Hi! I am making a python jupyter notebook, and I have a JSON returned from an HTTP get method using OpenRouter API to get all the data of the models they are offering right now. To evaluate size, costs, and efficiency between them I want to use pandas, matplotlib, and similar to work this is a dataframe, plot some graphs about costs and sizes, and other useful metrics to decide which model to use in production. Then after this is set I want to dynamically check for a given input and output token values, the price per model and the total costs.

I will post a snippet of the request I got from the API so we can start with this:

```json
{
   "data":[
      {
         "id":"mistralai/ministral-8b",
         "name":"Ministral 8B",
         "created":1729123200,
         "description":"Ministral 8B is an 8B parameter model featuring a unique interleaved sliding-window attention pattern for faster, memory-efficient inference. Designed for edge use cases, it supports up to 128k context length and excels in knowledge and reasoning tasks. It outperforms peers in the sub-10B category, making it perfect for low-latency, privacy-first applications.",
         "context_length":128000,
         "architecture":{
            "modality":"text->text",
            "tokenizer":"Mistral",
            "instruct_type":"None"
         },
         "pricing":{
            "prompt":"0.0000001",
            "completion":"0.0000001",
            "image":"0",
            "request":"0"
         },
         "top_provider":{
            "context_length":128000,
            "max_completion_tokens":"None",
            "is_moderated":false
         },
         "per_request_limits":{
            "prompt_tokens":"451999670",
            "completion_tokens":"451999670"
         }
      },
      {
         "id":"mistralai/ministral-3b",
         "name":"Ministral 3B",
         "created":1729123200,
         "description":"Ministral 3B is a 3B parameter model optimized for on-device and edge computing. It excels in knowledge, commonsense reasoning, and function-calling, outperforming larger models like Mistral 7B on most benchmarks. Supporting up to 128k context length, it’s ideal for orchestrating agentic workflows and specialist tasks with efficient inference.",
         "context_length":128000,
         "architecture":{
            "modality":"text->text",
            "tokenizer":"Mistral",
            "instruct_type":"None"
         },
         "pricing":{
            "prompt":"0.00000004",
            "completion":"0.00000004",
            "image":"0",
            "request":"0"
         },
         "top_provider":{
            "context_length":128000,
            "max_completion_tokens":"None",
            "is_moderated":false
         },
         "per_request_limits":{
            "prompt_tokens":"1129999175",
            "completion_tokens":"1129999175"
         }
      },
      {
         "id":"qwen/qwen-2.5-7b-instruct",
         "name":"Qwen2.5 7B Instruct",
         "created":1729036800,
         "description":"Qwen2.5 7B is the latest series of Qwen large language models. Qwen2.5 brings the following improvements upon Qwen2:\n\n- Significantly more knowledge and has greatly improved capabilities in coding and mathematics, thanks to our specialized expert models in these domains.\n\n- Significant improvements in instruction following, generating long texts (over 8K tokens), understanding structured data (e.g, tables), and generating structured outputs especially JSON. More resilient to the diversity of system prompts, enhancing role-play implementation and condition-setting for chatbots.\n\n- Long-context Support up to 128K tokens and can generate up to 8K tokens.\n\n- Multilingual support for over 29 languages, including Chinese, English, French, Spanish, Portuguese, German, Italian, Russian, Japanese, Korean, Vietnamese, Thai, Arabic, and more.\n\nUsage of this model is subject to [Tongyi Qianwen LICENSE AGREEMENT](https://huggingface.co/Qwen/Qwen1.5-110B-Chat/blob/main/LICENSE).",
         "context_length":131072,
         "architecture":{
            "modality":"text->text",
            "tokenizer":"Qwen",
            "instruct_type":"chatml"
         },
         "pricing":{
            "prompt":"0.00000027",
            "completion":"0.00000027",
            "image":"0",
            "request":"0"
         },
         "top_provider":{
            "context_length":32768,
            "max_completion_tokens":"None",
            "is_moderated":false
         },
         "per_request_limits":{
            "prompt_tokens":"167407285",
            "completion_tokens":"167407285"
         }
      },
 ...
```