Claude Opus 4.6 vs Qwen2.5 VL 7B Instruct
Compare Claude Opus 4.6 and Qwen2.5 VL 7B Instruct side-by-side. See how these vision models stack up in Open Prompt, OCR, and Image Captioning.
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Claude Opus 4.6 vs Qwen2.5 VL 7B Instruct: Overview
Claude Opus 4.6 is the flagship large language model from Anthropic, released on 2026-02-05 for advanced reasoning, complex coding, and enterprise agent workflows. It supports text and image inputs via API, offers a 200K-token standard context window with a 1M-token beta option, and enables outputs up to 128K tokens, with adaptive reasoning and context compaction for sustained tasks.
As of 2026-02-17, Anthropic also released Claude Sonnet 4.6, extending the 1M-token context window to a broader tier. Opus remains positioned for maximum depth and benchmark performance, while Sonnet 4.6 brings long-context capability to more cost- and latency-sensitive production use cases.
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 Opus 4.6 vs Qwen2.5 VL 7B Instruct Comparison Table
| Property | Claude Opus 4.6 | Qwen2.5 VL 7B Instruct |
|---|---|---|
| Organization | Anthropic | Qwen |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Feb 2026 | Jan 2025 |
| Context Window | 1.0M | 33K |
| Parameters | 7B | |
| License | Proprietary | Apache 2.0 |
| Pricing per 1M tokens | ||
| Input $/1M | $5.00 | |
| Output $/1M | $25.00 | |
| 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 | 64.18% | 52.24% |
| Avg Response Time | 23.35s | 47.64s |
| Median input tokensincl. image tokens | 2.2K | |
| Median output tokens | 130 | |
| Est. cost / taskon this benchmark | $0.014 | |
| Defect Detection | 73.3%(11/15) | 60%(9/15) |
| Document Understanding | 77.8%(7/9) | 77.8%(7/9) |
| Object Counting | 20%(2/10) | 0%(0/10) |
| Object Understanding | 71.4%(10/14) | 57.1%(8/14) |
| Spatial Understanding | 68.4%(13/19) | 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