Claude Fable 5.1 vs GPT-5.6 Luna
Compare Claude Fable 5.1 and GPT-5.6 Luna side-by-side. See how these vision models stack up in Object Detection, OCR, Image Captioning, Open Prompt, and Classification.
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Models in this comparison
Claude Fable 5.1 vs GPT-5.6 Luna on Vision Evals
Claude Fable 5.1 scores higher on all five Vision Evals tasks.
The widest gap is OCR, where Claude Fable 5.1 leads 72.9% to 51.8%.
Overall, Claude Fable 5.1 averages 74.7% (#14 of 63) against 64.7% (#29 of 63) for GPT-5.6 Luna.
GPT-5.6 Luna is cheaper ($0.0011 vs $0.039 per sample), while Claude Fable 5.1 is faster (8.3s vs 8.6s per sample).
Claude Fable 5.1 vs GPT-5.6 Luna Comparison Table
Evals updated October 13, 2026Pricing updated October 17, 2026
| Property | Claude Fable 5.1 | GPT-5.6 Luna |
|---|---|---|
| Organization | Anthropic | OpenAI |
| Category | closed | closed |
| Modality | multimodal | multimodal |
| Release Date | Sep 2026 | Jul 2026 |
| Context Window | 1.0M | 1.5M |
| Parameters | Unknown | Unknown |
| License | Proprietary | Proprietary |
| Commercial use | commercial use per vendor terms | commercial use per vendor terms |
| Pricing per 1M tokens | ||
| Input $/1M | $10.00 | $0.200 |
| Output $/1M | $50.00 | $1.20 |
| Vision Tasks | ||
| Captioning | Demo | Demo |
| Chart Question Answering | Supported | Supported |
| Classification | Demo | Demo |
| Document Question Answering | Supported | Supported |
| Image Tagging | Supported | Supported |
| Multi-Label Classification | Supported | Supported |
| Object Detection | Demo | Demo |
| OCR | Demo | Demo |
| Vision Language | Supported | Supported |
| Visual Question Answering | Demo | Demo |
| Model Features | ||
| Foundation Vision | Supported | Supported |
| LLMs with Vision Capabilities | Supported | Supported |
| Multimodal Vision | Supported | Supported |
Vision Evalsground-truth scores across 5 vision tasks, pooled at low effort | ||
| Overall | 74.7% | 64.7% |
| Avg cost / sample | $0.039 | $0.0011 |
| Avg speed / sample | 8.35s | 8.60s |
| By task | ||
| Object Detection (low) | 61.4% ±0.5, Mean of 3 runs, range 61.0 to 62.0 | 61.0% ±1.2, Mean of 3 runs, range 59.9 to 62.2 |
| Object Detection (high) | 65.0% ±0.4, Mean of 3 runs, range 64.6 to 65.3 | 62.3% ±1.2, Mean of 3 runs, range 61.4 to 63.8 |
| Counting (low) | 69.4% ±2.7, Mean of 3 runs, range 66.2 to 71.6 | 67.1% ±1.4, Mean of 3 runs, range 66.2 to 68.9 |
| Counting (high) | 73.0% ±4.7, Mean of 3 runs, range 67.6 to 77.0 | 70.7% ±3.4, Mean of 3 runs, range 66.2 to 73.0 |
| Identification (low) | 97.9% ±1.6, Mean of 3 runs, range 96.9 to 100.0 | 83.3% ±1.6, Mean of 3 runs, range 81.3 to 84.4 |
| Identification (high) | 96.9% ±3.1, Mean of 3 runs, range 93.8 to 100.0 | 84.4% ±6.3, Mean of 3 runs, range 78.1 to 90.6 |
| OCR (low) | 72.9% | 51.8% |
| by category |
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| OCR (high) | 75.3% | 55.7% |
| by category |
|
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| Reasoning (low) | 72.0% ±1.3, Mean of 3 runs, range 70.9 to 73.5 | 60.5% ±5.0, Mean of 3 runs, range 55.0 to 64.9 |
| Reasoning (high) | 73.1% ±2.0, Mean of 3 runs, range 71.5 to 75.5 | 65.6% ±3.6, Mean of 3 runs, range 60.9 to 68.2 |
Claude Fable 5.1 vs GPT-5.6 Luna: 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.
GPT-5.6 Luna is the fastest and most cost-efficient model in OpenAI's GPT-5.6 family, which also includes Sol (the flagship tier) and Terra (the balanced mid-tier). Introduced under a new naming convention where the generation number (5.6) and a durable capability tier name (Luna, Terra, Sol) together define each model, Luna occupies the lightweight end of the family and is designed for high-volume, latency-sensitive workloads such as summarization, drafting, autocomplete, classification, and routine automation. The GPT-5.6 family as a whole advances capabilities in software engineering, computer use, professional knowledge work, scientific research, and cybersecurity, with all three tiers rated at the "High" capability level under OpenAI's Preparedness Framework for both cybersecurity and biological/chemical risk domains.
GPT-5.6 Luna supports multimodal input and function calling, and shares the family's 1.5 million token context window. On Terminal-Bench 2.1, Luna scores 82.5%, and on the Artificial Analysis Coding Agent Index it outperforms comparable models at roughly one-quarter the estimated cost of higher-tier alternatives. Luna supports the GPT-5.6 prompt caching scheme, which introduces explicit cache breakpoints and a 30-minute minimum cache life. The model was previewed on June 26, 2026 to a limited group of trusted partners via the OpenAI API and Codex, with general availability rolling out on July 9, 2026 across ChatGPT, Codex, and the API.
Frequently Asked Questions
On Roboflow's Vision Evals, Claude Fable 5.1 performed better. It scores higher on all five vision tasks and averages 74.7% (#14 of 63) against 64.7% (#29 of 63) for GPT-5.6 Luna. These scores measure vision capabilities only; either model may perform differently on work outside this benchmark's tasks.
Yes. On the Vision Evals OCR benchmark at low effort, Claude Fable 5.1 leads with 72.9% against 51.8%. This is the widest gap between the two models across the benchmark's tasks.
GPT-5.6 Luna is cheaper to run. Across Roboflow's Vision Evals task mix it averaged $0.0011 per sample against $0.039. Claude Fable 5.1 is priced at $10.00 per 1M input tokens and $50.00 per 1M output; GPT-5.6 Luna is priced at $0.20 per 1M input tokens and $1.20 per 1M output. 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 8.6s. Latency varies with image size, prompt length, and provider load, so treat these as relative rather than guaranteed figures.
Yes. The comparison demo on this page runs both models on the same image side by side for object detection and OCR in the free Roboflow Playground. You can try it instantly, and a free account unlocks unlimited runs.