Claude Opus 4.1 vs Claude Sonnet 5
Compare Claude Opus 4.1 and Claude Sonnet 5 side-by-side. See how these vision models stack up in Open Prompt, Classification, Object Detection, OCR, and Image Captioning.
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Claude Opus 4.1 vs Claude Sonnet 5: Overview
Claude 4.1 Opus, released by Anthropic in August 2025, is the upgraded flagship of the Claude 4 family, building on Opus 4 with stronger reasoning and agentic capabilities. Like its predecessor, it is multimodal and optimized for text, code, and tool use, with support for large context windows suited to multi-file codebases, technical workflows, and long-horizon problem solving.
On benchmarks, Opus 4.1 improves coding performance, reaching ~74.5% on SWE-Bench Verified compared to Opus 4’s ~72.5%. It demonstrates more precise debugging, refactoring, and orchestration of agentic tasks while maintaining similar safety and alignment safeguards. It is best suited for enterprise-scale software development, research automation, and advanced reasoning workflows where reliability and depth of analysis are critical.
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 4.1 vs Claude Sonnet 5 Comparison Table
| Property | Claude Opus 4.1 | Claude Sonnet 5 |
|---|---|---|
| Organization | Anthropic | Anthropic |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Aug 2025 | Jun 2026 |
| Context Window | 200K | 1.0M |
| Parameters | ||
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $15.00 | $2.00 |
| Output $/1M | $75.00 | $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 | 59.7% | 70.15% |
| Avg Response Time | 7.09s | 3.90s |
| Median input tokensincl. image tokens | 2.0K | 2.1K |
| Median output tokens | 140 | 61 |
| Est. cost / taskon this benchmark | $0.040 | $0.0048 |
| Defect Detection | 73.3%(11/15) | 73.3%(11/15) |
| Document Understanding | 88.9%(8/9) | 66.7%(6/9) |
| Object Counting | 0%(0/10) | 20%(2/10) |
| Object Understanding | 64.3%(9/14) | 92.9%(13/14) |
| Spatial Understanding | 63.2%(12/19) | 78.9%(15/19) |
| OCR | ||
| Overall Score | 68.56% | 83.84% |
| Avg Response Time | 5.08s | 2.77s |
| Median input tokensincl. image tokens | 552 | 642 |
| Median output tokens | 97 | 64 |
| Est. cost / taskon this benchmark | $0.016 | $0.0019 |
| Focused Scene OCR | 73.7%(73/99) | 88.9%(88/99) |
| Handwritten Math | 30%(3/10) | 50%(5/10) |
| License Plate Recognition | 53.3%(16/30) | 90%(27/30) |
| Text Recognition | 80%(24/30) | 80%(24/30) |
| VQA & Extraction | 68.3%(41/60) | 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