Claude Opus 4.6 vs Claude Sonnet 5.5
Compare Claude Opus 4.6 and Claude Sonnet 5.5 side-by-side.
Compare Claude Opus 4.6 vs Claude Sonnet 5.5 live
Run the same image across every model that supports a task and compare their outputs side-by-side.
These models don't share enough common tasks for a side-by-side demo. See the comparison table below for their capabilities.
Models in this comparison
Claude Opus 4.6 vs Claude Sonnet 5.5 Comparison Table
Evals updated September 28, 2026Pricing updated September 28, 2026
| Property | Claude Opus 4.6 | Claude Sonnet 5.5 |
|---|---|---|
| Organization | Anthropic | Anthropic |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Feb 2026 | Sep 2026 |
| Context Window | 1.0M | 1.0M |
| Parameters | ||
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $5.00 | |
| Output $/1M | $25.00 | |
| Vision Tasks | ||
| Captioning | Demo | |
| Chart Question Answering | ||
| Classification | Demo | |
| Document Question Answering | ||
| Image Tagging | ||
| Multi-Label Classification | ||
| Object Detection | Demo | |
| OCR | Demo | |
| Vision Language | ||
| Visual Question Answering | Demo | |
| Model Features | ||
| Foundation Vision | ||
| LLMs with Vision Capabilities | ||
| Multimodal Vision | ||
Vision Evalsground-truth scores across 6 vision tasks, pooled at low effort | ||
| Overall | Not evaluated | 83.8% |
| Avg cost / sample | – | $0.0065 |
| Avg speed / sample | – | 10.78s |
| By task | ||
| Object Detection (low) | – | 74.3% ±0.9, Mean of 3 runs, range 73.5 to 75.3 |
| Object Detection (high) | – | 76.8% ±0.4, Mean of 3 runs, range 76.5 to 77.3 |
| Counting (low) | – | 79.3% ±0.7, Mean of 3 runs, range 78.4 to 79.7 |
| Counting (high) | – | 82.9% ±1.4, Mean of 3 runs, range 81.1 to 83.8 |
| Identification (low) | – | 91.7% ±3.1, Mean of 3 runs, range 87.5 to 93.8 |
| Identification (high) | – | 90.6% ±0.0, Mean of 3 runs, range 90.6 to 90.6 |
| OCR (low) | – | 90.6% ±0.9, Mean of 3 runs, range 90.0 to 91.7 |
| OCR (high) | – | 90.9% ±1.5, Mean of 3 runs, range 89.2 to 92.3 |
| Data Extraction (low) | – | 90.7% ±1.5, Mean of 3 runs, range 89.7 to 92.8 |
| Data Extraction (high) | – | 93.1% ±0.5, Mean of 3 runs, range 92.8 to 93.8 |
| Reasoning (low) | – | 76.4% ±0.7, Mean of 3 runs, range 75.5 to 76.8 |
| Reasoning (high) | – | 83.9% ±1.7, Mean of 3 runs, range 82.1 to 85.4 |
Claude Opus 4.6 vs Claude Sonnet 5.5: 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 Sonnet 5.5 is a proprietary multimodal language model from Anthropic and the second release in the Claude 5.5 family, following Claude Opus 5.5. It accepts interleaved text and image input and returns text, operating with a 1M token context window and a maximum output of 128K tokens per request. The model uses adaptive thinking by default, allocating variable reasoning effort per request rather than exposing a manual extended thinking toggle, and its training data cutoff is June 2026. Anthropic positions it as a faster, lower cost complement to Opus 5.5 for well scoped everyday tasks, bug fixing, and producing documents, slides, and spreadsheets.
On visual and agentic evaluations reported at launch, Sonnet 5.5 scores 61.6% on Chartography, a chart recognition test, compared with 15.6% for Claude Sonnet 5, and 80.1% on OSWorld 2.1, a computer use benchmark measuring screenshot driven control of a desktop environment, compared with 57.0% for Sonnet 5. It reports 70.6% on Terminal-Bench 4.0 for agentic coding. Anthropic describes it as the first Sonnet model able to complete Pokemon Red from screenshots alone, and it generates output more than 30% faster than Sonnet 5 while using fewer tokens for equivalent work.