Claude Sonnet 5 vs Claude Sonnet 5.5
Compare Claude Sonnet 5 and Claude Sonnet 5.5 side-by-side.
Compare Claude Sonnet 5 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 Sonnet 5 vs Claude Sonnet 5.5 on Vision Evals
Claude Sonnet 5.5 scores higher on 5 of the six Vision Evals tasks.
The widest gap is Object Detection, where Claude Sonnet 5.5 leads 74.3% to 36.1%.
Overall, Claude Sonnet 5 averages 66.4% (#37 of 60) against 83.8% (#7 of 60) for Claude Sonnet 5.5.
Claude Sonnet 5 is both cheaper ($0.0064 vs $0.0065 per sample) and faster (4.8s vs 10.8s per sample).
Claude Sonnet 5 vs Claude Sonnet 5.5 Comparison Table
Evals updated September 28, 2026Pricing updated September 28, 2026
| Property | Claude Sonnet 5 | Claude Sonnet 5.5 |
|---|---|---|
| Organization | Anthropic | Anthropic |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Jun 2026 | Sep 2026 |
| Context Window | 1.0M | 1.0M |
| Parameters | ||
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $2.00 | |
| Output $/1M | $10.00 | |
| 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 | 66.4% | 83.8% |
| Avg cost / sample | $0.0064 | $0.0065 |
| Avg speed / sample | 4.84s | 10.78s |
| By task | ||
| Object Detection (low) | 36.1% | 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) | 56.8% | 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) | 81.3% | 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) | 91.7% | 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) | 89.7% | 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) | 43.0% | 76.4% ±0.7, Mean of 3 runs, range 75.5 to 76.8 |
| Reasoning (high) | 43.0% | 83.9% ±1.7, Mean of 3 runs, range 82.1 to 85.4 |
Claude Sonnet 5 vs Claude Sonnet 5.5: Overview
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 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.
Frequently Asked Questions
On Roboflow's Vision Evals, Claude Sonnet 5.5 performed better. It scores higher on 5 of the six vision tasks and averages 83.8% (#7 of 60) against 66.4% (#37 of 60) for Claude Sonnet 5. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
No. On the Vision Evals Object Detection benchmark at low effort, Claude Sonnet 5.5 leads with 74.3% against 36.1%. This is the widest gap between the two models across the benchmark's tasks.
Claude Sonnet 5 is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0064 per sample against $0.0065. Actual costs depend on your image sizes, prompts, and output length.
Claude Sonnet 5 is faster. Across Roboflow's Vision Evals it averaged 4.8s per inference against 10.8s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.