The LMSYS Chatbot Arena, with over 1,000,000 human comparisons, is the gold standard for evaluating LLM performance.

But, testing multiple LLMs is a pain, requiring you to juggle APIs that all work differently, with different authentication and dependencies.

Enter Portkey: A unified, open source API for accessing over 200 LLMs. Portkey makes it a breeze to call the models on the LMSYS leaderboard - no setup required.

In this notebook, you’ll see how Portkey streamlines LLM evaluation for the Top 10 LMSYS Models, giving you valuable insights into cost, performance, and accuracy metrics.

Let’s dive in!

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Video Guide

The notebook comes with a video guide that you can follow along

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Setting up Portkey

To get started, install the necessary packages:

!pip install -qU portkey-ai openai

Next, sign up for a Portkey API key at https://app.portkey.ai/. Navigate to “Settings” -> “API Keys” and create an API key with the appropriate scope.

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Defining the Top 10 LMSYS Models

Let’s define the list of Top 10 LMSYS models and their corresponding providers.

top_10_models = [

    ["gpt-4o-2024-05-13", "openai"],

    ["gemini-1.5-pro-latest", "google"],

##  ["gemini-advanced-0514","google"],             # This model is not available on a public API

    ["gpt-4-turbo-2024-04-09", "openai"],

    ["gpt-4-1106-preview","openai"],

    ["claude-3-opus-20240229", "anthropic"],

    ["gpt-4-0125-preview","openai"],

##  ["yi-large-preview","01-ai"],                  # This model is not available on a public API

    ["gemini-1.5-flash-latest", "google"],

    ["gemini-1.0-pro", "google"],

    ["meta-llama/Llama-3-70b-chat-hf", "together"],

    ["claude-3-sonnet-20240229", "anthropic"],

    ["reka-core-20240501","reka-ai"],

    ["command-r-plus", "cohere"],

    ["gpt-4-0314", "openai"],

    ["glm-4","zhipu"],

##  ["qwen-max-0428","qwen"]                       # This model is not available outside of China

]

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Add Provider API Keys to Portkey Vault

ALL the providers above are integrated with Portkey - which means, you can add their API keys to Portkey vault and get a corresponding Virtual Key and streamline API key management.

ProviderLink to get API KeyPayment Mode
openaihttps://platform.openai.com/Wallet Top Up
anthropichttps://console.anthropic.com/Wallet Top Up
googlehttps://aistudio.google.com/

Free to Use
coherehttps://dashboard.cohere.com/

Free Credits
together-aihttps://api.together.ai/

Free Credits
reka-aihttps://platform.reka.ai/Wallet Top Up
zhipuhttps://open.bigmodel.cn/

Free to Use
## Replace the virtual keys below with your own

virtual_keys = {

    "openai": "openai-new-c99d32",

    "anthropic": "anthropic-key-a0b3d7",

    "google": "google-66c0ed",

    "cohere": "cohere-ab97e4",

    "together": "together-ai-dada4c",

    "reka-ai":"reka-54f5b5",

    "zhipu":"chatglm-ba1096"

}

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Running the Models with Portkey

Now, let’s create a function to run the Top 10 LMSYS models using OpenAI SDK with Portkey Gateway:

from openai import OpenAI

from portkey_ai import PORTKEY_GATEWAY_URL, createHeaders

def run_top10_lmsys_models(prompt):

    outputs = {}

    for model, provider in top_10_models:

        portkey = OpenAI(

            api_key = "dummy_key",

            base_url = PORTKEY_GATEWAY_URL,

            default_headers = createHeaders(

                api_key="YOUR_PORTKEY_API_KEY",                 # Grab from https://app.portkey.ai/

                virtual_key = virtual_keys[provider],

                trace_id="COMPARING_LMSYS_MODELS"

            )

        )

        response = portkey.chat.completions.create(

            messages=[{"role": "user", "content": prompt}],

            model=model,

            max_tokens=256

        )

        outputs[model] = response.choices[0].message.content

    return outputs

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Comparing Model Outputs

To display the model outputs in a tabular format for easy comparison, we define the print_model_outputs function:

from tabulate import tabulate

def print_model_outputs(prompt):

    outputs = run_top10_lmsys_models(prompt)

    table_data = []

    for model, output in outputs.items():

        table_data.append([model, output.strip()])

    headers = ["Model", "Output"]

    table = tabulate(table_data, headers, tablefmt="grid")

    print(table)

    print()

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Example: Evaluating LLMs for a Specific Task

Let’s run the notebook with a specific prompt to showcase the differences in responses from various LLMs:

On Portkey, you will be able to see the logs for all models:

prompt = "If 20 shirts take 5 hours to dry, how much time will 100 shirts take to dry?"

print_model_outputs(prompt)

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Conclusion

With minimal setup and code modifications, Portkey enables you to streamline your LLM evaluation process and easily call 200+ LLMs to find the best model for your specific use case.

Explore Portkey further and integrate it into your own projects. Visit the Portkey documentation at https://docs.portkey.ai/ for more information on how to leverage Portkey’s capabilities in your workflow.

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