Gemini 2.5 Pro vs Claude Fable 5.1
Compare Gemini 2.5 Pro and Claude Fable 5.1 side-by-side. See comparisons of price, response speed, accuracy, and file support to choose the best model for your task.
Overview
Gemini 2.5 Pro vs Claude Fable 5.1 Overview
Everything you need to know about these AI models, including capabilities, performance, pricing, and technical details.
Google / Gemini 2.5 Pro
proDescription
Google's previous flagship reasoning model remains effective across code, mathematics, and STEM. Gemini 2.5 Pro reads text, images, audio, video, and PDFs, and holds a million tokens of context, so entire codebases and document sets fit in one session. A thinking model built for complex problems rather than quick answers. Superseded by newer Gemini releases, though the capability set remains broad.
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.
About
About
Capabilities
Capabilities
Comparison
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Google / Gemini 2.5 Pro
proCoding Across Large Repositories
Google describes it as reasoning over complex problems in code, and says it can comprehend entire code repositories. Built for understanding a whole system rather than editing one file inside it.
A Thinking Model by Design
Google calls this a thinking model, meaning it reasons through a problem before answering rather than generating straight through. Aimed at maths, STEM, and problems where the route to the answer matters.
Room for Whole Codebases
A one-million-token context window. Load a full repository, a research corpus, or dozens of PDFs and ask questions across all of it without chunking the input into separate requests.
Writing From Dense Source Material
Strongest when writing from something rather than from nothing. Give it the datasets, papers, or documents and it will produce structured output grounded in what you supplied.
Analysis Over Large Datasets
Google names analysing large datasets, codebases, and documents as a core use. Suited to synthesis across many sources at once, where the volume of material is the hard part.
Audio, Video, Images, and PDFs
Google's own model page lists audio, images, video, text, and PDF as accepted inputs. One of the broadest input ranges available, and unusual in accepting both audio and video.
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.
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