Anthropic: Claude Opus 4

Claude Opus 4 processes code files and repositories up to 200K tokens in a single request, enabling analysis of entire codebases without chunking or retrieval. The model uses transformer-based attention mechanisms optimized for long sequences, allowing it to maintain coherence across multi-file dependencies, architectural patterns, and historical context. This enables generation of code that respects existing patterns and avoids conflicts across large projects.

Claude Opus 4 implements extended thinking patterns that allow the model to reason through multi-step problems by explicitly working through intermediate steps before generating final answers. This is achieved through transformer-based token prediction with learned reasoning tokens that don't appear in the output but guide internal computation. The model can decompose ambiguous requirements into sub-tasks, identify dependencies, and validate solutions against constraints before committing to output.

Claude Opus 4 has built-in safety training that reduces generation of harmful content (violence, hate speech, illegal activities), but developers can implement additional custom moderation via system prompts and output filtering. The model's training includes constitutional AI principles that guide it toward helpful, harmless, and honest responses. For applications requiring stricter policies, developers can implement post-generation filtering or use system prompts to enforce domain-specific safety rules. The model will refuse certain requests but may not catch all edge cases.

Claude Opus 4 accepts images as input and can analyze screenshots of code editors, architecture diagrams, UI mockups, and system designs to extract information and generate corresponding code or documentation. The model uses vision transformer architecture to parse visual elements, recognize code syntax highlighting patterns, and understand spatial relationships in diagrams. This enables workflows where developers can screenshot a design and have the model generate implementation code or documentation.

Claude Opus 4 maintains conversation state across multiple API calls, allowing developers to build interactive workflows where each turn builds on previous context. The model implements a message history mechanism where prior exchanges inform subsequent responses, enabling iterative refinement of code, requirements, or solutions. This is achieved through explicit message passing in the API (not implicit session state), requiring the client to manage conversation history and resend context on each request.

Claude Opus 4 supports constrained output generation where developers provide a JSON schema and the model generates responses guaranteed to conform to that schema. This is implemented via token-level constraints during decoding — the model's output tokens are filtered at generation time to only allow tokens that maintain schema validity. This enables reliable extraction of structured data (entities, relationships, classifications) without post-processing or validation logic.

Claude Opus 4 implements function calling via a schema-based tool registry where developers define available functions as JSON schemas and the model generates structured tool-use requests indicating which function to call with what parameters. The model's output includes tool-use blocks that applications parse to invoke actual functions, enabling agentic workflows where the model decides when and how to use external tools. This is distinct from simple prompt-based tool description — the model's training includes explicit tool-use tokens that guide generation toward valid function calls.

Claude Opus 4 supports batch processing via Anthropic's Batch API, where developers submit multiple requests in a single batch job that processes asynchronously with 50% cost reduction compared to real-time API calls. Requests are queued and processed during off-peak hours, with results returned via webhook or polling. This is implemented as a separate API endpoint that accepts JSONL-formatted request batches and returns results in the same format, enabling cost-effective processing of large volumes of data without real-time latency requirements.

Claude Opus 4 allows developers to provide custom system prompts that define the model's behavior, personality, and constraints for specific use cases. The system prompt is sent with every API request and shapes how the model interprets user input and generates responses. This enables building domain-specific assistants (legal advisor, medical consultant, code reviewer) by injecting specialized instructions, constraints, and knowledge without fine-tuning. The model respects system-level instructions with higher priority than user input, enabling guardrails and role-based behavior.

Claude Opus 4 can generate code and reason about execution results when integrated with code execution environments (Jupyter, sandboxed Python, Node.js). The model generates code, receives execution output or errors, and iteratively refines the code based on feedback. This is not a built-in capability but is enabled by tool-use integration where code execution is a tool the model can invoke. The model learns from error messages and stack traces to fix bugs and improve solutions across multiple iterations.

Claude Opus 4 can be integrated with vector databases and semantic search systems to implement RAG workflows where relevant documents are retrieved and injected into the prompt before generation. The model processes retrieved context and generates responses grounded in that context, reducing hallucination on factual questions. This is not a built-in capability but is enabled through prompt engineering and tool-use integration where document retrieval is a tool the model can invoke. The model can reason about which documents are relevant and request additional retrieval if needed.