Claude Opus 4.7 vs Claude Sonnet 5
Compare Claude Opus 4.7 and Claude Sonnet 5 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 Claude Sonnet 5: 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.
Claude Sonnet 5 is a mid-tier large language model from Anthropic, released on June 30, 2026, as the latest model in the Sonnet series and a direct successor to Claude Sonnet 4.6. It is a hybrid reasoning model designed primarily for agentic workflows, software coding, and professional tasks. The model features a 1 million token context window, a 128k maximum output token limit, and runs adaptive thinking by default, giving API users fine-grained control over reasoning effort across five levels (low, medium, high, max, and extra-high). It uses an updated tokenizer shared with Opus 4.7 and later models, which produces approximately 30% more tokens for equivalent text compared to earlier Claude models. On benchmarks, Sonnet 5 scores 63.2% on agentic coding and 81.2% on OSWorld, narrowing the gap with Opus 4.8 while remaining at Sonnet-tier pricing.
The model supports text and image input with text output, and accepts tools including browsers and terminals for autonomous multi-step task execution. Anthropic's safety evaluations report that Sonnet 5 shows a lower rate of undesirable behaviors than Sonnet 4.6 and is generally safer in agentic contexts, with improved resistance to prompt injection and reduced sycophancy. Cybersecurity safeguards equivalent to those on Opus 4.7 and 4.8 are active, though Anthropic notes the model was not deliberately trained on cybersecurity tasks. The model is proprietary and API-only, with no open weights.
Claude Opus 4.7 vs Claude Sonnet 5 Comparison Table
| Property | Claude Opus 4.7 | Claude Sonnet 5 |
|---|---|---|
| Organization | Anthropic | Anthropic |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Apr 2026 | Jun 2026 |
| Context Window | 1.0M | 1.0M |
| Parameters | ||
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $5.00 | $2.00 |
| Output $/1M | $25.00 | $10.00 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Classification | Demo | Demo |
| Object Detection | Demo | Demo |
| OCR | Demo | Demo |
| Vision Language | ||
| Visual Question Answering | Demo | Demo |
| Document Question Answering | ||
| Multi-Label Classification | ||
| Model Features | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
| Foundation Vision | ||
Vision Evalspass/fail results · 67 prompts Score key:≥75%40–74%<40% | ||
| Visual Understanding | ||
| Overall Score | 67.16% | 70.15% |
| Avg Response Time | 4.85s | 3.90s |
| Median input tokensincl. image tokens | 2.4K | 2.1K |
| Median output tokens | 110 | 61 |
| Est. cost / taskon this benchmark | $0.015 | $0.0048 |
| Defect Detection | 73.3%(11/15) | 73.3%(11/15) |
| Document Understanding | 77.8%(7/9) | 66.7%(6/9) |
| Object Counting | 20%(2/10) | 20%(2/10) |
| Object Understanding | 85.7%(12/14) | 92.9%(13/14) |
| Spatial Understanding | 68.4%(13/19) | 78.9%(15/19) |
| OCR | ||
| Overall Score | 86.9% | 83.84% |
| Avg Response Time | 4.19s | 2.77s |
| Median input tokensincl. image tokens | 969 | 642 |
| Median output tokens | 81 | 64 |
| Est. cost / taskon this benchmark | $0.0069 | $0.0019 |
| Focused Scene OCR | 88.9%(88/99) | 88.9%(88/99) |
| Handwritten Math | 80%(8/10) | 50%(5/10) |
| License Plate Recognition | 93.3%(28/30) | 90%(27/30) |
| Text Recognition | 86.7%(26/30) | 80%(24/30) |
| VQA & Extraction | 81.7%(49/60) | 80%(48/60) |
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