GPT-5 nano
Fastest, most cost-efficient version of GPT-5
OpenAI Fast, cost-efficient text generation from OpenAI
GPT-5 Nano is a text generation model developed by OpenAI and released as part of the GPT-5 model family. It is designed to be the fastest and most cost-efficient variant in that family, making it accessible for high-volume or latency-sensitive applications. The model supports a 400,000-token context window and has a training data cutoff of May 2024. It accepts structured inputs including tool calls and MCP server configurations.
GPT-5 Nano is particularly well-suited for summarization and classification tasks, where speed and throughput matter more than extended reasoning depth. Its large context window allows it to process long documents in a single pass, which is useful for document triage, content labeling, and similar workflows. Developers can integrate it with external tools and MCP servers, extending its utility beyond pure text generation into agentic and multi-step task scenarios.
What GPT-5 nano supports
Large Context Window
Processes up to 400,000 tokens in a single request, enabling full-document ingestion for summarization or classification without chunking.
Tool Use
Supports function calling and tool integrations, allowing the model to invoke external APIs or structured actions during a conversation.
MCP Server Support
Accepts MCP server configurations as inputs, enabling connection to Model Context Protocol-compatible tool servers for agentic workflows.
Text Summarization
Condenses long-form content into concise outputs; the 400K context window allows entire documents to be summarized in one pass.
Text Classification
Assigns categories or labels to input text, suited for content moderation, routing, and tagging pipelines at scale.
Low-Latency Inference
Optimized for speed within the GPT-5 family, making it suitable for real-time or high-throughput production use cases.
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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 | 78.0% |
| GPQA Diamond | PhD-level science questions (biology, physics, chemistry) | 67.6% |
| LiveCodeBench | Real-world coding tasks from recent competitions | 78.9% |
| HLE | Questions that challenge frontier models across many domains | 8.2% |
| SciCode | Scientific research coding and numerical methods | 36.6% |
Common questions about GPT-5 nano
What is the context window size for GPT-5 Nano?
GPT-5 Nano supports a context window of 400,000 tokens, allowing large volumes of text to be processed in a single request.
What is the training data cutoff for GPT-5 Nano?
The model's training data has a cutoff of May 2024, meaning it does not have knowledge of events after that date.
What tasks is GPT-5 Nano best suited for?
According to OpenAI's overview, GPT-5 Nano is designed for summarization and classification tasks, and is optimized for speed and cost efficiency.
Does GPT-5 Nano support tool calling and MCP servers?
Yes. The model supports tool use and MCP server inputs, enabling integration with external APIs and Model Context Protocol-compatible tool servers.
How does GPT-5 Nano relate to the rest of the GPT-5 family?
GPT-5 Nano is positioned as the fastest and most cost-efficient model in the GPT-5 family, intended for use cases where throughput and cost matter more than maximum reasoning depth.
What people think about GPT-5 nano
Community discussion around GPT-5 Nano has largely centered on leaked model details that surfaced on GitHub in August 2025, generating significant interest across Reddit communities including r/ChatGPT and r/singularity. Users engaged with the leaked descriptions to speculate about the model's positioning and capabilities within the GPT-5 family.
A separate thread in r/LocalLLaMA discussed fine-tuned smaller models outperforming frontier models on narrow tasks, reflecting ongoing community interest in cost-efficient model options for specialized workloads. No significant concerns specific to GPT-5 Nano were surfaced in the reviewed threads.
🚨 BREAKING: intern accidentally leaked GPT-5's model description on github.
Fine-tuned Qwen3 SLMs (0.6-8B) beat frontier LLMs on narrow tasks
GitHub leaks GPT-5 Details
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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