SAM 3 vs Claude Fable 5+ 2 others
Compare SAM 3, Claude Fable 5, and 2 other vision models side-by-side. Test these models on Object Detection in the Playground.
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Models in this comparison
Model Overviews
Released on November 19th, 2025, Segment Anything 3 (SAM 3) is a zero-shot image segmentation model that “detects, segments, and tracks objects in images and videos based on concept prompts.” This model was developed by Meta as the third model in the Segment Anything series.
Unlike its previous SAM models (Segment Anything and Segment Anything 2), you can provide SAM 3 with the prompt “shipping container” and it will generate precise segmentation masks for all shipping containers in an image. SAM 3 generates segmentation masks that correspond to the location of the objects found with a text prompt.
SAM 3 vs Claude Fable 5 Comparison Table + 2 others
| Property | SAM 3 | Claude Fable 5 | Gemini 3.1 Pro | GPT-5.5 |
|---|---|---|---|---|
| Organization | Meta | Anthropic | OpenAI | |
| Category | closed | closed | closed | closed |
| Modality | multimodal | multimodal | multimodal | multimodal |
| Release Date | Nov 2025 | Jun 2026 | Feb 2026 | Apr 2026 |
| Context Window | — | 1.0M | 1.0M | 1.0M |
| Parameters | ||||
| License | Proprietary | Proprietary | Proprietary | Proprietary |
| Pricing per 1M tokens | ||||
| Input $/1M | $10.00 | $2.00 | $5.00 | |
| Output $/1M | $50.00 | $12.00 | $30.00 | |
| Vision Tasks | ||||
| Object Detection | Demo | Demo | Demo | |
| Captioning | Demo | Demo | ||
| Classification | Demo | Demo | ||
| OCR | Demo | Demo | ||
| Vision Language | ||||
| Visual Question Answering | Demo | Demo | ||
| Chart Question Answering | ||||
| Document Question Answering | ||||
| Instance Segmentation | ||||
| Promptable Concept Segmentation | Demo | |||
| Video Object Tracking | ||||
| Zero Shot Segmentation | ||||
| Model Features | ||||
| Foundation Vision | ||||
| LLMs with Vision Capabilities | ||||
| Multimodal Vision | ||||
| Zero-shot Detection | ||||
Vision Evalspass/fail results · 67 prompts Score key:≥75%40–74%<40% | ||||
| Visual Understanding | ||||
| Overall Score | 74.63% | 75.76% | 77.61% | |
| Avg Response Time | 16.44s | 6.13s | 30.12s | |
| Median input tokensincl. image tokens | 1.1K | 1.4K | ||
| Median output tokens | 11 | 138 | ||
| Est. cost / taskon this benchmark | $0.0024 | $0.011 | ||
| Defect Detection | 73.3%(11/15) | 73.3%(11/15) | 86.7%(13/15) | |
| Document Understanding | 77.8%(7/9) | 88.9%(8/9) | 88.9%(8/9) | |
| Object Counting | 30%(3/10) | 44.4%(4/9) | 30%(3/10) | |
| Object Understanding | 100%(14/14) | 92.9%(13/14) | 92.9%(13/14) | |
| Spatial Understanding | 78.9%(15/19) | 73.7%(14/19) | 78.9%(15/19) | |
| OCR | ||||
| Overall Score | 89.52% | 81.22% | ||
| Avg Response Time | 3.11s | 5.16s | ||
| Median input tokensincl. image tokens | 1.1K | 105 | ||
| Median output tokens | 12 | 83 | ||
| Est. cost / taskon this benchmark | $0.0024 | $0.0030 | ||
| Focused Scene OCR | 94.9%(94/99) | 77.8%(77/99) | ||
| Handwritten Math | 90%(9/10) | 40%(4/10) | ||
| License Plate Recognition | 90%(27/30) | 93.3%(28/30) | ||
| Text Recognition | 86.7%(26/30) | 83.3%(25/30) | ||
| VQA & Extraction | 81.7%(49/60) | 86.7%(52/60) | ||
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