OpenAI: GPT-5.4

Processes and generates text across a 922K token input window and 128K token output window, enabling multi-document analysis, long-form content generation, and complex reasoning over extended context. Uses a unified transformer architecture that consolidates the Codex and GPT lines, allowing seamless switching between code and natural language tasks within a single forward pass without model switching overhead.

Generates, completes, and refactors code across 40+ programming languages using a single model trained on the Codex lineage, eliminating language-specific model selection. Understands language-specific idioms, frameworks, and best practices through unified embeddings, enabling cross-language transpilation and architecture pattern recognition without separate language models.

Adapts GPT-5.4 to domain-specific tasks through supervised fine-tuning on custom datasets, enabling improved performance on specialized domains without full model retraining. Fine-tuned models are deployed as separate endpoints with custom model IDs, enabling A/B testing and gradual rollout of customized versions.

Maintains conversation history and context across multiple turns without server-side session storage, enabling stateless API design where all context is passed in each request. Conversation history is compressed and deduplicated to fit within token limits, allowing 50+ turn conversations within 922K token context window.

Analyzes images, diagrams, charts, and screenshots to extract structured information, answer visual questions, and perform OCR with layout preservation. Uses vision transformer architecture integrated into the unified model, enabling seamless switching between image and text analysis without separate vision API calls or model composition.

Executes external functions and APIs through a schema-based function registry that supports OpenAI, Anthropic, and Ollama function-calling protocols natively. Model generates structured JSON function calls with parameter validation against registered schemas, enabling deterministic tool use without prompt engineering or output parsing fragility.

Generates explicit reasoning chains and task decomposition through structured thinking patterns, enabling transparent multi-step problem solving. Model produces intermediate reasoning steps as tokens, allowing inspection of decision logic and enabling human-in-the-loop verification before final output generation.

Retrieves relevant documents and context from external knowledge bases using semantic similarity matching, enabling grounding of responses in external data without fine-tuning. Integrates with vector databases (Pinecone, Weaviate, Milvus) through standardized embedding APIs, allowing dynamic context injection during generation.

Detects and filters harmful content including hate speech, violence, sexual content, and misinformation through learned safety classifiers integrated into the model. Provides configurable safety levels and detailed violation reports without requiring separate moderation APIs, enabling real-time content filtering with sub-100ms latency.

Processes multiple requests in batches with optimized throughput and reduced per-request costs through batch API endpoints. Requests are queued, deduplicated, and processed during off-peak hours with 50% cost reduction, enabling cost-effective bulk processing of documents, code, or content without real-time latency requirements.

Generates responses incrementally through server-sent events (SSE) with token-level granularity, enabling real-time display of generated content and early termination of long-running requests. Streaming reduces perceived latency by 50-70% compared to waiting for complete response generation, and enables cancellation without wasting compute.

Generates responses that conform to provided JSON schemas, ensuring output is valid, parseable, and matches expected structure without post-processing or validation. Model constrains token generation to valid JSON paths, eliminating hallucinated fields and invalid syntax while maintaining semantic quality.