Raw shipping document input
AI Models & Assistants / Knowledge library
GPT-5 nano
Fastest, most cost-efficient version of GPT-5
Why learn GPT-5 nano?
Offers the documented OpenAI model capabilities through hosted.
The problem it solves
Provides model inference for supported text / image inputs.
Where it came from
GPT-5 nano: Fastest, most cost-efficient version of GPT-5 Published aliases: gpt-5-nano-2025-08-07. Input: text, image. Output: text. Checked 2026-10-13. Model capabilities are distinct from tools available in a particular agent.
Build your understanding
Work through these exercises in order. Try the task before opening a reference.
- Explain what GPT-5 nano does.
- Identify a use case for GPT-5 nano.
- Describe a limitation of GPT-5 nano.
- Name a compatible tool for GPT-5 nano.
- Compare GPT-5 nano against an alternative.
Documentation and sources
- Primary source / model card (developers.openai.com)
Catalog research notes
- See the AI Workbench for source dates, pricing evidence and configuration details.
Connect the ideas
Built on
Real World Example
High-Volume Logistics Classification
Global logistics firm managing millions of daily shipping manifests.
The business problem
The firm faces prohibitive costs and latency when using high-parameter models to classify incoming shipping manifests into customs categories, leading to processing bottlenecks.
How this topic solves it
Implementing GPT-5 nano via the Chat Completions API allows the firm to perform rapid, cost-efficient classification of text-based manifests at scale, utilizing the model's optimized architecture for high-throughput tasks.
- Configure the application to route manifest text data to the gpt-5-nano-2025-08-07 snapshot.
- Implement structured outputs to ensure the model returns consistent JSON classification labels.
- Utilize prompt caching for recurring manifest templates to reduce input token expenditure.
- Deploy the classification logic using the v1/chat/completions endpoint for real-time processing.
This diagram illustrates the flow of manifest data from ingestion to classification using the GPT-5 nano model.
Check for recurring template matches
Model processes text classification
JSON response with category label
- Manifest Ingestion → Sends raw text → Prompt Caching
- Prompt Caching → Provides cached context → GPT-5 nano
- GPT-5 nano → Generates classification → Structured Output
Expected business outcome
The firm achieves a streamlined customs documentation workflow with significantly reduced operational overhead and improved throughput for high-volume manifest ingestion.
Limitations and checks
The model's focus on speed and cost-efficiency may result in lower reasoning depth for highly ambiguous or complex legal edge cases; verify performance by monitoring classification confidence scores against a human-audited sample set.