Running a frontier model for every request is expensive and slow, but a smaller model trained from scratch usually can’t match its accuracy. LLM knowledge distillation splits the difference: it transfers what a large “teacher” model knows into a smaller “student” model, so you keep most of the accuracy at a fraction of the cost and latency. In 2026, with inference costs under constant scrutiny and OpenAI, Together AI, and open-source toolchains all shipping built-in distillation workflows, it’s become one of the most practical levers teams have for shipping cheaper, faster LLM features.
This guide walks through how LLM knowledge distillation actually works, the main techniques in use today, a step-by-step workflow you can follow, and the mistakes that most commonly derail a distillation project.
What Is Knowledge Distillation, and Why It Matters Now
Knowledge distillation is a training technique where a smaller student model learns to mimic a larger teacher model’s behavior, rather than being trained from raw labels alone. Instead of just learning “this is the right answer,” the student also learns the teacher’s confidence and reasoning patterns — information a fixed label alone doesn’t capture.
The payoff is significant: a well-distilled model can be roughly 10x smaller and faster than its teacher while retaining 95%+ of its performance on the target task. That matters for three practical reasons in production systems:
- Cost: smaller models cost dramatically less per token to serve, which compounds fast at scale.
- Latency: fewer parameters mean faster inference, which matters for anything real-time or user-facing.
- Deployability: distilled models can often run on cheaper hardware, or even on-device, where the teacher never could.
How LLM Knowledge Distillation Works
At a high level, distillation involves running your teacher model on a set of inputs, capturing its outputs (and often its confidence scores), and using that as training signal for the student. There are three common approaches:
Logit-based distillation
The student learns from the teacher’s full probability distribution over possible answers (the “soft labels”), not just the single correct answer. This preserves nuance — for example, how confident the teacher was between two plausible answers — that hard labels throw away.
Feature-based distillation
Rather than only matching final outputs, this approach aligns intermediate activations between the teacher’s and student’s internal layers, so the student learns to “think” more like the teacher internally, not just mimic its final answer.
Relation-based distillation
This method copies structural relationships between predictions or features, rather than matching individual values directly, which can help the student generalize better across related tasks.
Distillation methods also split by how much access you have to the teacher. White-box distillation uses the teacher’s internal parameters or full output distribution, which is only possible with open-source teacher models. Black-box distillation works only from the teacher’s final text outputs, which is the only option with closed frontier models like GPT-4o or Claude, and is the approach behind most commercial distillation offerings today.
Distilling Step-by-Step: Teaching the “Why,” Not Just the “What”
One of the most effective recent techniques is “Distilling Step-by-Step,” which extracts chain-of-thought rationales from the teacher model rather than just its final answers. By training the student on both the reasoning steps and the answer, researchers demonstrated a 770-million-parameter model outperforming a few-shot-prompted 540-billion-parameter model on the same task — a result that would have been implausible with output-only distillation. For any use case involving multi-step reasoning, this rationale-based approach tends to outperform simpler logit matching.
How to Distill an LLM: A Step-by-Step Workflow
- Pick your teacher and target task. Choose a frontier model that performs well on the specific task you’re distilling (customer support triage, summarization, classification) rather than trying to distill general-purpose capability.
- Generate and store teacher completions. Run the teacher on representative inputs and store its outputs. A few hundred high-quality examples can work for narrow tasks; more complex or open-ended tasks often need thousands.
- Filter for quality. Not every teacher completion is worth training on — filter out low-confidence, inconsistent, or incorrect outputs before they become training data.
- Fine-tune the student model. Use the filtered dataset to fine-tune a smaller base model, optionally combining hard labels with the teacher’s soft labels or rationales. Tools like those covered in our LLM fine-tuning framework guide can handle this step.
- Evaluate against both baselines. Compare the distilled student’s accuracy and latency against both the original teacher and an un-distilled version of the same student model to confirm the distillation actually helped.
- Monitor in production. Track accuracy drift over time, since a student trained on last quarter’s teacher outputs can fall behind as the underlying task or data shifts.
OpenAI’s built-in distillation workflow follows this same pattern in a managed way: developers generate completions from a frontier model like GPT-4o with store: true, filter the stored completions down to the best examples, and kick off a fine-tuning job on a smaller model like GPT-4o mini directly from the platform, closing what used to be a manual, multi-step, error-prone pipeline. Once you have a distilled student running, pairing it with an efficient serving setup — see our guide on local LLM inference — maximizes the cost and latency gains.
Common Pitfalls and How to Avoid Them
Teams that get burned by distillation projects tend to hit the same handful of problems. Training on too narrow a slice of examples produces a student that looks great on your eval set and falls apart on real traffic. Skipping quality filtering on teacher outputs means the student inherits the teacher’s mistakes at scale. And treating distillation as a one-time project instead of an ongoing process means the student silently drifts out of sync as your task or user base evolves.
Temperature scaling and intermediate-layer matching have also been shown to reduce hallucination rates in the resulting student model, so it’s worth tuning these rather than using default settings out of the box.
FAQ
What’s the difference between knowledge distillation and fine-tuning?
Fine-tuning adapts a model using labeled examples of correct answers. Knowledge distillation specifically trains a smaller student model to mimic a larger teacher model’s outputs (and often its reasoning), which is a specific application of fine-tuning that uses teacher-generated data instead of, or alongside, human-labeled data.
Do I need access to the teacher model’s internals to distill it?
No. Black-box distillation, which only requires the teacher’s text outputs, works with closed models like GPT-4o or Claude and is what most commercial distillation tools (including OpenAI’s) use. White-box distillation, which needs access to internal parameters, is only possible with open-source teacher models.
How much smaller can a distilled model be while staying accurate?
Published results show distilled models can be roughly 10x smaller and faster than their teacher while retaining 95%+ of task-specific performance, though the exact ratio depends heavily on task complexity and training data quality.
How many examples do I need to distill a model well?
It varies by task: a few hundred high-quality examples can be enough for narrow, well-defined tasks, while broader or more open-ended tasks typically need thousands of filtered examples to generalize well.
Conclusion
LLM knowledge distillation has moved from a research technique to a practical, often platform-supported workflow for cutting inference costs without gutting accuracy. Start narrow — pick one well-defined task, generate a filtered set of teacher completions, and fine-tune a small student against both hard and soft labels — before trying to distill broader capability. Measured against the right baselines and monitored over time, a distilled model can deliver most of a frontier model’s quality at a fraction of the serving cost.
If you’re weighing whether distillation is worth it for your workload, start by estimating your current per-token serving cost against a distilled alternative’s expected accuracy on your actual eval set, not a generic benchmark — that’s the number that will tell you whether the investment pays off. Explore more practical AI engineering guides in our Technology section.
