OpenAI: GPT-4 Turbo Preview

Processes multi-turn conversations with improved instruction adherence through transformer-based attention mechanisms trained on instruction-tuning datasets. Supports up to 128K tokens of context (approximately 96K input + 32K output), enabling analysis of entire documents, codebases, or conversation histories in a single request without context truncation or sliding-window approximations.

Constrains model output to valid JSON format through post-processing validation and beam search constraints during token generation. When enabled, the model generates only syntactically valid JSON that matches a provided schema, eliminating the need for regex parsing or output repair logic in downstream applications.

Enables the model to invoke multiple functions simultaneously in a single response through a structured function-calling protocol. The model generates a list of function calls with arguments, which are executed in parallel by the client, and results are fed back to the model for synthesis — supporting complex workflows that require coordinating multiple APIs or tools.

Provides deterministic model outputs through a seed parameter that controls the random number generator used during token sampling. When the same seed is provided with identical inputs, the model generates identical outputs, enabling reproducible results for testing, debugging, and consistent behavior in production systems.

Processes images alongside text prompts to answer questions about visual content, perform OCR, analyze diagrams, and describe scenes. The model encodes images into visual tokens using a vision transformer backbone, then fuses them with text embeddings in the transformer for joint reasoning about image and text content.

Generates syntactically correct code in 40+ programming languages based on natural language descriptions, code comments, or partial code. Uses transformer-based code understanding trained on public repositories to predict the next tokens in a code sequence, supporting both completion (filling in missing code) and generation (writing code from scratch).

Decomposes complex problems into step-by-step reasoning chains through prompting techniques that encourage the model to 'think aloud' before providing answers. The model generates intermediate reasoning steps, which improve accuracy on multi-step problems by allowing the transformer to allocate more computation to reasoning rather than direct answer prediction.

The model has training data only up to December 2023, meaning it lacks knowledge of events, product releases, API changes, and research published after that date. Requests about current events or recent developments will produce outdated or hallucinated information, as the model cannot distinguish between pre-cutoff knowledge and post-cutoff speculation.

The model is heavily rate-limited by OpenAI during the preview period, meaning requests may be throttled or rejected with 429 (Too Many Requests) errors. Rate limits vary by account tier and usage patterns, and the model may become temporarily unavailable during peak usage periods.