Unstructured legal PDF
AI Models & Assistants / Knowledge library
DeepSeek V4 Pro
DeepSeek model endpoint with thinking, tool calls and structured output. Displayed prices are peak rates; off-peak is half.
Why learn DeepSeek V4 Pro?
Offers the documented DeepSeek model capabilities through hosted.
The problem it solves
Provides model inference for supported documented inputs.
Where it came from
DeepSeek V4 Pro: DeepSeek model endpoint with thinking, tool calls and structured output. Displayed prices are peak rates; off-peak is half. Published aliases: deepseek-v4-pro. Input: consult source. Output: consult source. 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 DeepSeek V4 Pro does.
- Identify a use case for DeepSeek V4 Pro.
- Describe a limitation of DeepSeek V4 Pro.
- Name a compatible tool for DeepSeek V4 Pro.
- Compare DeepSeek V4 Pro against an alternative.
Documentation and sources
- Primary source / model card (api-docs.deepseek.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 Global Compliance
International law firms managing massive volumes of cross-jurisdictional regulatory filings.
The business problem
Manual review of thousands of unstructured legal documents leads to inconsistent interpretation of compliance requirements and high operational overhead.
How this topic solves it
DeepSeek V4 Pro utilizes its native thinking mode and tool calling capabilities to autonomously parse, categorize, and extract structured compliance data from complex legal PDFs, ensuring consistent logic application across diverse document types.
- Configure DeepSeek V4 Pro with thinking mode enabled to allow the model to reason through complex regulatory clauses before generating output.
- Define a set of tool definitions that allow the model to query internal document databases for specific jurisdictional precedents.
- Implement structured output schemas to ensure the extracted compliance data maps directly to the firm's internal database fields.
- Execute the model request, allowing the thinking process to validate findings against the provided tool-based evidence.
- Review the final structured JSON output for integration into the compliance dashboard.
This flow illustrates how the model processes documents using internal tools and structured reasoning.
Thinking mode enabled
Tool for precedent lookup
JSON compliance report
- Raw Document → Ingests text → DeepSeek V4 Pro
- DeepSeek V4 Pro → Calls tool for context → Internal DB
- Internal DB → Returns precedent → DeepSeek V4 Pro
- DeepSeek V4 Pro → Generates schema → Structured Output
Expected business outcome
A highly consistent, auditable, and automated pipeline that reduces the time required for initial regulatory document triage while maintaining high logical rigor.
Limitations and checks
The model's reasoning process increases input token consumption significantly; developers must monitor the ratio of thinking tokens to output tokens to ensure cost-efficiency, which can be checked by analyzing the token usage metadata in the API response.