Quantization: a practical guide
A practical guide to quantization, using data from the AI & LLM Index. The goal is a shortlist and a decision, not a tour of every option.
What quantization is
quantization is a concept that shows up when teams design, evaluate or operate language model systems. The short definition matters less than the decisions it changes: what data goes into the prompt, how results are checked and where cost accumulates.
In the index, quantization is best understood through the models and benchmarks it touches. A concept is useful only if it helps you ask a sharper question about price, context, latency or quality.
Why it matters
Most production failures are not caused by a model being generally weak. They are caused by a mismatch between the task and the system around the model. quantization is one of the places where that mismatch shows up, which is why it deserves a explicit place in your design review.
How to evaluate it
- Define the behaviour you expect in one sentence.
- Build a small set of examples that should pass and examples that should fail.
- Measure both quality and cost per successful task.
- Re-run the set when you change model, prompt or retrieval.
Where it shows up in the index
You can see related signals on model pages and benchmark leaderboards. For a quick comparison, GPT-5.2 Pro and GPT-5.2 Max sit at different price and capability points, which makes them a useful pair for a small pilot.
Models worth testing
| Model | Provider | Input / 1M | Context | Avg score |
|---|---|---|---|---|
| GPT-5.2 Pro | OpenAI | $21.32 | 2,000,000 | 89.6 |
| GPT-5.2 Max | OpenAI | $21.39 | 1,000,000 | 89.2 |
| Claude Opus 4.5 Pro | Anthropic | $20.98 | 200,000 | 89.1 |
| GPT-5.1 Reasoning | OpenAI | $20.27 | 400,000 | 88.3 |
| Claude Opus 4.5 Reasoning | Anthropic | $20.91 | 2,000,000 | 88.2 |
Related
FAQ
What is the most important factor in quantization?
Fit to the task beats raw benchmark rank. Define the failure modes, pick the benchmarks that resemble them, then test a small shortlist on your own examples.
How many models should I evaluate?
Three is usually enough: a quality candidate, a value candidate and a fallback. More candidates add work without changing the decision.
How do I control cost?
Compare blended 3:1 price, watch output tokens, use caching where available and route simple requests to a cheaper model.