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docs: add documentation for additional cost-related keys in custom pricing (#14949)

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Co-authored-by: aider (openrouter/anthropic/claude-sonnet-4) <aider@aider.chat>
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Olivier Cornelis 2025-09-26 19:32:30 +02:00 • committed by GitHub
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docs/my-website/docs/proxy/custom_pricing.md
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@ -83,6 +83,24 @@ model_list:
cache_read_input_token_cost: 0.0000006
```
### Additional Cost Keys
There are other keys you can use to specify costs for different scenarios and modalities:
- `input_cost_per_token_above_200k_tokens` - Cost for input tokens when context exceeds 200k tokens
- `output_cost_per_token_above_200k_tokens` - Cost for output tokens when context exceeds 200k tokens
- `cache_creation_input_token_cost_above_200k_tokens` - Cache creation cost for large contexts
- `cache_read_input_token_cost_above_200k_token` - Cache read cost for large contexts
- `input_cost_per_image` - Cost per image in multimodal requests
- `output_cost_per_reasoning_token` - Cost for reasoning tokens (e.g., OpenAI o1 models)
- `input_cost_per_audio_token` - Cost for audio input tokens
- `output_cost_per_audio_token` - Cost for audio output tokens
- `input_cost_per_video_per_second` - Cost per second of video input
- `input_cost_per_video_per_second_above_128k_tokens` - Video cost for large contexts
- `input_cost_per_character` - Character-based pricing for some providers
These keys evolve based on how new models handle multimodality. The latest version can be found at [https://github.com/BerriAI/litellm/blob/main/model_prices_and_context_window.json](https://github.com/BerriAI/litellm/blob/main/model_prices_and_context_window.json).
## Set 'base_model' for Cost Tracking (e.g. Azure deployments)
**Problem**: Azure returns `gpt-4` in the response when `azure/gpt-4-1106-preview` is used. This leads to inaccurate cost tracking