Claude Sonnet 5 vs GPT-5.5
Compare Claude Sonnet 5 and GPT-5.5 side-by-side. See how these vision models stack up in Object Detection, Open Prompt, OCR, Classification, and Image Captioning.
Compare Claude Sonnet 5 vs GPT-5.5 live
Run the same image across every model that supports a task and compare their outputs side-by-side.
Detect and compare bounding boxes across models on the same image.
Upload an image
Drag and drop an image here, or click to browse
Models in this comparison
Claude Sonnet 5 vs GPT-5.5: 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.
GPT-5.5 is a multimodal large language model released by OpenAI on April 23, 2026, engineered for autonomous, multi-step knowledge work and agentic workflows. It accepts text, images, and code as input, featuring enhanced spatial reasoning and visual grounding to support its computer use capabilities for operating software and navigating UI elements. Built to execute complex workflows end-to-end, the model interprets loosely defined tasks, selects appropriate tools, and performs self-verification with minimal user intervention. It is available in a standard version, a Thinking mode for extended reasoning budgets, and a Pro variant that uses parallel test-time compute for maximum precision on complex tasks.
Co-optimized with NVIDIA for GB200 NVL72 infrastructure, GPT-5.5 delivers per-token latency comparable to its predecessor GPT-5.4 while maintaining a 1-million-token context window. Despite increased capability, the model achieves greater token efficiency in coding and data analysis workflows, often completing tasks with fewer total tokens than previous versions. OpenAI reports a 60% reduction in hallucination rate compared to GPT-5.4, improving reliability for accuracy-sensitive applications. API access is available via the Responses and Chat Completions endpoints at $5 per million input tokens and $30 per million output tokens, double the unit price of GPT-5.4.
Claude Sonnet 5 vs GPT-5.5 Comparison Table
| Property | Claude Sonnet 5 | GPT-5.5 |
|---|---|---|
| Organization | Anthropic | OpenAI |
| 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 | $5.00 |
| Output $/1M | $10.00 | $30.00 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Classification | Demo | Demo |
| Object Detection | Demo | Demo |
| OCR | Demo | Demo |
| Vision Language | ||
| Visual Question Answering | Demo | 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% | 77.61% |
| Avg Response Time | 3.90s | 30.12s |
| Median input tokensincl. image tokens | 2.1K | 1.4K |
| Median output tokens | 61 | 138 |
| Est. cost / taskon this benchmark | $0.0048 | $0.011 |
| Defect Detection | 73.3%(11/15) | 86.7%(13/15) |
| Document Understanding | 66.7%(6/9) | 88.9%(8/9) |
| Object Counting | 20%(2/10) | 30%(3/10) |
| Object Understanding | 92.9%(13/14) | 92.9%(13/14) |
| Spatial Understanding | 78.9%(15/19) | 78.9%(15/19) |
| OCR | ||
| Overall Score | 83.84% | 81.22% |
| Avg Response Time | 2.77s | 5.16s |
| Median input tokensincl. image tokens | 642 | 105 |
| Median output tokens | 64 | 83 |
| Est. cost / taskon this benchmark | $0.0019 | $0.0030 |
| Focused Scene OCR | 88.9%(88/99) | 77.8%(77/99) |
| Handwritten Math | 50%(5/10) | 40%(4/10) |
| License Plate Recognition | 90%(27/30) | 93.3%(28/30) |
| Text Recognition | 80%(24/30) | 83.3%(25/30) |
| VQA & Extraction | 80%(48/60) | 86.7%(52/60) |
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