GPT-4

What is GPT-4?

GPT-4 is a large multimodal language model developed by OpenAI and released on March 14, 2023. Its main output is text: it can produce explanations, summaries, classifications, rewritten content, natural-language answers, and computer code. Compared with GPT-3.5, GPT-4 was designed to provide more reliable instruction following, stronger reasoning, improved steerability, and better safety behavior.

The term “multimodal” needs some qualification in GPT-4’s case. OpenAI described the model as capable of accepting both text and image inputs, but image understanding was initially offered as a limited research capability rather than a uniformly available feature of every GPT-4 integration. The canonical gpt-4 API model produces text, not images, audio, or video.

Where GPT-4 fits in OpenAI’s lineup

GPT-4 was released through ChatGPT Plus and the OpenAI API and became one of OpenAI’s principal high-capability models. OpenAI later introduced GPT-4 Turbo, GPT-4o, and GPT-4.1 as newer members of the broader GPT-4 lineage. These names describe related generations or variants, not interchangeable specifications.

OpenAI’s current model catalog classifies the original GPT-4 as an older high-intelligence model. In practical terms, it is now a legacy option: useful for maintaining existing systems, reproducing historical evaluations, or preserving behavior that was tuned around GPT-4, but usually not the first choice for a new application.

The gpt-4 identifier was also used as a moving alias that could point to a recommended stable version. Dated identifiers such as gpt-4-0314 represented pinned snapshots. This distinction matters when an application requires reproducibility, because an alias and a dated model version do not provide the same stability over time.

What GPT-4 does well

GPT-4 is intended for broad language and reasoning work rather than a narrow single-purpose task. It can follow complex written instructions, transform supplied material, explain difficult concepts, and generate or inspect code. OpenAI reported strong performance on a range of human-oriented examinations and multilingual evaluations, while also noting that the model could still hallucinate facts and make reasoning mistakes.

• Writing and language: drafting, rewriting, summarization, classification, explanation, and multilingual communication.
• Reasoning: working through multi-step questions and interpreting detailed instructions, although its answers still require verification.
• Coding: generating code, explaining existing code, suggesting debugging approaches, and assisting with software-development tasks.
• Document and visual analysis: interpreting supplied documents, diagrams, or images where vision access is enabled by the specific configuration.
• Steerability: adapting its response style and task behavior to detailed prompts and application instructions.
• Safety alignment: stronger refusal and safety-oriented behavior than GPT-3.5, without eliminating bias, unsafe suggestions, or incorrect responses.

The supplied editorial assessment rates GPT-4’s reasoning at 8 out of 10, coding at 7 out of 10, speed at 5 out of 10, and cost at 2 out of 10. These are comparative editorial scores, not scores published by OpenAI, and they summarize trade-offs rather than formal benchmark results.

GPT-4 technical specifications

The canonical GPT-4 model has an 8,192-token context window. A context window is the amount of input and generated conversation material the model can consider within one request. This limit affects long documents, extended conversations, and applications that need to include substantial background information.

OpenAI also offered a separate GPT-4 32K variant. It should not be treated as the same exact model record as the standard gpt-4 entry. The supplied research does not specify a maximum output-token limit for the canonical model, so no separate maximum is stated here.

Knowledge cutoff and important limitations

GPT-4’s documented knowledge largely cuts off around September 2021. It should not be expected to know later events, products, policy changes, or facts unless that information is supplied in the prompt or provided through an external retrieval system. This limitation is especially important for research, current-events questions, technical documentation, and business information that changes frequently.

The 8K context window is another practical constraint. Long reports, large codebases, or extended conversations may need to be shortened, divided into multiple requests, or handled with an external retrieval and summarization workflow. The model can also hallucinate plausible but incorrect information, make reasoning errors, reflect biases, or produce insecure code. Generated code should therefore be reviewed and tested rather than deployed without validation.

GPT-4 does not natively generate images, audio, or video. Its image-related capability concerns supported image input and visual understanding, not image creation. It is therefore not a suitable standalone choice for applications centered on media generation or native voice interaction.

GPT-4 pricing and cost trade-offs

The original documented API price for GPT-4 was $30 per 1 million prompt tokens and $60 per 1 million completion tokens. In the smaller units used in the launch-era pricing documentation, that equals $0.03 per 1,000 prompt tokens and $0.06 per 1,000 completion tokens.

These figures are historical pricing information associated with the original GPT-4 API model, not a claim that every current GPT-4-related variant has identical pricing. GPT-4 was comparatively expensive and slower than many newer alternatives, which reduces its appeal for high-volume workloads where response speed or operating cost is more important than preserving GPT-4-specific behavior.

GPT-4 provides tool-use and streaming support in the supplied model data, and it was used in API applications that needed interactive text generation. The research does not verify every current function-calling or structured-output detail for this legacy model, so those capabilities should be checked against the exact API endpoint and model identifier before implementation.

Best uses for GPT-4

GPT-4 remains most appropriate when an organization already depends on it. Existing applications may have prompts, evaluations, user expectations, or output-processing rules tuned to GPT-4’s behavior. Replacing it with a newer model may require testing even if the successor is technically more capable.

• Maintaining an established GPT-4 integration.
• General-purpose text generation and rewriting.
• Document analysis within the 8,192-token context limit.
• Complex explanations and structured instruction-following tasks.
• Programming assistance, code explanation, and debugging support.
• Workflows that need historical compatibility with GPT-4 outputs or evaluations.

When should you choose GPT-4?

Choose GPT-4 when compatibility, established behavior, or migration risk matters more than access to the newest model capabilities. It can still be a reasonable maintenance choice for a production system that has been evaluated on GPT-4 and performs acceptably at its historical price and context limits.

For a new application, compare GPT-4 with a currently recommended model before committing to it. A newer option may be more appropriate when the workload requires a larger context window, more current knowledge, lower cost, higher throughput, native audio interaction, image generation, advanced reasoning, or modern web-search integrations. OpenAI’s GPT-4 Turbo, GPT-4o, and GPT-4.1 families are relevant comparison points because they represent later developments in the same broad lineage, but their specifications should be evaluated separately rather than assumed from the GPT-4 name.

GPT-4 is therefore best understood as a capable but aging general-purpose model. Its historical strengths in writing, reasoning, coding, and instruction following remain useful, while its fixed-era knowledge, 8K context, text-only output, price, and legacy status make newer models a stronger starting point for many current projects.