Claude Opus 4.7 vs Qwen3 VL 235B A22B Instruct
Qwen3 VL 235B A22B Instruct: 55.2% average across five Vision Evals tasks (#50 of 65 models). We have not run Claude Opus 4.7 through Roboflow's Vision Evals, so there is no head-to-head score. Run both on your own image below.
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Claude Opus 4.7 vs Qwen3 VL 235B A22B Instruct Comparison Table
Evals updated October 26, 2026Pricing updated November 6, 2026
| Property | Claude Opus 4.7 | Qwen3 VL 235B A22B Instruct |
|---|---|---|
| Organization | Anthropic | Qwen |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Apr 2026 | Sep 2025 |
| Context Window | 1.0M | 256K |
| Parameters | Unknown | 235B |
| License | Proprietary | Apache 2.0 |
| Commercial use | commercial use per vendor terms | free for commercial use |
| Pricing per 1M tokens | ||
| Input $/1M | $5.00 | $0.210 |
| Output $/1M | $25.00 | $1.90 |
| Vision Tasks | ||
| Model Features | ||
Both support 3 more features | ||
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | Not evaluated | 55.2% |
| Avg cost / sample | – | $0.0011 |
| Avg speed / sample | – | 15.87s |
| By task | ||
| Object Detection | – | 52.1% |
| Counting | – | 47.3% |
| Identification | – | 90.6% |
| OCR (low) | – | 56.4% |
| by category |
| |
| OCR (high) | – | 55.5% |
| by category |
| |
| Reasoning (low) | – | 29.8% |
| Reasoning (high) | – | 33.8% |
Claude Opus 4.7 vs Qwen3 VL 235B A22B Instruct: Overview
Claude Opus 4.7 is a proprietary multimodal language model developed by Anthropic, released on April 16, 2026. It is designed for agentic coding, long-horizon task execution, and enterprise knowledge work. The model supports text and vision inputs and operates with a context window of up to 1,000,000 tokens. It introduces adaptive thinking, which dynamically allocates reasoning based on task complexity, along with configurable effort controls including a new xhigh setting that sits between the existing high and max levels.
Compared to Claude Opus 4.6, version 4.7 shows improved instruction following and higher reliability in extended agentic tasks. Vision capabilities now support high-resolution inputs up to 2,576px on the long edge (~3.75 megapixels), more than three times the resolution of prior Claude models, enabling finer interpretation of dense diagrams, UI screenshots, and document layouts. These improvements, combined with self-verification on long-running tasks and a new task budget system for controlling agentic loops, make it well-suited for complex software engineering, technical analysis, and multimodal vision workflows.
Qwen3 VL 235B A22B Instruct is a flagship multimodal vision-language model developed by Qwen (Alibaba Cloud), designed for instruction-following tasks that combine advanced text generation with visual understanding. It serves as a high-end open-weight model for developers and researchers building multimodal AI systems that require strong reasoning, perception, and long-context capabilities.
The model supports interleaved text and image inputs, very long context windows (up to roughly 256K tokens), and efficient inference through a mixture-of-experts architecture with about 22B active parameters out of 235B total. In today’s landscape, it competes with top-tier proprietary vision-language models while offering the advantages of open weights and flexible deployment. Typical applications include multimodal assistants, document and image analysis, visual reasoning, and large-context instruction-based workflows.