Claude Sonnet 5 vs Qwen3.5 9b
Compare Claude Sonnet 5 and Qwen3.5 9b side-by-side. See how these vision models stack up in Open Prompt, OCR, and Image Captioning.
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Models in this comparison
Claude Sonnet 5 vs Qwen3.5 9b on Vision Evals
Qwen3.5 9b scores higher on 3 of the six Vision Evals tasks.
The widest gap is Data Extraction, where Claude Sonnet 5 leads 89.7% to 78.3%.
Overall, Claude Sonnet 5 averages 66.4% (#38 of 61) against 64.4% (#45 of 61) for Qwen3.5 9b.
Qwen3.5 9b is cheaper ($0.0021 vs $0.0064 per sample), while Claude Sonnet 5 is faster (4.8s vs 41.4s per sample).
Claude Sonnet 5 vs Qwen3.5 9b Comparison Table
Evals updated September 29, 2026Pricing updated October 3, 2026
| Property | Claude Sonnet 5 | Qwen3.5 9b |
|---|---|---|
| Organization | Anthropic | Qwen |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Jun 2026 | Mar 2026 |
| Context Window | 1.0M | 262K |
| Parameters | 9B | |
| License | Proprietary | Apache 2.0 |
| Pricing per 1M tokens | ||
| Input $/1M | $2.00 | $0.100 |
| Output $/1M | $10.00 | $0.150 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Chart Question Answering | ||
| Classification | Demo | |
| Document Question Answering | ||
| Image Tagging | ||
| Multi-Label Classification | ||
| Object Detection | Demo | |
| OCR | Demo | Demo |
| Vision Language | ||
| Visual Question Answering | Demo | Demo |
| Model Features | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort | ||
| Overall | 66.4% | 64.4% |
| Quantizationsself-hosted | ||
| Avg cost / sample | $0.0064 | $0.0021 |
| Avg speed / sample | 4.84s | 41.36s |
| By task | ||
| Object Detection | 36.1% | 38.1% ±5.7, Mean of 3 runs, range 33.5 to 44.9 |
| Counting | 56.8% | 56.8% ±1.4, Mean of 3 runs, range 55.4 to 58.1 |
| Identification | 81.3% | 83.3% ±1.6, Mean of 3 runs, range 81.3 to 84.4 |
| OCR | 91.7% | 84.2% ±0.9, Mean of 3 runs, range 83.0 to 84.9 |
| Data Extraction | 89.7% | 78.3% ±2.1, Mean of 3 runs, range 76.3 to 80.4 |
| Reasoning (low) | 43.0% | 45.9% ±1.7, Mean of 3 runs, range 44.4 to 47.7 |
| Reasoning (high) | 43.0% | – |
Claude Sonnet 5 vs Qwen3.5 9b: 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.
Qwen3.5-9B is a 9-billion-parameter multimodal foundation model developed by Alibaba Cloud's Qwen team, released on March 2, 2026 as part of the Qwen3.5 model family. Designed for efficient multimodal reasoning and long-context language tasks, it notably outperforms the older Qwen3-30B, a model more than three times its size, on key benchmarks including GPQA Diamond, IFEval, and LongBench.
The model supports vision-language inputs through an early-fusion multimodal architecture built on a dense hybrid foundation of Gated Delta Networks and Gated Attention. It can also operate in a text-only mode by skipping the vision encoder during inference. It provides a 262,144-token context window (extensible to ~1M tokens via YaRN) and is released under the Apache License 2.0. Within the current AI landscape, Qwen3.5-9B offers a strong balance of capability and efficiency, making it well-suited for multimodal assistants, document analysis, long-context reasoning, and developer-deployed agentic systems.
Frequently Asked Questions
On Roboflow's Vision Evals, Qwen3.5 9b performed better. It scores higher on 3 of the six vision tasks and averages 64.4% (#45 of 61) against 66.4% (#38 of 61) for Claude Sonnet 5. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
Yes. On the Vision Evals Data Extraction benchmark at low effort, Claude Sonnet 5 leads with 89.7% against 78.3%. This is the widest gap between the two models across the benchmark's tasks.
Qwen3.5 9b is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0021 per sample against $0.0064. Actual costs depend on your image sizes, prompts, and output length.
Claude Sonnet 5 is faster. Across Roboflow's Vision Evals it averaged 4.8s per inference against 41.4s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.
Yes. The comparison demo on this page runs both models on the same image side by side for open prompts and OCR in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.