Qwen 2 72B
VS
ChatGPT 4o

Additional Features

The Qwen 2 model was pressure-tested against risk-high activity (such as explicit materials and fraud) across multiple languages. It demonstrated safety performance on par with the renowned GPT-4o.

Moreover, Qwen 2's capabilities were also tested for long-form context comprehension using the Needle in a Haystack test. The model was able to accurately extract details from text spanning up to 128K tokens without missing any critical information.

ChatGPT 4o is around 1.5x times faster than Qwen 2 and also has vision and speech recognition capabilities - an important feat. If you want a recap of GPT 4o announcement - check out this article here.

Conclusion

Qwen 2 AI is beaten by GPT 4o in benchmarks, and although most tests were close - coding showed some important differences. Most likely Qwen 72B Instruct will find its place in the research community with its good comprehension of the whole 128K tokens.

Pricing

The Pricing model is given in AI/ML API tokens. As Qwen 2 is open-source, setting it up and maintaining locally would have different pricing.

Compare for yourself

Now that we've highlighted some key strengths, and run the tests - it's time for you to test out the models for your use case. Below, you'll find a code snippet that pits Qwen 2 against ChatGPT 4o using our API Key. Throw anything at them, and see how robust they are in your field!

import openai

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

    # Specify the two models you want to compare
    model1 = 'gpt-4o'
    model2 = 'Qwen/Qwen2-72B-Instruct'
    selected_models = [model1, model2]

    system_prompt = 'You are an AI assistant that only responds with jokes.'
    user_prompt = 'Why is the sky blue?'
    results = {}

    for model in selected_models:
        try:
            response = client.chat.completions.create(
                model=model,
                messages=[
                    {'role': 'system', 'content': system_prompt},
                    {'role': 'user', 'content': user_prompt}
                ],
            )

            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:')
    print(f"{model1}: {results.get(model1, 'No response')}")
    print(f"{model2}: {results.get(model2, 'No response')}")

if __name__ == "__main__":
    main()

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Conclusion

Qwen 2 couldn't keep up with ChatGPT 4o, and unlike LLama 3, its speed couldn't compensate for the drawbacks. It's a good open-source alternative, still. The cheaper output price certainly makes the model worth consideration.

No matter which model you go with, prepare to be blown away by its capabilities. If you're feeling good after our comparison, go for ChatGPT 4o. If the pricing model sealed the deal for you, Qwen 2 is your best bet.

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