Claude Opus 4.6 vs GPT-5 Nano
Compare Claude Opus 4.6 and GPT-5 Nano side-by-side. See how these vision models stack up in Open Prompt, OCR, Object Detection, Classification, and Image Captioning.
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Claude Opus 4.6 vs GPT-5 Nano: Overview
Claude Opus 4.6 is the flagship large language model from Anthropic, released on 2026-02-05 for advanced reasoning, complex coding, and enterprise agent workflows. It supports text and image inputs via API, offers a 200K-token standard context window with a 1M-token beta option, and enables outputs up to 128K tokens, with adaptive reasoning and context compaction for sustained tasks.
As of 2026-02-17, Anthropic also released Claude Sonnet 4.6, extending the 1M-token context window to a broader tier. Opus remains positioned for maximum depth and benchmark performance, while Sonnet 4.6 brings long-context capability to more cost- and latency-sensitive production use cases.
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 4.6 vs GPT-5 Nano Comparison Table
| Property | Claude Opus 4.6 | GPT-5 Nano |
|---|---|---|
| Organization | Anthropic | OpenAI |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Feb 2026 | Aug 2025 |
| Context Window | 1.0M | 400K |
| Parameters | ||
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $5.00 | $0.050 |
| Output $/1M | $25.00 | $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 | 64.18% | 58.21% |
| Avg Response Time | 23.35s | 6.58s |
| Median input tokensincl. image tokens | 2.2K | 1.8K |
| Median output tokens | 130 | 591 |
| Est. cost / taskon this benchmark | $0.014 | $0.0003 |
| Defect Detection | 73.3%(11/15) | 86.7%(13/15) |
| Document Understanding | 77.8%(7/9) | 66.7%(6/9) |
| Object Counting | 20%(2/10) | 0%(0/10) |
| Object Understanding | 71.4%(10/14) | 64.3%(9/14) |
| Spatial Understanding | 68.4%(13/19) | 57.9%(11/19) |
| OCR | ||
| Overall Score | 82.53% | 69% |
| Avg Response Time | 5.05s | 6.15s |
| Median input tokensincl. image tokens | 736 | 122 |
| Median output tokens | 99 | 539 |
| Est. cost / taskon this benchmark | $0.0062 | $0.0002 |
| Focused Scene OCR | 85.9%(85/99) | 64.6%(64/99) |
| Handwritten Math | 70%(7/10) | 40%(4/10) |
| License Plate Recognition | 90%(27/30) | 83.3%(25/30) |
| Text Recognition | 80%(24/30) | 70%(21/30) |
| VQA & Extraction | 76.7%(46/60) | 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