Matching LLMs to Use Cases
Let's map common business use cases to the right LLM choices. This practical guide will help you make informed decisions.
Customer Support Chatbot
Requirements: Fast responses, handles FAQs, escalates complex issues
Recommended approach:
- First tier: Claude Haiku 4.5 or GPT-5.4 Mini
- Handles 80% of simple queries
- Fast, cost-effective
- Escalation tier: Claude Sonnet 4.6 or GPT-5.4
- Complex issues, unhappy customers
- Better reasoning and empathy
Key consideration: Implement conversation memory and handoff to humans.
Document Analysis & Summarization
Requirements: Process long documents, extract key information
Recommended approach:
- Long documents (50+ pages): Claude Sonnet 4.6 (200K context)
- Can process entire documents at once
- No chunking needed
- Standard documents: Any capable model
- GPT-5.4 (large context)
- Gemini 3.1 Pro (1M context)
Key consideration: Test with your actual document types.
Code Generation & Review
Requirements: Write, explain, debug code
Recommended approach:
- Production code: GPT-5.4 or Claude Sonnet 4.6/Opus 4.6
- Better code quality and explanations
- Understands complex architectures
- Quick prototypes: GPT-5.4 Mini or Claude Haiku 4.5
- Fast iteration
- Good for boilerplate code
Key consideration: Always review AI-generated code before production use.
Content Creation
Requirements: Marketing copy, blog posts, social media
Recommended approach:
- Brand-critical content: Claude Opus 4.6 or GPT-5.4 Pro
- Better nuance and creativity
- Follows brand guidelines well
- High-volume content: GPT-5.4 Mini or Claude Haiku 4.5
- Drafts that humans will edit
- Social media variations
Key consideration: Always have human review for published content.
Data Extraction & Structuring
Requirements: Convert unstructured text to structured data
Recommended approach:
- Complex extraction: GPT-5.4 with structured outputs
- Reliable structured output
- Handles edge cases well
- Simple patterns: Any model with clear examples
- Few-shot prompting works well
- Lower cost at scale
Key consideration: Validate outputs against expected schema.
Decision Matrix
| Use Case | Speed Priority | Quality Priority | Budget Priority |
|---|---|---|---|
| Customer Support | Haiku 4.5/GPT-5.4 Mini | Sonnet 4.6/GPT-5.4 | Haiku 4.5/GPT-5.4 Mini |
| Document Analysis | Sonnet 4.6 | Opus 4.6/GPT-5.4 | Self-hosted |
| Code Generation | Haiku 4.5 | Opus 4.6/GPT-5.4 | Self-hosted |
| Content Creation | GPT-5.4 Mini/Haiku 4.5 | Opus 4.6/GPT-5.4 Pro | GPT-5.4 Mini/Haiku 4.5 |
| Data Extraction | GPT-5.4 Mini/Haiku 4.5 | GPT-5.4 structured | Self-hosted |
Getting Started Recommendation
- Start with a balanced model (GPT-5.4 Mini or Claude Sonnet 4.6)
- Measure quality and cost for your specific use case
- Experiment down to cheaper models where quality holds
- Scale up only for tasks that clearly need it
Remember: The best model is the one that solves your problem at acceptable cost and quality—not necessarily the most powerful one.
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