Receives text manifest and image files
AI Models & Assistants / Knowledge library
ChatGPT-4o
GPT-4o model used in ChatGPT
Why learn ChatGPT-4o?
Offers the documented OpenAI model capabilities through hosted.
The problem it solves
Provides model inference for supported text / image inputs.
Where it came from
ChatGPT-4o: GPT-4o model used in ChatGPT Published aliases: chatgpt-4o-latest. 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 ChatGPT-4o does.
- Identify a use case for ChatGPT-4o.
- Describe a limitation of ChatGPT-4o.
- Name a compatible tool for ChatGPT-4o.
- Compare ChatGPT-4o 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
Multimodal Logistics Documentation Analysis
Global logistics firms managing complex, multi-lingual shipping manifests and damaged cargo reports.
The business problem
Operational delays occur when human agents must manually reconcile text-based shipping manifests with visual evidence of cargo damage provided via uploaded photographs.
How this topic solves it
By utilizing the chatgpt-4o-latest model via the Chat Completions API, the firm can process both text manifests and image inputs simultaneously to generate automated damage assessment reports, reducing the manual reconciliation burden.
- Configure the application to interface with the v1/chat/completions endpoint.
- Format incoming shipping manifests as text payloads and attach cargo photographs as image inputs.
- Construct a system prompt that instructs the model to cross-reference visual damage indicators against manifest descriptions.
- Stream the model output to the internal logistics dashboard for immediate agent review.
- Verify the output against the 16,384 max output token constraint to ensure full report generation.
This diagram illustrates the flow of data from cargo intake to the final assessment report.
Combines text and image inputs
Processes multimodal data
Final output for insurance claims
- Cargo Intake → Data Aggregation → API Payload
- API Payload → v1/chat/completions → chatgpt-4o-latest
- chatgpt-4o-latest → Generates Analysis → Assessment Report
Implementation example
This code snippet demonstrates how to send both text and image data to the chatgpt-4o-latest model to perform the required damage assessment.
client.chat.completions.create(model='chatgpt-4o-latest', messages=[{'role': 'user', 'content': [{'type': 'text', 'text': 'Analyze damage'}, {'type': 'image_url', 'image_url': {'url': '...'}}]}]) # Uses multimodal input supportExpected business outcome
Improved consistency in damage reporting and faster throughput for cargo insurance claims processing.
Limitations and checks
The model has a fixed knowledge cutoff of Oct 01, 2023, meaning it may struggle with highly specific, newly introduced international shipping regulations or proprietary logistics software updates released after that date; this can be checked by monitoring the model's responses to queries regarding recent regulatory changes.