GPT-4

GPT-4 processes both text and image inputs through a single transformer-based architecture that encodes visual information into the same token space as language tokens, enabling joint reasoning across modalities. The model uses vision encoders to convert images into embeddings that integrate seamlessly with the language model's attention mechanisms, allowing it to answer questions about images, read text within images, and reason about visual content in context with textual prompts.

GPT-4 supports an 8K token context window (later extended to 32K and 128K in variants), enabling the model to maintain coherence and reasoning across significantly longer documents, codebases, or conversation histories than GPT-3.5. The implementation uses standard transformer attention with optimizations to manage computational complexity at scale, allowing developers to pass entire files, specifications, or multi-turn conversations without truncation.

GPT-4 extracts structured data from unstructured text and generates outputs conforming to specified schemas (JSON, XML, CSV) through instruction-following and constraint adherence. The model parses natural language, documents, or semi-structured data and maps it to defined schemas, enabling developers to build data extraction pipelines without custom parsing logic, though output validation is still required.

GPT-4 maintains coherent multi-turn conversations by tracking context across exchanges, using transformer attention to weight relevant prior messages and maintain consistency in responses. The model can engage in extended dialogues, remember user preferences and context from earlier turns, and adapt responses based on conversation history, enabling developers to build conversational AI systems without explicit state management.

GPT-4 decomposes complex problems into sub-tasks and generates step-by-step plans through chain-of-thought reasoning patterns, using transformer attention to identify dependencies and logical structure. The model can break down multi-step problems, generate execution plans, and reason about intermediate steps, enabling developers to build planning and reasoning systems without explicit planning algorithms.

GPT-4 demonstrates strong in-context learning capabilities, allowing developers to specify task behavior through natural language instructions and examples without fine-tuning. The model uses transformer attention to recognize patterns in provided examples and apply them to new inputs, enabling rapid task adaptation by simply modifying the prompt structure, example selection, and instruction clarity.

GPT-4 generates syntactically correct, idiomatic code across Python, JavaScript, TypeScript, Java, C++, Go, Rust, SQL, and 30+ other languages through training on diverse code repositories and documentation. The model understands language-specific idioms, standard libraries, and common patterns, enabling it to generate production-quality code snippets, complete functions, and suggest refactorings with language-aware context awareness.

GPT-4 demonstrates improved mathematical reasoning capabilities compared to GPT-3.5, solving algebra, calculus, geometry, and logic problems through step-by-step symbolic manipulation and reasoning. The model uses chain-of-thought patterns to break complex problems into intermediate steps, enabling it to work through multi-step proofs, equation solving, and formal logic problems with higher accuracy than previous versions.

GPT-4 demonstrates superior instruction-following capabilities, reliably adhering to complex constraints, output format specifications, and multi-part instructions through improved training on instruction-following datasets. The model maintains consistency across multiple requests with the same system prompt, enabling developers to build reliable, deterministic workflows by specifying precise constraints and expected output formats.

GPT-4 answers factual questions across diverse domains (history, science, current events, technical topics) by retrieving and synthesizing knowledge from its training data, which includes text up to April 2023. The model uses transformer attention to identify relevant knowledge patterns and synthesize coherent answers, though it operates without real-time information access or explicit knowledge base retrieval, making it subject to knowledge cutoff and hallucination risks.

GPT-4 generates creative content (stories, poetry, marketing copy, dialogue) with improved stylistic control and coherence compared to GPT-3.5, using transformer attention to maintain narrative consistency across long-form content. The model can adapt to specified writing styles, tones, and genres through prompt engineering, enabling developers to generate diverse content types without fine-tuning.

GPT-4 translates between 100+ languages with improved fluency and cultural adaptation compared to GPT-3.5, using transformer attention to understand semantic meaning and idiomatic expressions across languages. The model can translate technical documents, creative content, and conversational text while preserving tone and meaning, though it lacks the specialized optimization of dedicated translation services.

GPT-4 incorporates safety training through RLHF and constitutional AI techniques to reduce generation of harmful, biased, or inappropriate content compared to GPT-3.5. The model uses learned safety patterns to refuse unsafe requests, provide warnings for sensitive topics, and generate more balanced perspectives on controversial subjects, though it is not immune to jailbreaks or adversarial prompts.