OpenAI: GPT-4 Turbo

Processes both text and image inputs simultaneously through a unified transformer architecture, enabling the model to reason about visual content and generate coherent text responses. The vision encoder converts images into token embeddings that are interleaved with text tokens in the same attention mechanism, allowing cross-modal reasoning without separate vision-language fusion layers.

Enforces JSON schema compliance on model outputs when processing vision inputs, using constrained decoding to guarantee valid JSON structure without post-processing. The model's token generation is guided by a schema validator that prunes invalid tokens at each step, ensuring the output conforms to a user-specified JSON schema while maintaining semantic understanding of image content.

Enables the model to invoke external functions based on visual analysis, using a schema-based function registry that maps image understanding to API calls. The model generates function names and arguments by analyzing image content, with the function calling interface supporting multiple concurrent function invocations and automatic parameter type coercion based on the schema definition.

Processes up to 128,000 tokens (approximately 96,000 words) in a single request, enabling analysis of entire documents, codebases, or conversation histories without truncation. The model uses a sliding window attention mechanism with sparse attention patterns to manage the computational cost of long sequences, allowing efficient processing of multi-document inputs and maintaining coherence across extended contexts.

Generates syntactically valid code across 40+ programming languages using transformer-based token prediction trained on public code repositories and documentation. The model understands language-specific idioms, frameworks, and best practices, producing code that follows conventions for each language rather than generic templates. Completion works both for inline suggestions and full function/class generation based on context and docstrings.

Generates step-by-step reasoning chains that decompose complex problems into intermediate steps, using a learned pattern of explicit reasoning before final answers. The model produces internal monologue-style outputs that show mathematical derivations, logical deductions, or multi-step problem solving, improving accuracy on reasoning-heavy tasks by forcing the model to articulate intermediate conclusions rather than jumping to answers.

Generates responses while explicitly acknowledging knowledge limitations based on a December 2023 training cutoff, signaling uncertainty when asked about recent events, newly released products, or evolving information. The model learned to distinguish between stable knowledge (mathematics, historical facts) and time-sensitive information, producing appropriate caveats rather than hallucinating recent information.

Generates coherent text and performs translation across 100+ languages using a unified multilingual transformer trained on parallel corpora and monolingual text in diverse languages. The model understands language-specific grammar, idioms, and cultural context, producing natural translations rather than word-for-word substitutions. A single model handles all language pairs without requiring separate translation models.

Interprets and follows complex, multi-step instructions with high fidelity, including nested conditionals, format specifications, and role-based prompting. The model learned instruction-following patterns from RLHF (reinforcement learning from human feedback) training, enabling it to parse detailed system prompts and user instructions and adapt its behavior accordingly without requiring explicit programming.