Llama 3.1 405B
VS
Mixtral 8x22B v0.1

Pricing

The Pricing model is given in AI/ML API tokens. Both models have the same input and output prices as they are open-source. Llama 405B is much larger, so the price is higher.

Compare for yourself

You've seen what these models can do - now try them for your use case. Plug the code below into Google Colab or any IDE, use your API Key, and get testing!

import openai
import requests

def main():
    client = OpenAI(
      api_key='<YOUR_API_KEY>',
      base_url="https://api.aimlapi.com",
    )

    # Specify the two models you want to compare
    model1 = 'meta-llama/Meta-Llama-3.1-405B-Instruct-Turbo'
    model2 = 'mistralai/Mixtral-8x22B-Instruct-v0.1' 
    selected_models = [model1, model2]

    results= {}
    
    for model in selected_models:
        try:
            response = client.chat.completions.create(
                model=model,
                messages=[
                    {'role': 'system', 'content': "be strong"},
                    {'role': 'user', 'content': "who is strong?"}
                ],
            )

            message = response.choices[0].message.content
            results[model] = message
        except Exception as error:
            print(f"Error with model {model}:", error)

    # Compare the results
    print('Comparison of models:\n')
    print(f"{model1}:\n{results.get(model1, 'No response')}")
    print('\n')
    print(f"{model2}:\n{results.get(model2, 'No response')}")

if __name__ == "__main__":
    main()

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Conclusion

Well, what can I say, the difference is evident. Llama 3.1 405B is really impressive, but Mixtral 8x22B v0.1 is cheaper and well-performing. Given that Llama-3 has three times more parameters, such a gap was expected. It'll be interesting to see how Mistral AI responds to this challenge. In any case, we now have great tools for different tasks and budgets.

You can check our model lineup here - try any of them for yourself with our API Key.