Claude Opus 4.1 vs GPT-5 Nano
Compare Claude Opus 4.1 and GPT-5 Nano side-by-side. See how these vision models stack up in Open Prompt, Classification, Object Detection, OCR, and Image Captioning.
Compare Claude Opus 4.1 vs GPT-5 Nano 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 Opus 4.1 vs GPT-5 Nano: Overview
Claude 4.1 Opus, released by Anthropic in August 2025, is the upgraded flagship of the Claude 4 family, building on Opus 4 with stronger reasoning and agentic capabilities. Like its predecessor, it is multimodal and optimized for text, code, and tool use, with support for large context windows suited to multi-file codebases, technical workflows, and long-horizon problem solving.
On benchmarks, Opus 4.1 improves coding performance, reaching ~74.5% on SWE-Bench Verified compared to Opus 4’s ~72.5%. It demonstrates more precise debugging, refactoring, and orchestration of agentic tasks while maintaining similar safety and alignment safeguards. It is best suited for enterprise-scale software development, research automation, and advanced reasoning workflows where reliability and depth of analysis are critical.
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 Opus 4.1 vs GPT-5 Nano Comparison Table
| Property | Claude Opus 4.1 | GPT-5 Nano |
|---|---|---|
| Organization | Anthropic | OpenAI |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Aug 2025 | Aug 2025 |
| Context Window | 200K | 400K |
| Parameters | ||
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $15.00 | $0.050 |
| Output $/1M | $75.00 | $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 | 7.09s | 6.58s |
| Median input tokensincl. image tokens | 2.0K | 1.8K |
| Median output tokens | 140 | 591 |
| Est. cost / taskon this benchmark | $0.040 | $0.0003 |
| Defect Detection | 73.3%(11/15) | 86.7%(13/15) |
| Document Understanding | 88.9%(8/9) | 66.7%(6/9) |
| Object Counting | 0%(0/10) | 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 | 68.56% | 69% |
| Avg Response Time | 5.08s | 6.15s |
| Median input tokensincl. image tokens | 552 | 122 |
| Median output tokens | 97 | 539 |
| Est. cost / taskon this benchmark | $0.016 | $0.0002 |
| Focused Scene OCR | 73.7%(73/99) | 64.6%(64/99) |
| Handwritten Math | 30%(3/10) | 40%(4/10) |
| License Plate Recognition | 53.3%(16/30) | 83.3%(25/30) |
| Text Recognition | 80%(24/30) | 70%(21/30) |
| VQA & Extraction | 68.3%(41/60) | 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