Claude Opus 4.6 vs Claude Opus 4.7
Compare Claude Opus 4.6 and Claude Opus 4.7 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 Claude Opus 4.7: 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.
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.
Claude Opus 4.6 vs Claude Opus 4.7 Comparison Table
| Property | Claude Opus 4.6 | Claude Opus 4.7 |
|---|---|---|
| Organization | Anthropic | Anthropic |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Feb 2026 | Apr 2026 |
| Context Window | 1.0M | 1.0M |
| Parameters | ||
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $5.00 | $5.00 |
| Output $/1M | $25.00 | $25.00 |
| 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 | 64.18% | 67.16% |
| Avg Response Time | 23.35s | 4.85s |
| Median input tokensincl. image tokens | 2.2K | 2.4K |
| Median output tokens | 130 | 110 |
| Est. cost / taskon this benchmark | $0.014 | $0.015 |
| Defect Detection | 73.3%(11/15) | 73.3%(11/15) |
| Document Understanding | 77.8%(7/9) | 77.8%(7/9) |
| Object Counting | 20%(2/10) | 20%(2/10) |
| Object Understanding | 71.4%(10/14) | 85.7%(12/14) |
| Spatial Understanding | 68.4%(13/19) | 68.4%(13/19) |
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