Claude Opus 4 vs Claude Sonnet 4.6
Compare Claude Opus 4 and Claude Sonnet 4.6 side-by-side. See how these vision models stack up in Image Captioning, OCR, Object Detection, Open Prompt, and Classification.
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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 Opus 4 vs Claude Sonnet 4.6: Overview
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 4.6 is Anthropic's mid-tier large language model, released February 17, 2026, designed to balance performance, cost, and versatility for professional and developer use. It supports text and vision-based tasks with advanced reasoning, agentic capabilities, and Adaptive Thinking — a mode where the model dynamically scales its internal reasoning depth. A beta context window of up to 1,000,000 tokens (200K standard) enables processing of entire codebases or document collections in a single request. Parameters are undisclosed.
Optimized for coding, computer use, long-context reasoning, agent planning, and knowledge work, Sonnet 4.6 delivers a full generational upgrade over Sonnet 4.5 and approaches Opus 4.5-level performance across many benchmarks at a fraction of the cost. It is the default model on Claude.ai, Claude Cowork, and is available via API and major cloud platforms — making it well suited for production workloads requiring strong reasoning without flagship pricing.
Claude Opus 4 vs Claude Sonnet 4.6 Comparison Table
| Property | Claude Opus 4 | Claude Sonnet 4.6 |
|---|---|---|
| Organization | Anthropic | Anthropic |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | May 2025 | Feb 2026 |
| Context Window | 200K | 1.0M |
| Parameters | ||
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $15.00 | $3.00 |
| Output $/1M | $75.00 | $15.00 |
| 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 | 56.72% | 70.15% |
| Avg Response Time | 19.74s | 4.24s |
| Median input tokensincl. image tokens | 2.2K | |
| Median output tokens | 105 | |
| Est. cost / taskon this benchmark | $0.0080 | |
| Defect Detection | 66.7%(10/15) | 80%(12/15) |
| Document Understanding | 88.9%(8/9) | 77.8%(7/9) |
| Object Counting | 0%(0/10) | 30%(3/10) |
| Object Understanding | 64.3%(9/14) | 71.4%(10/14) |
| Spatial Understanding | 57.9%(11/19) | 78.9%(15/19) |
| OCR | ||
| Overall Score | 81.66% | |
| Avg Response Time | 3.42s | |
| Median input tokensincl. image tokens | 736 | |
| Median output tokens | 85 | |
| Est. cost / taskon this benchmark | $0.0035 | |
| Focused Scene OCR | 85.9%(85/99) | |
| Handwritten Math | 50%(5/10) | |
| License Plate Recognition | 90%(27/30) | |
| Text Recognition | 86.7%(26/30) | |
| VQA & Extraction | 73.3%(44/60) | |
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