Claude Sonnet 4.5 vs Claude Opus 4
Compare Claude Sonnet 4.5 and Claude Opus 4 side-by-side. See how these vision models stack up in Object Detection, Classification, Image Captioning, OCR, and Open Prompt.
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Claude Opus 4 is deprecated and can no longer be run. Details and evals are still available on its model page.
Models in this comparison
Claude Sonnet 4.5 vs Claude Opus 4: Overview
Claude Sonnet 4.5, released by Anthropic in September 2025, is the company’s most advanced Sonnet-series model, built for high-performance reasoning, coding, and long-horizon agentic workflows. It is a multimodal system that accepts both text and images, with a 200,000-token context window designed for handling large documents and extended interactions. Anthropic highlights its improvements in reliability, reduced sycophancy, and alignment, making it suitable for sustained enterprise use.
The model delivers strong results in coding and autonomous workflows, achieving 61.4% on the OSWorld benchmark and leading performance on SWE-bench Verified. It introduces infrastructure features such as a memory tool (beta), checkpointing for Claude Code, parallel tool use, and tighter integration with VS Code. Compared to Opus, which targets broader reasoning, Sonnet 4.5 is optimized for structured, long-duration tasks. Positioned against leading offerings from OpenAI and Google, it is aimed at enterprise automation, software engineering, and research-intensive applications.
Claude 4 Opus, released by Anthropic in May 2025, is the flagship model of the Claude 4 family, built for complex, long-horizon reasoning and advanced coding workflows. It is multimodal, supporting text (including voice), images, and tool use, and operates as a hybrid reasoning model—able to deliver quick answers in fast mode or switch to extended thinking for deeper, multi-step problem solving. With a ~200,000-token context window and a training cutoff around March 2025, it is optimized for handling large documents, long conversations, and sophisticated agentic tasks.
Positioned at the high end of Anthropic’s offerings, Opus 4 achieves state-of-the-art results on coding benchmarks like SWE-Bench (72.5%) and Terminal-Bench (43.2%). It is best suited for research, enterprise automation, and software development at scale. The model is classified at Anthropic’s ASL-3 safety level, denoting advanced oversight and safety features.
Claude Sonnet 4.5 vs Claude Opus 4 Comparison Table
| Property | Claude Sonnet 4.5 | Claude Opus 4 |
|---|---|---|
| Organization | Anthropic | Anthropic |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Sep 2025 | May 2025 |
| Context Window | 200K | 200K |
| Parameters | ||
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $3.00 | $15.00 |
| Output $/1M | $15.00 | $75.00 |
| Vision Tasks | ||
| Captioning | Demo | |
| Classification | Demo | |
| Object Detection | Demo | |
| OCR | Demo | |
| Vision Language | ||
| Visual Question Answering | 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 | 59.7% | 56.72% |
| Avg Response Time | 5.67s | 19.74s |
| Median input tokensincl. image tokens | 2.2K | |
| Median output tokens | 182 | |
| Est. cost / taskon this benchmark | $0.0092 | |
| Defect Detection | 73.3%(11/15) | 66.7%(10/15) |
| Document Understanding | 77.8%(7/9) | 88.9%(8/9) |
| Object Counting | 10%(1/10) | 0%(0/10) |
| Object Understanding | 64.3%(9/14) | 64.3%(9/14) |
| Spatial Understanding | 63.2%(12/19) | 57.9%(11/19) |
| OCR | ||
| Overall Score | 67.44% | |
| Avg Response Time | 3.58s | |
| Median input tokensincl. image tokens | 701 | |
| Median output tokens | 114 | |
| Est. cost / taskon this benchmark | $0.0038 | |
| Focused Scene OCR | 71.7%(71/99) | |
| License Plate Recognition | 53.3%(16/30) | |
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