Claude Fable 5.1 vs Gemma 3 27B
Compare Claude Fable 5.1 and Gemma 3 27B side-by-side. See how these vision models stack up in OCR, Image Captioning, and Open Prompt.
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Claude Fable 5.1 vs Gemma 3 27B Comparison Table
Evals updated September 2, 2026Pricing updated September 2, 2026
| Property | Claude Fable 5.1 | Gemma 3 27B |
|---|---|---|
| Organization | Anthropic | |
| Category | closed | open |
| Modality | multimodal | multimodal |
| Release Date | Sep 2026 | Mar 2025 |
| Context Window | 1.0M | 128K |
| Parameters | ||
| License | Proprietary | Custom |
| Pricing per 1M tokens | ||
| Input $/1M | $10.00 | $0.080 |
| Output $/1M | $50.00 | $0.450 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Chart Question Answering | ||
| Classification | Demo | |
| Document Question Answering | ||
| Image Tagging | ||
| Multi-Label Classification | ||
| OCR | Demo | Demo |
| Vision Language | ||
| Visual Question Answering | Demo | Demo |
| Object Detection | 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% | Not evaluated |
| Avg cost / sample | $0.035 | – |
| Avg speed / sample | 8.28s | – |
| By task | ||
| Object Detection (low) | 61.4% ±0.5, Mean of 3 runs, range 61.0 to 62.0 | – |
| Object Detection (high) | 65.0% ±0.4, Mean of 3 runs, range 64.6 to 65.3 | – |
| Counting (low) | 69.4% ±2.7, Mean of 3 runs, range 66.2 to 71.6 | – |
| Counting (high) | 73.0% ±4.7, Mean of 3 runs, range 67.6 to 77.0 | – |
| Identification (low) | 97.9% ±1.6, Mean of 3 runs, range 96.9 to 100.0 | – |
| Identification (high) | 96.9% ±3.1, Mean of 3 runs, range 93.8 to 100.0 | – |
| OCR (low) | 94.0% ±0.4, Mean of 3 runs, range 93.6 to 94.4 | – |
| OCR (high) | 93.6% ±0.2, Mean of 3 runs, range 93.5 to 93.9 | – |
| Data Extraction (low) | 93.1% ±0.5, Mean of 3 runs, range 92.8 to 93.8 | – |
| Data Extraction (high) | 93.5% ±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 | – |
| Reasoning (high) | 73.1% ±2.0, Mean of 3 runs, range 71.5 to 75.5 | – |
Claude Fable 5.1 vs Gemma 3 27B: 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.
Gemma 3 27B, announced on March 12, 2025, is the largest open-weight model in Google DeepMind’s Gemma 3 family. With around 27 billion parameters, it is multimodal—accepting both text and images as input and producing text outputs. It supports a 128,000-token context window and typically generates up to ~8,192 tokens, enabling it to process multi-page documents, extended conversations, or large batches of images in a single prompt.
The model is instruction-tuned in its “-it” variants for chat, reasoning, and summarization use cases, and it supports structured outputs and function calling. It is multilingual, covering over 140 languages. Deployment is flexible: the full BF16 model requires ~46 GB of VRAM, but quantization-aware training (QAT) versions in 8-bit or 4-bit reduce the footprint significantly, allowing more accessible use outside large-scale clusters. While it delivers stronger reasoning and multimodal performance than smaller Gemma models, it remains lighter and more open than proprietary systems, making it well-suited for research, development, and fine-tuned applications.
Frequently Asked Questions
Gemma 3 27B has not yet been evaluated on Roboflow's current Vision Evals, so this comparison shows specs, licensing, and pricing rather than benchmark scores.
Claude Fable 5.1 is released under Proprietary, while Gemma 3 27B uses Custom. Licensing often matters more than raw accuracy for commercial deployments, so check the terms against how you plan to ship.
Yes. The comparison demo on this page runs both models on the same image side by side for OCR and image captioning in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.