Unstructured text input
AI Models & Assistants / Knowledge library
o3-mini
A small model alternative to o3
Why learn o3-mini?
Offers the documented OpenAI model capabilities through hosted.
The problem it solves
Provides model inference for supported text inputs.
Where it came from
o3-mini: A small model alternative to o3 Published aliases: o3-mini-2025-01-31. Input: text. 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 o3-mini does.
- Identify a use case for o3-mini.
- Describe a limitation of o3-mini.
- Name a compatible tool for o3-mini.
- Compare o3-mini 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
Automated Regulatory Compliance Extraction for Logistics
Global logistics firms managing thousands of daily international shipping manifests.
The business problem
Manual review of complex, unstructured customs documentation leads to high operational overhead and frequent classification errors during cross-border transit.
How this topic solves it
Utilizing o3-mini to perform structured extraction of regulatory data from shipping manifests, ensuring that customs declarations are formatted correctly for downstream ERP systems.
- Define a JSON schema representing mandatory customs fields like HS codes, origin, and weight.
- Configure the Chat Completions API to use o3-mini with the structured_outputs parameter enabled.
- Submit raw manifest text to the model for reasoning-based extraction of field values.
- Validate the returned JSON against the schema before committing to the database.
- Use the Batch API for high-volume, non-time-sensitive daily manifest processing to optimize throughput.
The flow of data from raw document to structured compliance record.
Reasoning model for extraction
Validated compliance data
Final record storage
- Raw Manifest → Ingests text → o3-mini
- o3-mini → Outputs schema-compliant data → Structured JSON
- Structured JSON → Updates database → ERP System
Implementation example
This code snippet demonstrates how to invoke o3-mini with the structured_outputs feature to ensure the extracted manifest data adheres to a specific format.
client.chat.completions.create(model='o3-mini-2025-01-31', messages=[{'role': 'user', 'content': manifest_text}], response_format={'type': 'json_object'}) # Enforces structured output for customs fieldsExpected business outcome
Improved consistency in customs documentation and reduced reliance on manual data entry for routine shipments.
Limitations and checks
The model's reasoning capabilities are optimized for text; it cannot directly process scanned image files, requiring a separate OCR preprocessing step to convert documents into text before ingestion.