Claude Fable 5.1 vs GPT-5 Nano
Compare Claude Fable 5.1 and GPT-5 Nano side-by-side. See comparisons of price, response speed, accuracy, and file support to choose the best model for your task.
Overview
Claude Fable 5.1 vs GPT-5 Nano Overview
Everything you need to know about these AI models, including capabilities, performance, pricing, and technical details.
Anthropic / Claude Fable 5.1
maxDescription
Anthropic's most capable generally available model, sitting above the Opus line. Fable 5.1 is built for long, unsupervised work: multi-day coding, agentic research, and tasks that run across dozens of tool calls. It reads text and images, maintains a very large context, and reasons before every answer at an adjustable depth. Its knowledge is the most recent of any Claude model. Anthropic positions Opus 5 as the starting point for most work, and Fable 5.1 for teams whose evaluations demand a higher ceiling.
OpenAI / GPT-5 Nano
lightDescription
OpenAI's smallest and cheapest GPT-5 model, built for speed above all. GPT-5 Nano is made for summarisation, classification, and other well-defined tasks that run at high volume. It reads text and images, with adjustable reasoning effort. Reasoning depth is shallow by design and its knowledge is dated. The trade is cost: it is among the cheapest models in the catalog.
About
About
Capabilities
Capabilities
Comparison
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Anthropic / Claude Fable 5.1
maxLong-Running Coding
Anthropic's strongest model for multi-day coding sessions and codebase research. Built to sustain complex software engineering across many steps and tool calls without a human checking in at each one.
Adaptive Thinking Always On
Reasoning is always on, with adjustable effort rather than an on-off switch. Steer depth up for the hardest problems or down for lighter work, without restructuring the prompt each time.
Room For Whole Projects
A very large context window, among the largest available. Load an entire codebase, a long research corpus, or a large document set and work across all of it in a single session.
Document-Heavy Analysis
Built for document-heavy analysis and multi-step automation alongside coding. Suited to work that reads across large, mixed source material and produces structured, reviewable output.
Long-Horizon Agent Work
Its defining strength. Retains context, uses tools, evaluates results, and works on a task through many steps. Built for agents who run for a long time with little supervision.
Text And Image Input
Reads text and images in the same prompt, so you can send screenshots, diagrams, or scanned pages alongside your question. Output is text only. Audio and video are not accepted.
OpenAI / GPT-5 Nano
lightCoding Sub-Tasks
Suited to small, well-scoped coding jobs and to acting as a fast sub-agent inside a larger system, rather than tackling whole problems. Reasoning depth is limited, so keep the scope tight.
Adjustable Reasoning Effort
Reasoning effort runs from minimal through low, medium, and high. Even at higher effort it is a shallow reasoner by design, so it suits speed-sensitive work rather than hard multi-step problems.
Room For Long Documents
A large context window, enough to summarise long reports or classify big batches of text in a single pass without splitting the input into separate requests.
Summarisation And Classification
OpenAI names summarisation and classification as its core uses. Built to process high volumes of text quickly and cheaply rather than to produce polished long-form writing.
Analysis On Older Knowledge
Its training data is dated by current standards, so it is fine for processing text you supply but weaker on recent events, which need live sources rather than the model's own knowledge.
Text And Image Input
Reads text and images, so you can send screenshots or scanned pages alongside your prompt. Output is text only. OpenAI states audio and video are not supported.
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Compare Responses
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