Instruction-Tuned Models vs. Base Models

Base models predict text. Instruction-tuned models follow directions. Understanding the difference is fundamental to working with LLMs.

Key takeaways
  • Raw pretrained models that predict next tokens but don't follow instructions or have conversations.
  • Fine-tunes base models on instruction-response pairs to teach question-answering and task-completion behavior.
  • Modern instruction tuning combines supervised fine-tuning with reinforcement learning from human feedback.
  • Instruction-tuned models suit most applications while base models are better for custom fine-tuning research.
  • Production systems typically follow base model instruct model fine-tuned instruct model progression.

What Is a Base Model?

A base model (also called a pretrained or foundation modelFoundation ModelA large AI model trained on broad data at massive scale that can be adapted to a wide variety of downstream tasks, forming the 'foundation' for specialized applications.Learn more →) is the raw output of pretrainingPretrainingThe initial large-scale training phase where language models learn patterns from vast text datasets through next-token prediction.Learn more →: a model trained on massive text corpora to predict the next tokenTokenThe basic unit of text that an LLM processes — roughly corresponding to a word or word-piece. Models read input and produce output in tokens, which is also how API usage is measured and billed.Learn more →. Base models have learned rich representations of language, facts, and reasoning patterns — but they aren't designed to follow instructions or have conversations.

If you promptPromptThe input text sent to a language model — the question, instruction, or context that triggers a response.Learn more → a base model with 'What is the capital of France?', it might continue the sentence in any direction: adding more questions, generating a quiz, or producing encyclopedia-style text. It's completing a document, not answering a query. Base models like GPTGPTGenerative Pre-trained Transformer — the model architecture and family name behind OpenAI's most famous models, from GPT-2 to GPT-5.Learn more →-3 before instruction tuning, Llama base weights, and Mistral base are useful starting points for fine-tuningFine-tuningThe process of further training a pre-trained model on a smaller, task-specific dataset to specialize its behavior for a particular use case.Learn more → but not for direct deployment.

How Instruction Tuning Works

Instruction tuning fine-tunes a base model on a dataset of (instruction, response) pairs — examples of humans asking for things and ideal assistant responses. This teaches the model to switch from document-completion mode to question-answering and task-completion mode. The resulting model can follow diverse instructions it has never seen before, generalizing from the instruction-following patterns it learned.

Modern instruction tuning typically combines supervised fine-tuningSupervised Fine-TuningSupervised Fine-Tuning (SFT) is the process of training a pre-trained base model on curated instruction-response pairs to teach it specific tasks before preference alignment.Learn more → (SFT) with reinforcement learning from human feedback (RLHFRLHFReinforcement Learning from Human Feedback — a training technique that uses human preferences to teach AI models to be helpful, honest, and harmless.Learn more →) or similar preference-optimization techniques. SFT teaches basic instruction following; RLHF refines the model's outputs to be more helpful, harmless, and honest according to human preferences. This two-stage process is what produces the assistants you interact with today.

When to Use Each

Instruction-tuned models are right for most applications: chatbots, writing assistants, code tools, and anything where users give the model directions. Base models are useful when you want to fine-tune on your own data without the constraints of prior alignmentAlignmentThe process of ensuring AI models behave according to human values and intentions through training techniques, evaluation methods, and safety measures.Learn more →, or when you're building research systems that need precise control over model behavior.

One common confusion: you can further fine-tune an instruction-tuned model. Companies like OpenAI and Anthropic offer fine-tuning APIs for their instruct models, allowing you to specialize behavior without starting from scratch. The base model instruct modelInstruct ModelA base language model that has been fine-tuned to follow instructions from users, as opposed to just completing text in the style of its training data.Learn more → fine-tuned instruct model pipeline is how most production AI systems are built.

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