Claude Fable 5.1 vs Claude Sonnet 5.5
Compare Claude Fable 5.1 and Claude Sonnet 5.5 side-by-side.
Compare Claude Fable 5.1 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 Fable 5.1 vs Claude Sonnet 5.5 on Vision Evals
Claude Fable 5.1 scores higher on 3 of the six Vision Evals tasks.
The widest gap is Object Detection, where Claude Sonnet 5.5 leads 74.3% to 61.4%.
Overall, Claude Fable 5.1 averages 81.3% (#11 of 60) against 83.8% (#7 of 60) for Claude Sonnet 5.5.
Claude Sonnet 5.5 is cheaper ($0.0065 vs $0.035 per sample), while Claude Fable 5.1 is faster (8.3s vs 10.8s per sample).
Claude Fable 5.1 vs Claude Sonnet 5.5 Comparison Table
Evals updated September 28, 2026Pricing updated September 28, 2026
| Property | Claude Fable 5.1 | Claude Sonnet 5.5 |
|---|---|---|
| Organization | Anthropic | Anthropic |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Sep 2026 | Sep 2026 |
| Context Window | 1.0M | 1.0M |
| Parameters | ||
| License | Proprietary | Proprietary |
| Pricing per 1M tokens | ||
| Input $/1M | $10.00 | |
| Output $/1M | $50.00 | |
| 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 | 81.3% | 83.8% |
| Avg cost / sample | $0.035 | $0.0065 |
| Avg speed / sample | 8.28s | 10.78s |
| By task | ||
| Object Detection (low) | 61.4% ±0.5, Mean of 3 runs, range 61.0 to 62.0 | 74.3% ±0.9, Mean of 3 runs, range 73.5 to 75.3 |
| Object Detection (high) | 65.0% ±0.4, Mean of 3 runs, range 64.6 to 65.3 | 76.8% ±0.4, Mean of 3 runs, range 76.5 to 77.3 |
| Counting (low) | 69.4% ±2.7, Mean of 3 runs, range 66.2 to 71.6 | 79.3% ±0.7, Mean of 3 runs, range 78.4 to 79.7 |
| Counting (high) | 73.0% ±4.7, Mean of 3 runs, range 67.6 to 77.0 | 82.9% ±1.4, Mean of 3 runs, range 81.1 to 83.8 |
| Identification (low) | 97.9% ±1.6, Mean of 3 runs, range 96.9 to 100.0 | 91.7% ±3.1, Mean of 3 runs, range 87.5 to 93.8 |
| Identification (high) | 96.9% ±3.1, Mean of 3 runs, range 93.8 to 100.0 | 90.6% ±0.0, Mean of 3 runs, range 90.6 to 90.6 |
| OCR (low) | 94.0% ±0.4, Mean of 3 runs, range 93.6 to 94.4 | 90.6% ±0.9, Mean of 3 runs, range 90.0 to 91.7 |
| OCR (high) | 93.6% ±0.2, Mean of 3 runs, range 93.5 to 93.9 | 90.9% ±1.5, Mean of 3 runs, range 89.2 to 92.3 |
| Data Extraction (low) | 93.1% ±0.5, Mean of 3 runs, range 92.8 to 93.8 | 90.7% ±1.5, Mean of 3 runs, range 89.7 to 92.8 |
| Data Extraction (high) | 93.5% ±0.5, Mean of 3 runs, range 92.8 to 93.8 | 93.1% ±0.5, Mean of 3 runs, range 92.8 to 93.8 |
| Reasoning (low) | 72.0% ±1.3, Mean of 3 runs, range 70.9 to 73.5 | 76.4% ±0.7, Mean of 3 runs, range 75.5 to 76.8 |
| Reasoning (high) | 73.1% ±2.0, Mean of 3 runs, range 71.5 to 75.5 | 83.9% ±1.7, Mean of 3 runs, range 82.1 to 85.4 |
Claude Fable 5.1 vs Claude Sonnet 5.5: Overview
Claude Fable 5.1 is a proprietary multimodal model from Anthropic in the Mythos-class tier of the Claude 5 family, positioned above Claude Opus for demanding reasoning and long-horizon agentic work. It accepts text and images as input and returns text, with a one million token context window and a maximum output of 128 thousand tokens. Adaptive thinking is always on, and an effort parameter controls how much reasoning the model applies to a given request. Anthropic reports a reliable knowledge and training data cutoff of June 2026. Claude Fable 5.1 and Claude Mythos 5.1 share the same underlying model; the difference between them is the set of safety classifiers applied to dual-use cybersecurity and biology requests.
On the vision side, Anthropic documents improvements in reading dense charts, financial filings, and tables nested inside PDF documents, which extends the model toward document understanding, chart question answering, and spreadsheet and slide work. Reported evaluations cover agentic scientific research on Terminal-Bench-Science 0.1, agentic coding on Terminal-Bench 4.0, computer use on OSWorld 2.0, and multidisciplinary reasoning on Humanity's Last Exam. Model weights are not published.
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 slightly better overall. The two split the six vision tasks 3 to 3, but Claude Sonnet 5.5 averages 83.8% (#7 of 60) against 81.3% (#11 of 60) for Claude Fable 5.1. 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 61.4%. This is the widest gap between the two models across the benchmark's tasks.
Claude Sonnet 5.5 is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0065 per sample against $0.035. Actual costs depend on your image sizes, prompts, and output length.
Claude Fable 5.1 is faster. Across Roboflow's Vision Evals it averaged 8.3s per inference against 10.8s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.