Comparison: Can Mistral 7B Really Beat GPT-3.5 Turbo?

Republished By Plato

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The AI landscape is continuously evolving, with new models like Mistral AI 7B challenging established ones like GPT-3.5. This article compares these two models in terms of performance, capabilities, and cost.

Performance and Capabilities

Mistral AI 7B

Fast inference and longer sequences: Mistral AI is designed for rapid inference and handling longer sequences, capable of managing an 8,000-token context length.
Attention mechanism: Utilizes grouped-query and sliding-window attention, optimizing for lower latency and high throughput.
Model size and memory requirements: A 7B parameter model that is less memory-intensive.
Accessibility: Available under the Apache 2.0 license, making it freely accessible.

GPT-3.5

Versatility in tasks: Known for its ability to handle a wide range of tasks with deep language understanding capabilities.
Computational intensity: More resource-intensive due to a higher model size.
Shorter sequences handling: Optimized for shorter sequences compared to Mistral AI 7B.

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Cost comparison

Mistral AI 7B: Remarkably cheaper, approximately 187 times less expensive than GPT-4 and 9 times cheaper than GPT-3.5. The cost of running on an NVIDIA A100 40GB GPU is about $2.67 for processing around 15.2 million tokens in 40 minutes.
GPT-3.5: Involves higher operational costs. The cost per input token ranges from $0.0015 to $0.03, and for output token from $0.002 to $0.06, depending on the model.

Practical use

Mistral AI 7B

Ideal for high-volume, fast processing applications at a lower cost.
Can be used effectively as a pre-filtering tool to reduce costs in conjunction with more advanced models like GPT-4.

GPT-3.5

Suitable for tasks that require complex language understanding and processing capabilities.

Technical comparison

Mistral AI’s fewer parameters make it less resource-intensive, and its attention mechanisms are tailored for efficient processing of long documents. In contrast, GPT-3.5, with its standard Transformer attention mechanisms, is optimized for a broader range of complex tasks but with higher resource requirements.

Conclusion

The choice between Mistral AI 7B and GPT-3.5 depends on specific use cases. Mistral AI 7B is a cost-effective option for handling longer sequences and high-volume tasks, while GPT-3.5 excels in tasks requiring deep language understanding. Both models have unique strengths, making them valuable in different scenarios within the AI landscape.