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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Claude Haiku 4.5 vs Qwen2.5 VL 7B Instruct: Overview
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 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
| Property | Claude Haiku 4.5 | Qwen2.5 VL 7B Instruct |
|---|---|---|
| Organization | Anthropic | Qwen |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Oct 2025 | Jan 2025 |
| Context Window | 200K | 33K |
| Parameters | 7B | |
| License | Proprietary | Apache 2.0 |
| Pricing per 1M tokens | ||
| Input $/1M | $1.00 | |
| Output $/1M | $5.00 | |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Object Detection | Demo | |
| OCR | Demo | Demo |
| Vision Language | ||
| Visual Question Answering | Demo | Demo |
| Classification | Demo | |
| 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 Time | 47.64s | |
| 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) | |