Claude Sonnet 4.5 vs GPT-5 Nano
Compare Claude Sonnet 4.5 and GPT-5 Nano side-by-side. See how these vision models stack up in Object Detection, Classification, Image Captioning, OCR, and Open Prompt.
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Claude Sonnet 4.5 vs GPT-5 Nano: Overview
Claude Sonnet 4.5, released by Anthropic in September 2025, is the company’s most advanced Sonnet-series model, built for high-performance reasoning, coding, and long-horizon agentic workflows. It is a multimodal system that accepts both text and images, with a 200,000-token context window designed for handling large documents and extended interactions. Anthropic highlights its improvements in reliability, reduced sycophancy, and alignment, making it suitable for sustained enterprise use.
The model delivers strong results in coding and autonomous workflows, achieving 61.4% on the OSWorld benchmark and leading performance on SWE-bench Verified. It introduces infrastructure features such as a memory tool (beta), checkpointing for Claude Code, parallel tool use, and tighter integration with VS Code. Compared to Opus, which targets broader reasoning, Sonnet 4.5 is optimized for structured, long-duration tasks. Positioned against leading offerings from OpenAI and Google, it is aimed at enterprise automation, software engineering, and research-intensive applications.
GPT-5 Nano, released by OpenAI on August 7, 2025, is the smallest and most cost-efficient model in the GPT-5 family. Like its larger counterparts, it is multimodal—accepting text and images, supporting tool use, structured outputs, and reasoning—but it is optimized for speed, low latency, and affordability. It features input and output token limits of roughly 272K and 128K tokens respectively, enabling large-context processing even at its compact scale. Its knowledge cutoff is around May 2024, slightly earlier than the full GPT-5 model.
GPT-5 Nano is well-suited for high-volume or cost-sensitive deployments such as mobile apps, embedded AI systems, or rapid-response APIs. While it offers less depth on complex reasoning and coding tasks compared to GPT-5 Mini or Pro, it retains core multimodal and agentic capabilities, making it an attractive option where efficiency and scale matter more than maximum performance.
Claude Sonnet 4.5 vs GPT-5 Nano Comparison Table
| Property | Claude Sonnet 4.5 | GPT-5 Nano |
|---|---|---|
| Organization | Anthropic | OpenAI |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Sep 2025 | Aug 2025 |
| Context Window | 200K | 400K |
| Parameters | ||
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $3.00 | $0.050 |
| Output $/1M | $15.00 | $0.400 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Classification | Demo | Demo |
| 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 Evalspass/fail results · 67 prompts Score key:≥75%40–74%<40% | ||
| Visual Understanding | ||
| Overall Score | 59.7% | 58.21% |
| Avg Response Time | 5.67s | 6.58s |
| Median input tokensincl. image tokens | 2.2K | 1.8K |
| Median output tokens | 182 | 591 |
| Est. cost / taskon this benchmark | $0.0092 | $0.0003 |
| Defect Detection | 73.3%(11/15) | 86.7%(13/15) |
| Document Understanding | 77.8%(7/9) | 66.7%(6/9) |
| Object Counting | 10%(1/10) | 0%(0/10) |
| Object Understanding | 64.3%(9/14) | 64.3%(9/14) |
| Spatial Understanding | 63.2%(12/19) | 57.9%(11/19) |
| OCR | ||
| Overall Score | 67.44% | 69% |
| Avg Response Time | 3.58s | 6.15s |
| Median input tokensincl. image tokens | 701 | 122 |
| Median output tokens | 114 | 539 |
| Est. cost / taskon this benchmark | $0.0038 | $0.0002 |
| Focused Scene OCR | 71.7%(71/99) | 64.6%(64/99) |
| Handwritten Math | 40%(4/10) | |
| License Plate Recognition | 53.3%(16/30) | 83.3%(25/30) |
| Text Recognition | 70%(21/30) | |
| VQA & Extraction | 73.3%(44/60) | |
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