GPT-5 nano
GPT-5 nano is a text generation model from OpenAI with a 400,000 token context window and tool-calling support.
OpenAILightweight text generation with tool support
GPT-5 nano is a text generation model developed by OpenAI, released in August 2025. It supports a 400,000 token context window and can produce responses up to 128,000 tokens in length. The model accepts image inputs alongside text and includes a configurable reasoning effort setting, allowing developers to tune how much computation the model applies to a given request.
GPT-5 nano is designed for use cases that require tool integration and agent-style workflows, as it natively supports function calling and MCP (Model Context Protocol) servers. Its large context window makes it well-suited for tasks involving long documents, extended conversations, or multi-step reasoning over large inputs. The model is available directly through MindStudio without requiring separate API key configuration.
What GPT-5 nano supports
Large Context Window
Processes up to 400,000 tokens of input in a single request, enabling analysis of long documents or extended multi-turn conversations.
Long Response Output
Generates responses up to 128,000 tokens, supporting detailed outputs such as long-form documents or extended code files.
Image Input
Accepts image inputs alongside text prompts, allowing the model to reason about visual content within a conversation.
Tool Calling
Supports function and tool calling natively, enabling the model to invoke external tools as part of an agentic workflow.
MCP Server Support
Integrates with Model Context Protocol servers, allowing structured external data sources and services to be connected directly to the model.
Configurable Reasoning Effort
Exposes a reasoning effort selector that lets developers adjust how much computational effort the model applies when generating a response.
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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, meaning it can process up to that amount of combined input text, images, and conversation history in a single request.
What is the maximum response length?
The model can generate responses up to 128,000 tokens in a single output.
Does GPT-5 nano support image inputs?
Yes, GPT-5 nano accepts image inputs alongside text, allowing it to reason about visual content.
What is the reasoning effort setting?
GPT-5 nano includes a configurable reasoning effort input that allows developers to control how much computation the model applies when generating a response, which can affect both output quality and latency.
Does GPT-5 nano support tool calling and MCP?
Yes, the model supports both function/tool calling and Model Context Protocol (MCP) server connections, making it suitable for agentic and tool-augmented workflows.
When was GPT-5 nano released?
GPT-5 nano was released in August 2025 and became available on MindStudio on August 7, 2025.
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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