These Models are a LOT better

Key Concepts:

• Thinking Model (LLM): An LLM that internally breaks down a problem into smaller tasks and performs them sequentially before providing a final answer.
• Non-Thinking Model (LLM): An LLM that directly provides an answer without internal problem decomposition.
• Query: A request or question submitted to an LLM.
• Internal Monologue: The process of an LLM thinking through a problem, breaking it down, and planning its response.

Thinking vs. Non-Thinking Models in LLMs

The core distinction lies in how Large Language Models (LLMs) process queries. A "thinking model" engages in an internal process of problem decomposition, akin to an "internal monologue," before generating a final answer. In contrast, a "non-thinking model" directly attempts to answer the query without this internal breakdown.

DeepSpeed as an Example

DeepSpeed is presented as an example of a thinking model. When a query is submitted, DeepSpeed spends a significant amount of time (e.g., 26 seconds) internally analyzing the problem. It breaks the problem down into smaller, manageable tasks and then executes these tasks. This process results in a higher quality answer compared to non-thinking models.

Evolution of Thinking Models

The development of thinking models is a relatively recent advancement, emerging after the "01 model" from OpenAI (approximately 3-4 months prior to the discussion). Now, most companies have both thinking and non-thinking models in their LLM offerings.

Trade-offs and Use Cases

While thinking models generally produce better answers, they are not universally superior. There are scenarios where the "thinking" process can negatively impact the LLM's performance, leading to worse results. Additionally, thinking models are typically more expensive to operate. This is because the user is charged for both the intermediate "thinking" steps and the final answer.

Cost Implications

The cost difference between thinking and non-thinking models is significant. With a thinking model, the user essentially pays for two answers: the output generated during the internal problem-solving process and the final, refined answer.

Conclusion

Thinking models represent a significant advancement in LLM technology, enabling more complex problem-solving and higher-quality answers. However, they are not a one-size-fits-all solution. The choice between a thinking and non-thinking model depends on the specific use case, the complexity of the query, and the cost considerations. While thinking models often provide superior results, they can sometimes lead to worse outcomes and are generally more expensive to operate.