Raw legal discovery files
AI Models & Assistants / Knowledge library
GPT-5.6 Terra
GPT-5.6 model that balances intelligence and cost
Why learn GPT-5.6 Terra?
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-5.6 Terra: GPT-5.6 model that balances intelligence and cost Published aliases: gpt-5.6-terra. 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-5.6 Terra does.
- Identify a use case for GPT-5.6 Terra.
- Describe a limitation of GPT-5.6 Terra.
- Name a compatible tool for GPT-5.6 Terra.
- Compare GPT-5.6 Terra 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 Legal Discovery for Mid-Sized Firms
Mid-sized legal firms managing high-volume document discovery for litigation.
The business problem
Firms struggle with the high cost of processing massive document sets while needing high-level reasoning to identify relevant evidence across thousands of pages.
How this topic solves it
GPT-5.6 Terra provides a balanced intelligence-to-cost ratio, allowing firms to utilize its 1,050,000 context window to analyze entire case files in a single pass without the overhead of premium flagship models.
- Ingest raw discovery documents into a centralized repository.
- Utilize the Chat Completions API with gpt-5.6-terra to perform semantic indexing of the documents.
- Configure reasoning.effort to medium to ensure accurate legal classification while maintaining cost efficiency.
- Implement prompt caching for recurring case-law references to reduce input token costs.
- Output structured findings using structured_outputs for direct integration into case management software.
The flow of documents through the GPT-5.6 Terra processing pipeline.
Reasoning engine
Structured evidence findings
- Document Store → Context ingestion → GPT-5.6 Terra
- GPT-5.6 Terra → Structured output → Case Database
Expected business outcome
Firms achieve comprehensive document review coverage with predictable operational costs, enabling faster case preparation without exceeding budget constraints.
Limitations and checks
The model's performance on highly nuanced legal synthesis may degrade if reasoning.effort is set too low; verify accuracy by comparing outputs against a small human-reviewed sample set.