Input text and images
AI Models & Assistants / Knowledge library
o3
Reasoning model for complex tasks, succeeded by GPT-5
Why learn o3?
Offers the documented OpenAI model capabilities through hosted.
The problem it solves
Provides model inference for supported text / image inputs.
Where it came from
o3: Reasoning model for complex tasks, succeeded by GPT-5 Published aliases: o3-2025-04-16. 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 o3 does.
- Identify a use case for o3.
- Describe a limitation of o3.
- Name a compatible tool for o3.
- Compare o3 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 Synthesis
Legal and compliance departments in multinational financial institutions.
The business problem
Manual review of thousands of pages of cross-jurisdictional regulatory updates leads to inconsistent interpretation and high operational costs.
How this topic solves it
The o3 model utilizes its reasoning capabilities to ingest multi-modal regulatory documents, including text and diagrams, to synthesize unified compliance requirements across disparate legal frameworks.
- Ingest raw regulatory PDF documents and associated visual flowcharts into the o3 context window.
- Define a structured output schema to force the model to categorize requirements by jurisdiction and risk level.
- Execute batch API calls to process large volumes of historical and current regulatory text.
- Validate the synthesized output against internal policy templates using function calling to trigger secondary verification workflows.
The flow shows how raw data is processed into a structured compliance matrix.
Reasoning engine
Structured output
Function call trigger
- Regulatory Docs → ingests → o3 Model
- o3 Model → generates → Compliance Matrix
- Compliance Matrix → validates → Verification
Expected business outcome
A cohesive, machine-readable compliance matrix that reduces the time required for human legal experts to identify conflicting regulatory obligations.
Limitations and checks
The reasoning process introduces latency due to the model's internal chain-of-thought generation; monitor response times to ensure they remain within the required SLA for daily reporting.