Is it GPT-4? 3.5 Claude Sonnet? 1.5 Gemini? Or Llama 3 from Meta? The most important query is: What constitutes "the best"? More importantly, is it really that important?

Evaluating models against industry-standard metrics in domains like coding, math, science, reading comprehension, reasoning, and reading comprehension is a typical way to determine which model is the best.

The Limitations of Benchmarking

Although it's important and fascinating to assess these models using benchmarks, most users don't use metrics on a daily basis. Numerous sources compare language models based on different criteria, use cases, and prompts to help you choose the best one.

It's also important to take into account the unpredictable nature of model responses. The same prompt can be entered into the same model more than once, and each time the output will be unique. GPT-4 may occasionally produce the finest results, but attempting again a few minutes later may produce different outcomes.

In actuality, every LLM has advanced to a highly competent level. What difference does it actually make if one model is marginally superior to another? When all AI models are commodities, what will be the significance of these models? Does the difference between benchmark scores with a few extra decimal places matter? Will anyone give a damn?

Changing Focus in the AI Market

There could be so many models trained on public web data that the market for models would collapse. In that case, the model itself does not contain the competitive edge (MOAT) for AI.

These days, a lot of model firms release items as well as LLMs. These businesses now realize that user experience, brand loyalty, and an extensive feature set are more important than model performance in determining MOAT.

• The new features like "Projects" and "Artifacts" may entice users to move to Claude rather than GPT-4.
• Claude is well-liked by people who either like its simple UI or have lost faith in OpenAI.
• Some people make use of NotebookLM's notes feature, which improves the usefulness of Google Gemini 1.5.

Future of AI

AI models have become so sophisticated that it is nearly hard to tell one from the other. The primary distinction that will push AI into the mainstream of our lives will be its user-friendliness. It's clear that significant tech businesses recognize this:

• Rather than pushing limits, Apple's AI strategy prioritizes personalization, trust, and usability. They prioritize product orchestration and seek to establish connections with LLMs as needed.
• Data-store integration is the main focus of Amazon's model-as-a-service strategy rather than the models themselves. In addition to knowledge base solutions, AWS Bedrock offers a range of proprietary and open-source models.
• Microsoft and Google are incorporating LLMs into their software applications, thereby embedding AI throughout.

In the end, the usefulness of AI models' output will determine their destiny, not just how well they benchmark.

Conclusion

The future of AI lies in the holistic experience offered to users rather than just the raw performance of the models. As AI technology matures, the focus on usability, integration, and additional features will determine the leaders in the AI space.