Instruct vs Thinking vs Base

Understanding Model Types

When working with large language models, you will encounter three distinct model types: base, instruct, and thinking. Each serves a different purpose, and choosing the right one can significantly impact your results. This post breaks down the differences and when to use each.

Base Models

A base model is the raw, pre-trained language model. It has been trained on vast amounts of text data to predict the next token, but it has not been fine-tuned to follow instructions or engage in conversation. Think of it as a sophisticated text completion engine.

How Base Models Work

Base models do not understand instructions. They simply continue text from where you leave off. If you prompt a base model with Once upon a time, it will generate a continuation in the style of fairy tales because that is the statistical pattern it has learned.

Use Cases for Base Models

• Text completion and creative writing where you want raw, unfiltered output
• Fine-tuning on domain-specific data to create specialized models
• Few-shot learning where you provide examples in the prompt
• Code generation when paired with code-specific training data
• Research and experimentation with model behavior

Instruct Models

Instruct models are base models that have been further trained using instruction tuning and RLHF (Reinforcement Learning from Human Feedback) to follow instructions and engage in conversation. They are designed to be helpful, harmless, and honest.

How Instruct Models Work

When you give an instruct model a prompt like Write a summary of this article, it understands that you want a summary and will produce one. The model has been trained to recognize instruction patterns and respond appropriately rather than just continuing text.

Use Cases for Instruct Models

• Chatbots and conversational AI applications
• Content generation: articles, emails, marketing copy
• Summarization and extraction of information from documents
• Question answering systems
• Translation and language transformation tasks
• Customer support automation

Thinking Models

Thinking models (also known as reasoning models) are a newer category that explicitly shows its reasoning process before providing an answer. Models like DeepSeek R1, OpenAI o1, and Claude with extended thinking generate a chain of thought that works through the problem step by step.

How Thinking Models Work

When given a problem, thinking models first generate an internal reasoning trace. They consider multiple approaches, check their work, and sometimes backtrack when they realize an error. This thinking process happens before the final answer, leading to more accurate results on complex tasks.

Use Cases for Thinking Models

• Complex mathematical and logical reasoning
• Multi-step problem solving that requires planning
• Code debugging and complex algorithm design
• Scientific analysis and research tasks
• Tasks where accuracy is more important than speed
• Chess, strategy games, and competitive analysis

Choosing the Right Model

The choice depends on your specific needs:

• Choose a base model when you need raw text completion, are fine-tuning for a specific domain, or want maximum control over model behavior.
• Choose an instruct model for most production applications: chatbots, content generation, summarization, and general-purpose AI tasks.
• Choose a thinking model when accuracy on complex reasoning tasks is critical and you can afford the extra latency and cost.

Trade-offs

Each model type involves trade-offs:

• Base models offer the most flexibility but require the most prompt engineering skill.
• Instruct models are the easiest to use but may refuse requests or add unnecessary caveats.
• Thinking models are the most accurate on hard problems but are slower and more expensive due to the reasoning tokens.

Conclusion

Understanding the difference between base, instruct, and thinking models is essential for building effective AI applications. Most developers will primarily use instruct models for their balance of capability and ease of use. Reserve thinking models for tasks that genuinely require deep reasoning, and consider base models only when you need fine-grained control or are building specialized systems.