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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AnthropicClaude Opus 4.6
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OpenAIGPT-5 Nano
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Claude Opus 4.6 vs GPT-5 Nano: Overview

Claude Opus 4.6

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

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

PropertyClaude Opus 4.6 GPT-5 Nano
OrganizationAnthropicOpenAI
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateFeb 2026Aug 2025
Context Window1.0M400K
Parameters
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$5.00$0.050
Output $/1M$25.00$0.400
Vision Tasks
CaptioningDemoDemo
ClassificationDemoDemo
Object DetectionDemoDemo
OCRDemoDemo
Vision Language
Visual Question AnsweringDemoDemo
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 Time23.35s6.58s
Median input tokensincl. image tokens2.2K1.8K
Median output tokens130591
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 Time5.05s6.15s
Median input tokensincl. image tokens736122
Median output tokens99539
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