GPT-5

What is GPT-5?

GPT-5 is a general-purpose reasoning model from OpenAI. It is designed for tasks where the model must work through multiple steps rather than simply produce a short, fluent response. Typical uses include software development, debugging, research, long-document analysis, visual document understanding, structured business processes, and agents that call tools or external services.

OpenAI introduced GPT-5 on August 7, 2025. The model offers configurable reasoning effort, meaning an application can choose how much internal problem-solving work to request. The available settings are minimal, low, medium, and high. In practical terms, lower effort can reduce latency and cost for straightforward tasks, while higher effort is intended for more difficult analysis, coding, and multi-step problems.

GPT-5 should be distinguished from the broader OpenAI product ecosystem. This page concerns the API model identified by gpt-5, not every capability available in ChatGPT. The model can accept images for analysis, for example, but it does not itself generate images, audio, or video.

Current status and position in OpenAI's lineup

The canonical gpt-5 alias remains documented and accessible, but OpenAI now describes GPT-5 as a previous-generation model and recommends newer models for new deployments. This makes GPT-5 a potentially useful established model for existing applications or teams that have evaluated its behavior, but it is not the provider's newest flagship choice for every new project.

The dated snapshot gpt-5-2025-08-07 is deprecated and scheduled for API removal on December 11, 2026. Developers that depend on reproducible behavior should track whether they use the stable alias or the dated snapshot. An alias may receive lifecycle changes over time, whereas a dated model identifier is intended to preserve a specific version until its retirement date.

OpenAI's newer model family includes entries such as GPT-5.5 and GPT-5.4. Those models should be evaluated separately rather than assuming that GPT-5's specifications apply to them.

Inputs, outputs, and modalities

GPT-5 accepts text and images and produces text. Image input allows the model to reason about visual information such as screenshots, diagrams, scanned pages, or other supported visual content. The supplied model specification does not list audio or video as direct input modalities.

• Text input: Supported.
• Image input: Supported for visual understanding and reasoning.
• Text output: Supported.
• Image, audio, and video output: Not supported as native GPT-5 model outputs.
• Audio and video input: Not listed as supported direct model inputs.

This distinction matters when planning a multimodal application. GPT-5 can analyze an image and explain what it contains, but an application requiring native image generation, speech synthesis, or video generation needs a different service or model workflow.

Context window and maximum output

GPT-5 has a 400,000-token context window and a maximum output length of 128,000 tokens. A token is a unit of text used by the model; it may represent a whole word, part of a word, punctuation, or another text fragment. The context window includes the material the model receives and the relevant generated response, so the full usable capacity depends on the size of the prompt, conversation history, tool content, and requested output.

This capacity is useful for long technical documents, large collections of source code, extended research workflows, and agents that need to retain substantial tool state. A large context window does not guarantee that every detail will receive equal attention, so applications should still organize inputs clearly and avoid sending irrelevant material.

Reasoning and coding capabilities

GPT-5 is intended for complex reasoning rather than only rapid conversational completion. Its configurable reasoning effort gives developers a control for balancing answer quality, response time, and token usage. Minimal or low effort may be appropriate for simple extraction, rewriting, or routine classification. Medium or high effort is more suitable when the model must compare evidence, plan a multi-step solution, diagnose a bug, or reason through a complicated technical request.

Coding is one of GPT-5's primary use cases. It can help write code, explain unfamiliar code, debug failures, reason about changes across a repository, and support agentic software workflows. The model's large context window is especially relevant when a task involves multiple files, long specifications, test output, or an extended sequence of tool calls. These capabilities do not remove the need for tests, code review, security checks, or human validation.

The research describes GPT-5's reasoning and coding ratings as editorial comparative estimates, not provider-published benchmark scores. They should therefore be treated as guidance about positioning rather than as official measurements.

Tools and developer features

GPT-5 is available through OpenAI's Responses API and Chat Completions API. It supports function and tool calling, which lets an application expose operations such as database queries, calculations, retrieval, or business actions. The model can decide when a declared tool is relevant and return structured arguments for the application to validate and execute.

It also supports streaming, allowing an application to display generated text incrementally instead of waiting for the complete response. Structured outputs can constrain responses to an application-defined schema, which is useful for extraction and workflow automation. Structured outputs should not automatically be treated as confirmation of a separate legacy JSON mode; the supplied model documentation confirms structured outputs but does not independently confirm JSON mode for this model.

