Claude Opus 4.7 vs GPT-5.4
Compare Claude Opus 4.7 and GPT-5.4 side-by-side. See how these vision models stack up in Image Captioning, Classification, OCR, Object Detection, and Open Prompt.
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Claude Opus 4.7 vs GPT-5.4: Overview
Claude Opus 4.7 is a proprietary multimodal language model developed by Anthropic, released on April 16, 2026. It is designed for agentic coding, long-horizon task execution, and enterprise knowledge work. The model supports text and vision inputs and operates with a context window of up to 1,000,000 tokens. It introduces adaptive thinking, which dynamically allocates reasoning based on task complexity, along with configurable effort controls including a new xhigh setting that sits between the existing high and max levels. It achieves 87.6% on SWE-bench Verified and 78.0% on OSWorld-Verified, reflecting strong performance on autonomous software engineering and computer use tasks respectively.
Compared to Claude Opus 4.6, version 4.7 shows improved instruction following and higher reliability in extended agentic tasks. Vision capabilities now support high-resolution inputs up to 2,576px on the long edge (~3.75 megapixels), more than three times the resolution of prior Claude models, enabling finer interpretation of dense diagrams, UI screenshots, and document layouts. These improvements, combined with self-verification on long-running tasks and a new task budget system for controlling agentic loops, make it well-suited for complex software engineering, technical analysis, and multimodal vision workflows.
GPT-5.4 is a proprietary multimodal large language model developed by OpenAI and released on March 5, 2026. It is designed for professional workloads such as advanced software development, research, and agentic automation. The model combines the general reasoning capabilities of the GPT-5 series with software engineering improvements derived from GPT-5.3-Codex. In the API and Codex environments it supports context windows of up to 1 million tokens, enabling long-context reasoning and large-scale code or document workflows.
Compared with GPT-5.2, GPT-5.4 reduces false individual claims by 33% and lowers overall response errors by 18%, improving factual reliability across complex tasks. It is also the first general-purpose OpenAI release with native computer-use capabilities, allowing agents to interact with desktops, browsers, and external applications to complete multi-step workflows. The model family includes three variants: GPT-5.4 (standard), GPT-5.4 Pro for higher-performance workloads, and GPT-5.4 Thinking, a reasoning-oriented version in ChatGPT that presents an upfront plan before generating its response. The API also introduces a Tool Search system that allows models to retrieve tool definitions dynamically, reducing token usage in tool-heavy integrations.
Claude Opus 4.7 vs GPT-5.4 Comparison Table
| Property | Claude Opus 4.7 | GPT-5.4 |
|---|---|---|
| Organization | Anthropic | OpenAI |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Apr 2026 | Mar 2026 |
| Context Window | 1.0M | 1.1M |
| Parameters | ||
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $5.00 | $2.50 |
| Output $/1M | $25.00 | $15.00 |
| 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% | ||
| Overall Score | 67.16% | 77.61% |
| Avg Response Time | 4.85s | 7.16s |
| Median input tokensincl. image tokens | 2.4K | 1.4K |
| Median output tokens | 110 | 108 |
| Est. cost / taskon this benchmark | $0.015 | $0.0052 |
| Defect Detection | 73.3%(11/15) | 86.7%(13/15) |
| Document Understanding | 77.8%(7/9) | 88.9%(8/9) |
| Object Counting | 20%(2/10) | 40%(4/10) |
| Object Understanding | 85.7%(12/14) | 85.7%(12/14) |
| Spatial Understanding | 68.4%(13/19) | 78.9%(15/19) |
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