> For the complete documentation index, see [llms.txt](https://docs.graphlit.dev/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://docs.graphlit.dev/api-guides/use-cases/production/cost-optimization-model-selection.md).

# Cost Optimization with Model Selection

## User Intent

"How do I optimize costs by choosing the right models? Show me cost-effective configurations."

## Operation

**Concept**: Model selection strategy for cost vs quality\
**Use Case**: Balance performance and budget

***

## Model Cost Comparison

### Completion Models (per 1M tokens)

**GPT-4**: $30 input, $60 output - Highest quality\
**GPT-4o**: $5 input, $15 output - Best balance\
**GPT-3.5**: $0.50 input, $1.50 output - Budget option\
**Claude 3.5 Sonnet**: $3 input, $15 output - Good value\
**Gemini 1.5 Pro**: $1.25 input, $5 output - Most economical

### Embedding Models

**text-embedding-3-large**: $0.13/1M tokens - Highest quality\
**text-embedding-3-small**: $0.02/1M tokens - Best value\
**text-embedding-ada-002**: $0.10/1M tokens - Legacy

***

## Cost Optimization Strategies

### 1. Choose Right Model for Task

```typescript
// High-stakes: Medical, legal, compliance
specification: { model: OpenAIModels.Gpt4 }

// Production default: Most use cases
specification: { model: OpenAIModels.Gpt4o }  //  Recommended

// High volume, simple tasks
specification: { model: OpenAIModels.Gpt35Turbo }
```

### 2. Optimize Token Usage

```typescript
// Smaller chunk sizes = fewer tokens per query
preparation: {
  chunkSize: 500,  // vs 1000+
  chunkOverlap: 50
}

// Limit response length
specification: {
  maxTokens: 500  // vs unlimited
}
```

### 3. Cache Results

```typescript
// Cache queries client-side
const cache = new Map<string, any>();

async function cachedQuery(prompt: string) {
  if (cache.has(prompt)) {
    return cache.get(prompt);
  }
  
  const result = await graphlit.promptConversation({
    prompt,
    id: conversationId
  });
  
  cache.set(prompt, result);
  return result;
}
```

### 4. Use Hybrid Search

```typescript
// Hybrid is default and most cost-effective
searchType: SearchTypes.Hybrid  // Best balance
// vs pure vector (more expensive)
```

***

## Cost Estimation

**RAG Query** (typical):

* Search: \~1K tokens input
* Response: \~500 tokens output
* GPT-4o cost: \~$0.01 per query
* GPT-4 cost: \~$0.06 per query (6x more)

**Entity Extraction** (per document):

* 10-page PDF: \~5K tokens
* GPT-4o cost: \~$0.10 per document
* GPT-4 cost: \~$0.30 per document (3x more)

***

## Best Practices

1. **Default to GPT-4o** for most use cases
2. **Upgrade to GPT-4** only when quality critical
3. **Use GPT-3.5** for simple, high-volume tasks
4. **Cache aggressively** to avoid repeated calls
5. **Monitor usage** in Developer Portal
6. **Set token limits** to prevent runaway costs

***