Claude Sonnet 5 vs Qwen3.8 Max
Compare Claude Sonnet 5 and Qwen3.8 Max 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 Qwen3.8 Max on Vision Evals
Qwen3.8 Max scores higher on 5 of the six Vision Evals tasks.
The widest gap is Object Detection, where Qwen3.8 Max leads 76.7% to 36.1%.
Overall, Claude Sonnet 5 averages 66.4% (#32 of 53) against 83.9% (#5 of 53) for Qwen3.8 Max.
Claude Sonnet 5 is both cheaper ($0.0064 vs $0.0074 per sample) and faster (4.8s vs 17.3s per sample).
Claude Sonnet 5 vs Qwen3.8 Max Comparison Table
Evals updated September 5, 2026Pricing updated September 18, 2026
| Property | Claude Sonnet 5 | Qwen3.8 Max |
|---|---|---|
| Organization | Anthropic | Qwen |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Jun 2026 | Aug 2026 |
| Context Window | 1.0M | 984K |
| Parameters | 2.4T total, ~95B active | |
| License | Proprietary | Apache 2.0 |
| Pricing per 1M tokens | ||
| Input $/1M | $2.00 | |
| Output $/1M | $10.00 | |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Chart Question Answering | ||
| Classification | Demo | Demo |
| Document Question Answering | ||
| Image Tagging | ||
| Multi-Label Classification | ||
| Object Detection | Demo | 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% | 83.9% |
| Avg cost / sample | $0.0064 | $0.0074 |
| Avg speed / sample | 4.84s | 17.25s |
| By task | ||
| Object Detection (low) | 36.1% | 76.7% ±0.3, Mean of 3 runs, range 76.5 to 77.1 |
| Object Detection (high) | – | 78.4% ±0.4, Mean of 3 runs, range 78.1 to 78.9 |
| Counting (low) | 56.8% | 81.1% ±2.0, Mean of 3 runs, range 78.4 to 82.4 |
| Counting (high) | – | 81.1% ±0.0, Mean of 3 runs, range 81.1 to 81.1 |
| Identification (low) | 81.3% | 88.5% ±1.6, Mean of 3 runs, range 87.5 to 90.6 |
| Identification (high) | – | 89.6% ±1.6, Mean of 3 runs, range 87.5 to 90.6 |
| OCR (low) | 91.7% | 93.3% ±0.5, Mean of 3 runs, range 92.8 to 93.9 |
| OCR (high) | – | 91.3% ±0.5, Mean of 3 runs, range 90.7 to 91.7 |
| Data Extraction (low) | 89.7% | 87.6% ±0.0, Mean of 3 runs, range 87.6 to 87.6 |
| Data Extraction (high) | – | 89.3% ±1.0, Mean of 3 runs, range 88.7 to 90.7 |
| Reasoning (low) | 43.0% | 75.9% ±2.0, Mean of 3 runs, range 73.5 to 77.5 |
| Reasoning (high) | 43.0% | 80.3% ±2.0, Mean of 3 runs, range 78.2 to 82.1 |
Claude Sonnet 5 vs Qwen3.8 Max: 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.
Qwen3.8 Max is the flagship tier of Alibaba's Qwen3.8 family, a sparse mixture-of-experts multimodal model with roughly 2.4 trillion total parameters of which about 95 billion activate per token, which keeps serving cost and latency well below what the total parameter count would imply. It builds on the architectural foundation established by Qwen3.5 and accepts text, images, video, and documents as input while producing text output. Reported context handling reaches close to one million tokens, with a maximum generation length of 131,072 tokens, so the model is aimed at long-horizon agentic work such as repository-scale coding, multi-step research, data analysis, and office document workflows.
For vision work the model performs image and video understanding, document and chart interpretation, text recognition inside images, and grounded visual question answering, and Alibaba reports gains concentrated in multimodal and agentic evaluation categories rather than general reasoning. Published figures include 86.6 on Terminal-Bench 2.1, 67.7 on SWE-bench Pro, 93.0 on PaperBench, 82.8 on IFBench, and 92.6 on GPQA Diamond. It is the first model in the Max tier of the Qwen line for which the team states weights will be released publicly, alongside a smaller Qwen3.8 27B checkpoint. No training or safety model card has been published.
Frequently Asked Questions
On Roboflow's Vision Evals, Qwen3.8 Max performed better. It scores higher on 5 of the six vision tasks and averages 83.9% (#5 of 53) against 66.4% (#32 of 53) for Claude Sonnet 5. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
No. On the Vision Evals Object Detection benchmark at low effort, Qwen3.8 Max leads with 76.7% against 36.1%. This is the widest gap between the two models across the benchmark's tasks.
Claude Sonnet 5 is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0064 per sample against $0.0074. 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 17.3s. 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 object detection and open prompts in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.