GPT-4
By OpenAI
5 predecessor and serving as the foundation for the paid tier of ChatGPT and numerous third-party applications via the OpenAI API. 5.
Definition
GPT-4 is a large multimodal language model developed by OpenAI that accepts text and image inputs and generates text outputs, offering improved reasoning, instruction-following, and factual reliability over its GPT-3.5 predecessor and serving as the foundation for the paid tier of ChatGPT and numerous third-party applications via the OpenAI API. It succeeded GPT-3.5 as OpenAI's flagship general-purpose model, remains closed-weight and accessible only through OpenAI's hosted infrastructure, and was later joined by faster and larger variants including GPT-4 Turbo and GPT-4.5.
Overview
GPT-4 is a transformer-based language model trained on a large, diverse corpus of text and, in its multimodal variants, image data. It was positioned as a significant step up from GPT-3.5 in tasks requiring multi-step reasoning, following detailed instructions, and handling longer, more nuanced prompts, and it introduced image understanding so that a single model could describe pictures, read charts, or answer questions about diagrams alongside text. The exact scale of the training run and the composition of its underlying dataset were not disclosed, so most public understanding of its behavior comes from OpenAI's own technical report combined with independent testing by researchers and developers after release. OpenAI made GPT-4 available both through ChatGPT and through an API that developers use to build custom applications, ranging from coding assistants to customer support bots to document analysis tools. Multiple variants followed the initial release, including versions with larger context windows that let the model process much longer documents or conversations in a single request, and a faster, lower-cost "Turbo" line aimed at production workloads. Its image-understanding ability was folded into the same weights as its text processing rather than bolted on as a separate pipeline, so a single forward pass can reference both a picture and accompanying text when forming an answer, which matters for tasks like reading a scanned form or interpreting a hand-drawn diagram. Architecturally, OpenAI has not published full technical details such as parameter count or exact training data composition, which is a departure from earlier GPT releases and has drawn some criticism from researchers who prefer more transparency for reproducibility and safety auditing. What is known publicly comes largely from OpenAI's own technical report and third-party benchmarking. It sits above GPT-3.5 in the same product family and below later releases such as GPT-4 Turbo and GPT-4.5 that trade off cost, speed, and knowledge recency differently, so choosing among them in practice is less about a single best model than about matching the trade-off to the workload. GPT-4 improved on earlier models in resisting some adversarial prompts and reducing (though not eliminating) hallucinated facts, and it performs well on standardized tests, coding benchmarks, and multi-turn dialogue compared to its predecessors. It still can produce confidently incorrect answers, has a training data cutoff, and lacks inherent access to real-time information unless paired with tools or retrieval systems. Enterprises commonly use it behind an internal review step for high-stakes drafting, while developers building consumer-facing tools often rely on it for the reasoning-heavy portion of a pipeline and hand off retrieval, search, or formatting to separate, more specialized components around it. As a closed, proprietary model, GPT-4 must be accessed through OpenAI's hosted infrastructure, which means users depend on OpenAI's pricing, rate limits, and usage policies rather than being able to self-host, distinguishing it from open-weight alternatives such as Llama or Mistral. Its main practical constraints are the ones common to closed hosted models: usage depends on OpenAI's availability, pricing, and rate limits, output can still be confidently wrong on unfamiliar or ambiguous questions, and its knowledge is frozen at a cutoff date unless the surrounding application supplies fresh context.
Key Features
- Multimodal input support for both text and images
- Significant reasoning and instruction-following gains over GPT-3.5
- Extended context window variants for long documents and conversations
- Available via ChatGPT and the OpenAI API for developers
- Function calling support for structured tool integrations
- Improved but not eliminated resistance to hallucination and adversarial prompts
- Closed-weight, proprietary architecture accessed only through OpenAI infrastructure