What should I use for an app that needs chat plus extraction?

If your app needs both chat and extraction, a good default choice is:

Use a chat-first LLM with structured output / JSON mode

This gives you:

• Conversational interactions for users
• Reliable data extraction from free-form text
• One model/API for both use cases

Recommended pattern

1. Chat layer: handle the back-and-forth with the user
2. Extraction layer: when you need fields from text, call the model with:
   • a strict schema
   • JSON output
   • validation on your side

Good options

• OpenAI GPT-4.1 / GPT-4o family: strong for chat + extraction, especially with structured outputs
• Claude: also very good for long-context chat and extraction
• Gemini: solid if you’re already in Google’s ecosystem

If extraction is critical

Prefer models and workflows that support:

• structured outputs / JSON schema
• tool calling
• post-parse validation
• fallback retries for malformed output

Practical recommendation

If you want the simplest setup:

• Pick one strong general-purpose model
• Use it for both chat and extraction
• Add a schema validator and retry logic

When to split models

Use a smaller/cheaper model for extraction if:

• the task is highly repetitive
• you need low cost/high throughput
• extraction is simple and well-defined

Use a stronger model if:

• extraction is messy or ambiguous
• users paste long documents
• you need high accuracy and fewer edge-case failures

If you want, I can recommend a specific stack based on your constraints like cost, latency, document length, and whether you need real-time chat or batch extraction.