Claude Sonnet 5 vs Qwen3 VL 235B A22B Instruct
Compare Claude Sonnet 5 and Qwen3 VL 235B A22B Instruct side-by-side. See how these vision models stack up in Object Detection, Open Prompt, OCR, Classification, and Image Captioning.
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Models in this comparison
Claude Sonnet 5 vs Qwen3 VL 235B A22B Instruct on Vision Evals
Claude Sonnet 5 scores higher on 3 of the five Vision Evals tasks.
The widest gap is Object Detection, where Qwen3 VL 235B A22B Instruct leads 52.1% to 36.1%.
Overall, Claude Sonnet 5 averages 55.3% (#47 of 63) against 55.2% (#48 of 63) for Qwen3 VL 235B A22B Instruct.
Qwen3 VL 235B A22B Instruct is cheaper ($0.0011 vs $0.0074 per sample), while Claude Sonnet 5 is faster (5.9s vs 15.9s per sample).
Claude Sonnet 5 vs Qwen3 VL 235B A22B Instruct Comparison Table
Evals updated October 13, 2026Pricing updated October 19, 2026
| Property | Claude Sonnet 5 | Qwen3 VL 235B A22B Instruct |
|---|---|---|
| Organization | Anthropic | Qwen |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Jun 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 | $2.00 | $0.210 |
| Output $/1M | $10.00 | $1.90 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Chart Question Answering | Supported | Supported |
| Classification | Demo | Demo |
| Document Question Answering | Supported | Supported |
| Image Tagging | Supported | Supported |
| Multi-Label Classification | Supported | Supported |
| Object Detection | Demo | Demo |
| OCR | Demo | Demo |
| Vision Language | Supported | Supported |
| Visual Question Answering | Demo | Demo |
| Model Features | ||
| Foundation Vision | Supported | Supported |
| LLMs with Vision Capabilities | Supported | Supported |
| Multimodal Vision | Supported | Supported |
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 55.3% | 55.2% |
| Avg cost / sample | $0.0074 | $0.0011 |
| Avg speed / sample | 5.86s | 15.87s |
| By task | ||
| Object Detection | 36.1% | 52.1% |
| Counting | 56.8% | 47.3% |
| Identification | 81.3% | 90.6% |
| OCR (low) | 59.4% | 56.4% |
| by category |
|
|
| OCR (high) | 59.5% | 55.5% |
| by category |
|
|
| Reasoning (low) | 43.0% | 29.8% |
| Reasoning (high) | 43.0% | 33.8% |
Claude Sonnet 5 vs Qwen3 VL 235B A22B Instruct: Overview
Claude Sonnet 5 is a mid-tier large language model from Anthropic, released on June 30, 2026, as the latest model in the Sonnet series and a direct successor to Claude Sonnet 4.6. It is a hybrid reasoning model designed primarily for agentic workflows, software coding, and professional tasks. The model features a 1 million token context window, a 128k maximum output token limit, and runs adaptive thinking by default, giving API users fine-grained control over reasoning effort across five levels (low, medium, high, max, and extra-high). It uses an updated tokenizer shared with Opus 4.7 and later models, which produces approximately 30% more tokens for equivalent text compared to earlier Claude models. On benchmarks, Sonnet 5 scores 63.2% on agentic coding and 81.2% on OSWorld, narrowing the gap with Opus 4.8 while remaining at Sonnet-tier pricing.
The model supports text and image input with text output, and accepts tools including browsers and terminals for autonomous multi-step task execution. Anthropic's safety evaluations report that Sonnet 5 shows a lower rate of undesirable behaviors than Sonnet 4.6 and is generally safer in agentic contexts, with improved resistance to prompt injection and reduced sycophancy. Cybersecurity safeguards equivalent to those on Opus 4.7 and 4.8 are active, though Anthropic notes the model was not deliberately trained on cybersecurity tasks. The model is proprietary and API-only, with no open weights.
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.