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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Claude Opus 4.6 vs GPT-5.4 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-5.4 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
| Property | Claude Opus 4.6 | GPT-5.4 Nano |
|---|---|---|
| Organization | Anthropic | OpenAI |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Feb 2026 | Mar 2026 |
| Context Window | 1.0M | 400K |
| Parameters | ||
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $5.00 | $0.200 |
| Output $/1M | $25.00 | $1.25 |
| 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% | 62.69% |
| Avg Response Time | 23.35s | 3.72s |
| Median input tokensincl. image tokens | 2.2K | 1.4K |
| Median output tokens | 130 | 105 |
| 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 Time | 5.05s | 2.59s |
| Median input tokensincl. image tokens | 736 | 105 |
| Median output tokens | 99 | 87 |
| 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