xAI: Grok 4

Processes both text and image inputs simultaneously within a 256,000 token context window, enabling extended reasoning chains across multi-page documents, codebases, and visual content. The architecture maintains token efficiency through selective attention mechanisms while preserving reasoning depth across long-form inputs, supporting complex multi-step problem decomposition without context truncation.

Executes multiple tool invocations concurrently within a single model response using a schema-based function registry. The model generates structured JSON payloads matching predefined schemas, enabling orchestration of parallel API calls, database queries, and external service integrations without sequential round-trips. Implementation uses typed function signatures with validation against provided schemas before execution.

Integrates with external knowledge bases and document stores through tool calling, enabling retrieval-augmented generation where the model queries external sources and reasons over retrieved results. The model can formulate search queries, evaluate relevance of retrieved documents, and synthesize information from multiple sources. Implementation uses semantic understanding to identify relevant search terms and evaluate document relevance without explicit ranking.

Analyzes problems to identify edge cases, potential failures, and adversarial inputs that could break proposed solutions. The model generates test cases, identifies boundary conditions, and reasons about failure modes without explicit prompting. Implementation uses reasoning patterns to systematically explore problem space and identify overlooked scenarios.

Generates responses constrained to match a provided JSON Schema, ensuring output conforms to exact field names, types, and nesting structures. The model's token generation is guided by the schema constraints, preventing invalid JSON and guaranteeing parseable structured data. Implementation uses schema-aware decoding that prunes invalid token sequences during generation, ensuring 100% schema compliance without post-processing.

Performs multi-step reasoning internally without explicit token-counting or reasoning budget controls, generating coherent reasoning chains that decompose complex problems into sub-steps. The model allocates reasoning depth implicitly based on problem complexity, using attention mechanisms to identify critical reasoning paths. Output includes both reasoning traces and final answers, enabling transparency into decision-making without explicit reasoning token management.

Generates, analyzes, and refactors code across 40+ programming languages using language-agnostic reasoning patterns. The model understands syntax, semantics, and idioms for each language, enabling cross-language code translation, bug detection, and optimization suggestions. Implementation uses abstract syntax tree (AST) reasoning internally, allowing structural code understanding without language-specific parsing.

Analyzes images of documents (PDFs rendered as images, scanned documents, screenshots) to extract structured information including text, tables, forms, and layout relationships. The model performs OCR-like text extraction with semantic understanding of document structure, enabling form field extraction, table parsing, and document classification without separate OCR preprocessing. Implementation uses visual attention mechanisms to identify document regions and their semantic relationships.

Integrates with external APIs through tool calling with built-in error handling patterns, enabling graceful degradation when external services fail. The model can reason about API errors, retry logic, and fallback strategies, generating code that handles rate limiting, timeouts, and service unavailability. Implementation uses structured error schemas to communicate failure modes back to the model for adaptive response generation.

Analyzes images to identify objects, spatial relationships, and contextual relationships between elements. The model performs scene understanding, object detection, and relationship reasoning in a single pass, enabling tasks like diagram analysis, UI/UX evaluation, and visual debugging. Implementation uses visual attention mechanisms to track object positions and relationships, supporting queries about 'what is next to X' or 'how are these elements related'.

Generates code completions that understand the broader codebase context, including imported modules, defined functions, and coding patterns. The model analyzes surrounding code to infer intent and generate completions that match existing code style and architecture. Implementation uses AST-level code understanding to track scope, variable types, and function signatures, enabling semantically-aware completions without full codebase indexing.

Maintains conversation state across multiple turns, preserving context from previous messages and building on prior reasoning. The model tracks conversation history implicitly within the context window, enabling follow-up questions, clarifications, and iterative refinement without explicit context management. Implementation uses attention mechanisms to weight recent context more heavily while maintaining access to earlier conversation threads.