• Function and tool calling: Supported.
• Streaming: Supported.
• Structured outputs: Supported.
• Prompt caching: Supported, with cached input priced separately.
• Batch processing: Supported through the Batch API.
• Web search: Available when used through supported OpenAI APIs and tools.
• Fine-tuning: Unsupported for this exact model.

Tool use does not mean that GPT-5 independently carries out every external action. The surrounding application remains responsible for providing tools, checking arguments, enforcing permissions, handling errors, and deciding whether an action is safe to execute.

GPT-5 API pricing

The stated standard API price is $1.25 per 1 million input tokens and $10.00 per 1 million output tokens. Cached input is priced at $0.125 per 1 million tokens. Batch processing may use separate pricing, so a production estimate should account for the selected endpoint, whether prompts are cached, the amount of generated reasoning and output, and whether the Batch API is used.

Input and output prices are not equivalent in practice. Applications that generate long answers, request substantial reasoning, or repeatedly process large results can incur considerably more output cost than a short-response workload. Prompt caching can reduce the cost of repeated input content when the application's traffic pattern qualifies for caching, but it does not make generated output free.

Main strengths and limitations

Where GPT-5 is strong

• Complex problem-solving: Configurable reasoning effort supports tasks that need more deliberate analysis.
• Software engineering: The model is suited to coding, debugging, repository-scale work, and technical explanations.
• Long-context work: A 400,000-token context window can accommodate large documents, code collections, and extended tool state.
• Visual understanding: Image input adds analysis of screenshots, diagrams, and other visual material to text-based reasoning.
• Workflow integration: Function calling, structured outputs, streaming, caching, and batch processing support production applications.

Important limitations

• GPT-5 is now described by OpenAI as a previous-generation model, so newer options may be preferable for new deployments.
• It produces text only and does not natively generate images, audio, or video.
• Fine-tuning is not supported for the exact GPT-5 model.
• Higher reasoning effort can increase latency and token usage.
• Its output price is higher than the stated positioning of smaller GPT-5 Mini and GPT-5 nano alternatives, making those types of models potentially more economical for simple, high-volume work.
• The dated gpt-5-2025-08-07 snapshot has a scheduled shutdown date of December 11, 2026.

As with other generative models, GPT-5 can produce incorrect or overconfident answers. Important code, research conclusions, calculations, and business actions should be checked rather than accepted solely because the response is detailed.

Best use cases for GPT-5

GPT-5 is a good fit when the task benefits from a combination of reasoning, long context, visual understanding, and tool access. Examples include:

• Debugging a complex application while reviewing several related source files and test logs.
• Analyzing a long technical or business document and returning structured findings.
• Building an agent that plans work, calls approved tools, and maintains state across several steps.
• Interpreting screenshots, diagrams, or visual documents alongside written instructions.
• Supporting research workflows where the model must compare information and use web search through a supported tool.
• Generating code or technical documentation where quality is more important than the lowest possible per-request cost.

For routine classification, short extraction, simple transformations, or very high-volume responses, a smaller and cheaper model may be more appropriate. For native media generation, GPT-5 is also the wrong choice because its direct output is text.

When to choose GPT-5

Choose GPT-5 when you need a mature general-purpose reasoning model and your workload justifies its cost and latency. It is particularly suitable when one request combines difficult reasoning with large inputs, images, code, or several tool calls. Its reasoning-effort settings also make it more adaptable than a model that offers only one fixed quality-speed profile.

Consider another option when the dominant requirement is minimal latency, very low cost, fine-tuning, or native image, audio, or video generation. Smaller sibling models may be a better fit for predictable, simple, high-throughput tasks. Newer OpenAI models should be evaluated for new projects because OpenAI now positions GPT-5 as a previous-generation model. Existing applications should test any replacement against representative prompts before migration, especially if they depend on exact formatting, tool arguments, or reasoning behavior.

Availability and migration considerations

Developers should record the exact model identifier used by each application. The stable gpt-5 alias and the dated gpt-5-2025-08-07 snapshot are not interchangeable from a lifecycle and reproducibility perspective. The snapshot is deprecated and scheduled for removal on December 11, 2026.

Applications that need consistent behavior should monitor OpenAI's lifecycle documentation, maintain regression tests, and validate structured outputs and tool calls before changing model identifiers. New applications should compare GPT-5 with OpenAI's recommended successor models, while existing applications can continue using GPT-5 where its context capacity, coding behavior, multimodal understanding, and cost profile remain a good match.