# Optimizing for Cost

ContextGem offers several strategies to optimize for cost efficiency while maintaining extraction quality:

- **💸 Select Cost-Efficient Models**: Use smaller/distilled LLMs for extracting aspects and basic concepts (e.g. titles, payment amounts, dates).
- **⚖️ Consider a Decision Model for Closed Questions**: Boolean, label and rating concepts, as well as aspects, can be extracted with a decision model, which generates no text. (See [Decision Models](https://contextgem.shcherbak.ai/decision_models/decision_models/index.html.md) for when a decision model is a good fit, and for what its requests cost.)
- **⚙️ Use Default Parameters**: All the extractions will be processed in as few model calls as possible.
- **📉 Enable Justifications Only When Necessary**: Do not use justifications for simple aspects or concepts. This will reduce the number of tokens generated.
- **📊 Monitor Usage and Cost**: Track model calls, token consumption, and cost to identify optimization opportunities.

```python
# Example of optimizing extraction for cost

import os

from contextgem import DocumentLLM


llm = DocumentLLM(
    model="openai/gpt-6-luna",
    api_key=os.environ.get("CONTEXTGEM_OPENAI_API_KEY"),
    auto_pricing=True,  # calculate the costs with `genai-prices`, to track them
    # or set `pricing_details=ModelPricing(...)` with the prices of your provider
)

# ... use the LLM for extraction ...

# ... monitor usage and cost ...
usage = llm.get_usage()  # get the usage details, including tokens and calls' details.
cost = llm.get_cost()  # get the cost details, including input, output, and total costs.
print(usage)
print(cost)
```