--- title: README emoji: 📈 colorFrom: yellow colorTo: green sdk: static pinned: false ---

Welcome to CARROT-LLM-Routing! For a given desired trade off between performance and cost, CARROT makes it easy to pick the best model among a set of 13 LLMs for any query. Below you may read the CARROT paper, replicate the training process of CARROT, or see how to utilize CARROT out of the box for routing.

Read the paper
Train CARROT

As is, CARROT supports routing to the following collection of large language models. | | claude-3-5-sonnet-v1 | titan-text-premier-v1 | openai-gpt-4o | openai-gpt-4o-mini | granite-3-2b-instruct | granite-3-8b-instruct | llama-3-1-70b-instruct | llama-3-1-8b-instruct | llama-3-2-1b-instruct | llama-3-2-3b-instruct | llama-3-3-70b-instruct | mixtral-8x7b-instruct | llama-3-405b-instruct | |----------------------|---------------------|----------------------|---------------|--------------------|----------------------|----------------------|----------------------|----------------------|----------------------|----------------------|----------------------|----------------------|----------------------| | **Input Token Cost ($ per 1M tokens)** | 3 | 0.5 | 2.5 | 0.15 | 0.1 | 0.2 | 0.9 | 0.2 | 0.06 | 0.06 | 0.9 | 0.6 | 3.5 | | **Output Token Cost ($ per 1M tokens)** | 15 | 1.5 | 10 | 0.6 | 0.1 | 0.2 | 0.9 | 0.2 | 0.06 | 0.06 | 0.9 | 0.6 | 3.5 |

```python your_code = do_some_stuff ```