Claude Sonnet 5

Sonnet 5 is Anthropic's most capable Sonnet-class model, with frontier performance across coding, agents, and professional work. It supports adaptive thinking with selectable reasoning effort levels (low, medium, high, max, and x-high), a 1M-token context window, and text, image, and file inputs. Sonnet 5 uses an updated tokenizer and includes real-time cyber safeguards that block certain high-risk dual-use activities.

Claude Fable 5

Claude Fable 5 is Anthropic's first publicly available Mythos-class model, built for autonomous knowledge work and coding. It supports text, image, and file inputs with text output, offers reasoning support, and has a 1M-token context window. Designed for long-running, complex, and asynchronous tasks that previously required frequent human check-ins, it is particularly strong at end-to-end work that would otherwise take a person hours, days, or weeks, taking on problems that are long-running, ambiguous, or highly multi-step. It executes well-scoped tasks with few mistakes, automatically self-correcting through verification loops, and ships with robust safeguards that fall back to Claude Opus 4.8 on a small fraction of high-risk queries.

Claude Opus 4.7

Anthropic's next-generation Opus model, built for long-running, asynchronous agents. Building on the coding and agentic strengths of Opus 4.6, it delivers stronger performance on complex, multi-step tasks and more reliable agentic execution across extended workflows. Especially effective for asynchronous agent pipelines where tasks unfold over time - large codebases, multi-stage debugging, and end-to-end project orchestration. Beyond coding, brings improved knowledge work capabilities from drafting documents and building presentations to analyzing data. Maintains coherence across very long outputs and extended sessions, making it a strong default for tasks that require persistence, judgment, and follow-through.

Claude Sonnet 4.6

Sonnet 4.6 is Anthropic's most capable Sonnet-class model yet, with frontier performance across coding, agents, and professional work. It excels at iterative development, complex codebase navigation, end-to-end project management with memory, polished document creation, and confident computer use for web QA and workflow automation.

Claude Opus 4.6

Anthropic's strongest model for coding and long-running professional tasks. Built for agents that operate across entire workflows rather than single prompts, excelling at large codebases, complex refactors, and multi-step debugging. Shows deeper contextual understanding, stronger problem decomposition, and greater reliability on hard engineering tasks. Also excels at sustained knowledge work, producing near-production-ready documents, plans, and analyses in a single pass.

Claude Opus 4.1

Claude Opus 4.1 is an updated version of Anthropic's flagship model, offering improved performance in coding, reasoning, and agentic tasks. It achieves 74.5% on SWE-bench Verified and shows notable gains in multi-file code refactoring, debugging precision, and detail-oriented reasoning. The model supports extended thinking up to 64K tokens and is optimized for tasks involving research, data analysis, and tool-assisted reasoning.

Claude Sonnet 5

Sonnet 5 is Anthropic's most capable Sonnet-class model, with frontier performance across coding, agents, and professional work. It supports adaptive thinking with selectable reasoning effort levels (low, medium, high, max, and x-high), a 1M-token context window, and text, image, and file inputs. Sonnet 5 uses an updated tokenizer and includes real-time cyber safeguards that block certain high-risk dual-use activities.

Claude Fable 5

Claude Fable 5 is Anthropic's first publicly available Mythos-class model, built for autonomous knowledge work and coding. It supports text, image, and file inputs with text output, offers reasoning support, and has a 1M-token context window. Designed for long-running, complex, and asynchronous tasks that previously required frequent human check-ins, it is particularly strong at end-to-end work that would otherwise take a person hours, days, or weeks, taking on problems that are long-running, ambiguous, or highly multi-step. It executes well-scoped tasks with few mistakes, automatically self-correcting through verification loops, and ships with robust safeguards that fall back to Claude Opus 4.8 on a small fraction of high-risk queries.

Claude Opus 4.7

Anthropic's next-generation Opus model, built for long-running, asynchronous agents. Building on the coding and agentic strengths of Opus 4.6, it delivers stronger performance on complex, multi-step tasks and more reliable agentic execution across extended workflows. Especially effective for asynchronous agent pipelines where tasks unfold over time - large codebases, multi-stage debugging, and end-to-end project orchestration. Beyond coding, brings improved knowledge work capabilities from drafting documents and building presentations to analyzing data. Maintains coherence across very long outputs and extended sessions, making it a strong default for tasks that require persistence, judgment, and follow-through.

Claude Sonnet 4.6

Sonnet 4.6 is Anthropic's most capable Sonnet-class model yet, with frontier performance across coding, agents, and professional work. It excels at iterative development, complex codebase navigation, end-to-end project management with memory, polished document creation, and confident computer use for web QA and workflow automation.

Claude Opus 4.6

Anthropic's strongest model for coding and long-running professional tasks. Built for agents that operate across entire workflows rather than single prompts, excelling at large codebases, complex refactors, and multi-step debugging. Shows deeper contextual understanding, stronger problem decomposition, and greater reliability on hard engineering tasks. Also excels at sustained knowledge work, producing near-production-ready documents, plans, and analyses in a single pass.

Claude Opus 4.1

Claude Opus 4.1 is an updated version of Anthropic's flagship model, offering improved performance in coding, reasoning, and agentic tasks. It achieves 74.5% on SWE-bench Verified and shows notable gains in multi-file code refactoring, debugging precision, and detail-oriented reasoning. The model supports extended thinking up to 64K tokens and is optimized for tasks involving research, data analysis, and tool-assisted reasoning.