OpenAI: GPT-5.4 Pro

Processes up to 922,000 input tokens in a single request using a unified transformer architecture optimized for extended context retention. The model maintains coherence and reasoning quality across document-length inputs by employing hierarchical attention mechanisms and sparse attention patterns that reduce computational complexity while preserving long-range dependencies. This enables analysis of entire codebases, research papers, or multi-document conversations without context truncation or sliding-window approximations.

Implements advanced reasoning through multi-step thought decomposition where the model explicitly breaks complex problems into sub-problems, evaluates intermediate steps, and backtracks when necessary. Built on GPT-5.4's unified architecture with reinforced training on reasoning-heavy tasks, this capability uses internal scaffolding to improve accuracy on math, logic, and multi-hop inference problems. The model exposes reasoning traces that developers can parse to understand decision pathways and validate correctness.

Adapts the base GPT-5.4 Pro model to custom domains or tasks using parameter-efficient fine-tuning techniques (LoRA, prefix tuning) that update only a small percentage of model parameters. Accepts training datasets in JSONL format and produces a fine-tuned model variant that can be deployed via the standard API. Supports supervised fine-tuning for instruction-following and reinforcement learning from human feedback (RLHF) for preference optimization. Includes automatic hyperparameter tuning and validation set evaluation.

Generates images from natural language descriptions using a diffusion-based architecture integrated with the GPT-5.4 text understanding pipeline. The model accepts detailed textual prompts and produces high-fidelity images by mapping semantic concepts from language to visual features through a learned cross-modal embedding space. Supports iterative refinement where users can request modifications (e.g., 'make the sky more dramatic') and the model regenerates with context from previous generations, enabling conversational image creation.

Generates and completes code by accepting the full context of a developer's codebase (imports, class definitions, function signatures, style conventions) and producing code that adheres to existing patterns and architecture. The model uses the 922K token context window to ingest entire modules or projects, enabling it to generate code that respects naming conventions, dependency structures, and architectural patterns without explicit instructions. Supports multiple languages (Python, JavaScript, Go, Rust, etc.) with language-specific optimizations for syntax and idioms.

Enables the model to invoke external tools and APIs by accepting a schema definition of available functions and returning structured function calls with arguments. The model parses the schema, determines which functions are relevant to the user's request, and generates properly-formatted function calls with validated arguments. Supports chaining multiple function calls in a single response and handles error recovery when function execution fails. Integrates with OpenAI's native function-calling API and supports custom tool registries via JSON schema.

Accepts external document collections and retrieves relevant passages to augment the model's responses, enabling it to answer questions grounded in specific documents or knowledge bases. The model uses semantic similarity matching to identify relevant context from a vector database or document store, then incorporates retrieved passages into the prompt to generate factually-grounded answers. Supports hybrid search combining semantic and keyword matching, and can cite sources by returning document references alongside answers.

Analyzes text inputs and outputs for harmful content (hate speech, violence, sexual content, etc.) and applies configurable filtering policies before processing or returning responses. The model uses learned classifiers trained on safety datasets to detect problematic content with configurable sensitivity levels. Supports custom policy definitions where organizations can specify which content categories to block, allow, or flag for review. Returns moderation metadata (confidence scores, detected categories) for transparency and auditing.

Extracts structured information from unstructured text by accepting a JSON schema definition and returning validated structured data that conforms to the schema. The model parses natural language or semi-structured text and maps it to defined fields, handling type coercion, validation, and error reporting. Supports nested schemas, arrays, enums, and custom validation rules. Returns extraction confidence scores and flags ambiguous or missing fields for manual review.

Maintains conversation state across multiple turns by retaining message history and using the 922K token context window to preserve full conversation context without external memory systems. The model can reference earlier messages, maintain consistent character or persona across turns, and handle context-dependent requests (e.g., 'what did I say earlier?'). Supports automatic context summarization when approaching token limits, allowing indefinitely long conversations with graceful degradation.

Processes multiple requests in batches with delayed execution to reduce per-request costs by up to 50%. The model queues requests and processes them together, amortizing overhead and enabling more efficient GPU utilization. Supports asynchronous job submission with webhook callbacks or polling for result retrieval. Ideal for non-time-sensitive workloads like data processing, report generation, or bulk content creation. Provides job status tracking and result caching to avoid reprocessing identical requests.