GPT-5 mini
A faster, more cost-efficient version of GPT-5 for well-defined tasks
OpenAI Fast, cost-efficient text generation for defined tasks
GPT-5 mini is a text generation model developed by OpenAI, designed as a faster and more cost-efficient variant of GPT-5. It supports a 400,000-token context window and has a training data cutoff of May 2024. The model is tagged as a latest release and supports tool use and MCP (Model Context Protocol) server integrations.
GPT-5 mini is best suited for well-defined tasks where precise prompting is used and response speed or cost efficiency is a priority. It accepts structured inputs including tool calls and MCP server configurations, making it a practical choice for agentic workflows and automation pipelines. Developers working on tasks with clear, bounded requirements are the primary intended audience for this model.
What GPT-5 mini supports
Large Context Window
Processes up to 400,000 tokens in a single context, enabling long documents, extended conversations, or large codebases to be handled in one request.
Tool Use
Supports function calling and tool integrations, allowing the model to invoke external tools or APIs as part of a response.
MCP Server Support
Accepts MCP (Model Context Protocol) server configurations as inputs, enabling standardized integration with external context and data sources.
Text Generation
Generates natural language text across a wide range of formats including summaries, instructions, and structured responses.
Fast Inference
Optimized for lower latency compared to full GPT-5, making it suitable for applications where response speed is a priority.
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Get Started FreeBenchmark scores
Scores represent accuracy — the percentage of questions answered correctly on each test.
| Benchmark | What it tests | Score |
|---|---|---|
| MMLU-Pro | Expert knowledge across 14 academic disciplines | 83.7% |
| GPQA Diamond | PhD-level science questions (biology, physics, chemistry) | 82.8% |
| LiveCodeBench | Real-world coding tasks from recent competitions | 83.8% |
| HLE | Questions that challenge frontier models across many domains | 19.7% |
| SciCode | Scientific research coding and numerical methods | 39.2% |
Common questions about GPT-5 mini
What is the context window for GPT-5 mini?
GPT-5 mini supports a context window of 400,000 tokens, allowing large volumes of text, documents, or conversation history to be included in a single request.
What is the knowledge cutoff date for GPT-5 mini?
GPT-5 mini has a training data cutoff of May 2024, meaning it does not have knowledge of events or information published after that date.
How does GPT-5 mini differ from GPT-5?
GPT-5 mini is described by OpenAI as a faster and more cost-efficient version of GPT-5, optimized for well-defined tasks and precise prompts rather than open-ended or highly complex reasoning.
Does GPT-5 mini support tool calling and MCP integrations?
Yes. GPT-5 mini supports tool use and MCP (Model Context Protocol) server inputs, making it compatible with agentic workflows and external integrations on platforms like MindStudio.
What types of tasks is GPT-5 mini best suited for?
According to OpenAI's overview, GPT-5 mini is best suited for well-defined tasks where precise prompts are used, such as structured data extraction, classification, summarization, and automation pipelines.
What people think about GPT-5 mini
Community discussion around GPT-5 mini's launch was driven largely by a viral Reddit post about an alleged internal leak of GPT-5's model description on GitHub, which generated over 1,000 upvotes and 142 comments on r/ChatGPT. Users expressed significant interest in the model's capabilities and the circumstances of the leak.
Commenters raised questions about the accuracy of the leaked information and what it revealed about GPT-5's design, with some skepticism about the leak's authenticity. The thread reflects broad community curiosity about the GPT-5 family rather than direct hands-on feedback about GPT-5 mini specifically.
Parameters & options
Used to give the model guidance on how many reasoning tokens it should generate before creating a response to the prompt. Low will favor speed and economical token usage, and high will favor more complete reasoning at the cost of more tokens generated and slower responses. The default value is medium, which is a balance between speed and reasoning accuracy.
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