Claude Sonnet 5 vs Qwen3.6 Plus
Compare Claude Sonnet 5 and Qwen3.6 Plus side-by-side. See how these vision models stack up in Open Prompt, OCR, and Image Captioning.
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Claude Sonnet 5 vs Qwen3.6 Plus: 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.
Qwen3.6 Plus is a flagship model in Alibaba’s Qwen Plus series, designed for agentic workflows, coding, and multi-step reasoning. It supports a 1 million token context window and up to 65,536 output tokens, with built-in reasoning capabilities. The model is available as a hosted, proprietary API through Alibaba Cloud.
Compared to Qwen3.5, it improves reliability in multi-step execution and frontend code generation, with stronger performance on agentic coding tasks. It also supports document and image understanding, though its vision capabilities are more limited than dedicated Qwen-VL models. Qwen3.6 Plus is part of a broader Qwen ecosystem that includes both closed-source APIs and open-weight models.
Claude Sonnet 5 vs Qwen3.6 Plus Comparison Table
| Property | Claude Sonnet 5 | Qwen3.6 Plus |
|---|---|---|
| Organization | Anthropic | Qwen |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Jun 2026 | Apr 2026 |
| Context Window | 1.0M | 1.0M |
| Parameters | ||
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $2.00 | $0.325 |
| Output $/1M | $10.00 | $1.95 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Object Detection | Demo | |
| OCR | Demo | Demo |
| Vision Language | ||
| Visual Question Answering | Demo | Demo |
| Classification | Demo | |
| Document Question Answering | ||
| Multi-Label Classification | ||
| Model Features | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
Vision Evalspass/fail results · 67 prompts Score key:≥75%40–74%<40% | ||
| Visual Understanding | ||
| Overall Score | 70.15% | 68.66% |
| Avg Response Time | 3.90s | 34.17s |
| Median input tokensincl. image tokens | 2.1K | 1.2K |
| Median output tokens | 61 | 47 |
| Est. cost / taskon this benchmark | $0.0048 | $0.0005 |
| Defect Detection | 73.3%(11/15) | 86.7%(13/15) |
| Document Understanding | 66.7%(6/9) | 77.8%(7/9) |
| Object Counting | 20%(2/10) | 20%(2/10) |
| Object Understanding | 92.9%(13/14) | 78.6%(11/14) |
| Spatial Understanding | 78.9%(15/19) | 68.4%(13/19) |
| OCR | ||
| Overall Score | 83.84% | 58.52% |
| Avg Response Time | 2.77s | 5.49s |
| Median input tokensincl. image tokens | 642 | 124 |
| Median output tokens | 64 | 18 |
| Est. cost / taskon this benchmark | $0.0019 | $0.0001 |
| Focused Scene OCR | 88.9%(88/99) | 76.8%(76/99) |
| Handwritten Math | 50%(5/10) | 80%(8/10) |
| License Plate Recognition | 90%(27/30) | 13.3%(4/30) |
| Text Recognition | 80%(24/30) | 50%(15/30) |
| VQA & Extraction | 80%(48/60) | 51.7%(31/60) |
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