claude-sonnet-4-20250514-thinkingClaude Sonnet 4 is a major upgrade over Sonnet 3.7, delivering stronger coding and reasoning with better precision and control. It reaches 72.7% on SWE-bench, while staying efficient and responsive, making it useful for everything from everyday coding to complex software projects. Improvements include better autonomous codebase navigation, fewer agent workflow errors, and more reliable handling of complex instructions.
Select an endpoint and copy a working example for this model.
from openai import OpenAI client = OpenAI( api_key="YOUR_API_KEY", base_url="https://api.apertis.ai/v1") response = client.chat.completions.create( model="claude-sonnet-4-20250514-thinking", messages=[ {"role": "user", "content": "Hello!"} ], max_tokens=1024, temperature=0.7) print(response.choices[0].message.content) # Optional: Enable context compression to reduce token usage# response = client.chat.completions.create(# model="claude-sonnet-4-20250514-thinking",# messages=[{"role": "user", "content": "Hello!"}],# extra_body={"compression": {"enabled": True, "model": "gpt-4.1-mini"}}# )modelmessagesmax_tokenstemperaturetop_pstreamtoolsreasoning_effortstream_optionsthinkingextra_bodyUse these namespaced identifiers in Cursor IDE to avoid conflicts with built-in models.
See how this model compares to others from the same provider.
Sonnet 5 is Anthropic's most capable Sonnet-class model, delivering frontier-level performance across coding, agentic workflows, and professional knowledge tasks. It supports text, image, and file inputs, features a 1M-token context window, and offers adaptive thinking with configurable reasoning levels (low, medium, high, max, and x-high) to balance speed, cost, and reasoning depth. Optimized for complex coding, long-horizon agent execution, and professional workflows, Sonnet 5 combines strong reasoning, robust instruction following, and enhanced safety features, including an updated tokenizer and real-time cyber safeguards for high-risk dual-use scenarios.
Claude Fable 5 is Anthropic's Mythos-class model, designed for autonomous knowledge work, coding, and long-running agentic workflows. It supports text, image, and file inputs with text output, includes reasoning capabilities, and features a 1M-token context window for handling complex, high-context tasks. Optimized for asynchronous and long-horizon execution, Claude Fable 5 excels at end-to-end tasks that would typically require hours, days, or weeks of human effort. It combines strong reasoning, autonomous verification and self-correction loops, and robust safeguards, making it well suited for complex research, software engineering, and large-scale knowledge work.
This is the fast version of Opus 4.8
Claude Opus 4.8 is Anthropic's most capable generally available model in the Opus family, designed for highly autonomous agents, long-horizon workflows, and advanced knowledge work. It supports text, image, and file inputs with text output, includes reasoning capabilities, and features a 1M-token context window for maintaining coherence across extended tasks and sessions. The model excels at multi-step reasoning, complex coding, and end-to-end project orchestration, including large codebases, multi-stage debugging, and long-running asynchronous agent pipelines. Beyond software engineering, it is highly effective for document drafting, presentation creation, data analysis, and memory-driven workflows, delivering consistent quality across very long outputs and complex projects.
See how this model compares to others from the same provider.
Opus 4.6 is Anthropic's more faster version of Opus 4.6 model for coding and long-running professional workflows, designed for agents that operate across entire workflows rather than single prompts. It demonstrates strong performance on large codebases, complex refactoring, and multi-step debugging, with improved contextual understanding, deeper problem decomposition, and higher reliability on challenging engineering tasks compared to earlier generations. Beyond software development, Opus 4.6 excels at sustained knowledge work, producing near production-ready documents, technical plans, and analyses in a single pass while maintaining coherence across long outputs and extended sessions. Its strength in persistence, judgment, and structured execution makes it well suited for technical design, migration planning, and end-to-end project execution.
Claude Opus 4.8 is Anthropic's most capable generally available model in the Opus family, designed for highly autonomous agents, long-horizon workflows, and advanced knowledge work. It supports text, image, and file inputs with text output, includes reasoning capabilities, and features a 1M-token context window for maintaining coherence across extended tasks and sessions. The model excels at multi-step reasoning, complex coding, and end-to-end project orchestration, including large codebases, multi-stage debugging, and long-running asynchronous agent pipelines. Beyond software engineering, it is highly effective for document drafting, presentation creation, data analysis, and memory-driven workflows, delivering consistent quality across very long outputs and complex projects.
Claude Opus 4.1 is Anthropic's upgraded flagship model, with stronger coding, reasoning, and agent-style abilities. It scores 74.5% on SWE-bench Verified and shows big improvements in multi-file refactoring, debugging accuracy, and detailed reasoning. It supports up to 64K tokens of extended thinking and is tuned for research, data analysis, and tool-assisted workflows.
Claude Fable 5 is Anthropic's Mythos-class model, designed for autonomous knowledge work, coding, and long-running agentic workflows. It supports text, image, and file inputs with text output, includes reasoning capabilities, and features a 1M-token context window for handling complex, high-context tasks. Optimized for asynchronous and long-horizon execution, Claude Fable 5 excels at end-to-end tasks that would typically require hours, days, or weeks of human effort. It combines strong reasoning, autonomous verification and self-correction loops, and robust safeguards, making it well suited for complex research, software engineering, and large-scale knowledge work.
Initialized observational baseline with no recorded failures