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Claude Opus 4.6 vs GPT-5.4 Nano

Compare Claude Opus 4.6 and GPT-5.4 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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Claude Opus 4.6 vs GPT-5.4 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-5.4 Nano

GPT-5.4 nano is a high-throughput model developed by OpenAI and released on March 17, 2026, as the efficiency-optimized entry in the GPT-5.4 family. Engineered for cost-sensitive production environments and latency-critical workloads, it features an expanded 400,000-token context window that enables the processing of large document batches or extensive logs in a single pass. The model is primarily optimized for text-heavy operations, serving as a premier engine for high-volume classification, data extraction, ranking, and the orchestration of lightweight sub-agents where speed and low per-token costs are the primary requirements.

While it supports text and image inputs, GPT-5.4 nano is designed as a text-first worker rather than a specialized visual reasoning tool. In multi-model architectures, it is best utilized for structured text tasks and simple coding sub-tasks, leaving intensive vision reasoning and UI navigation to its sibling, GPT-5.4 mini. Compared to the previous GPT-5 nano, this version provides a significant leap in reliability for structured outputs and tool calling, making it a dependable and economical choice for developers building scalable, automated pipelines that require rapid execution at the edge of the GPT-5.4 ecosystem.

Claude Opus 4.6 vs GPT-5.4 Nano Comparison Table

PropertyClaude Opus 4.6 GPT-5.4 Nano
OrganizationAnthropicOpenAI
Categoryclosedclosed
Modalitymultimodalmultimodal
Release DateFeb 2026Mar 2026
Context Window1.0M400K
Parameters
LicenseProprietaryProprietary
Pricing per 1M tokens
Input $/1M$5.00$0.200
Output $/1M$25.00$1.25
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%
62.69%
Avg Response Time23.35s3.72s
Median input tokensincl. image tokens2.2K1.4K
Median output tokens130105
Est. cost / taskon this benchmark$0.014$0.0004
Defect Detection
73.3%(11/15)
80%(12/15)
Document Understanding
77.8%(7/9)
77.8%(7/9)
Object Counting
20%(2/10)
30%(3/10)
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%
62.45%
Avg Response Time5.05s2.59s
Median input tokensincl. image tokens736105
Median output tokens9987
Est. cost / taskon this benchmark$0.0062$0.0001
Focused Scene OCR
85.9%(85/99)
55.6%(55/99)
Handwritten Math
70%(7/10)
20%(2/10)
License Plate Recognition
90%(27/30)
83.3%(25/30)
Text Recognition
80%(24/30)
70%(21/30)
VQA & Extraction
76.7%(46/60)
66.7%(40/60)

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