter count remains undisclosed, it achieves about 73.3% on SWE-bench Verified, reflecting strong coding and reasoning ability. Haiku 4.5 is ideal for developers and researchers seeking rapid, cost-effective model calls for analysis, coding, or multimodal understanding.

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.

Claude Haiku 4.5 vs Qwen2.5 VL 7B Instruct Comparison Table

PropertyClaude Haiku 4.5Qwen2.5 VL 7B Instruct
OrganizationAnthropicQwen
Categoryclosedopen
Modalitymultimodalmultimodal
Release DateOct 2025Jan 2025
Context Window200K33K
Parameters7B
LicenseProprietaryApache 2.0
Pricing per 1M tokens
Input $/1M$1.00
Output $/1M$5.00
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
52.24%
Avg Response Time47.64s
Defect Detection
60%(9/15)
Document Understanding
77.8%(7/9)
Object Counting
0%(0/10)
Object Understanding
57.1%(8/14)
Spatial Understanding
57.9%(11/19)