Blog · 2026-01-09 · Analysis

How to evaluate context windows

How to evaluate context windows. A short analysis built from the index, with the model-level data behind the claim.

What to compare in context windows

An evaluation is only useful if it answers a decision. For context windows, decide first whether you are choosing a model, validating a price or checking a quality claim.

A benchmark leaderboard is a starting point. It tells you which models are worth testing; it does not tell you which one fits your workflow.

How to score it

Use a small set of examples with clear pass/fail criteria. Run every candidate on the same set. Record quality, cost and latency for each run. GPT-5.2 Pro and GPT-5.1 Reasoning make a useful high/low pair for the first pass.

  • Write the rubric before looking at outputs.
  • Use blind review where possible.
  • Include edge cases, not just happy paths.
  • Re-run after any model or prompt change.

A simple evaluation plan

  • Pick 20 representative examples.
  • Define pass/fail for each.
  • Run three candidates.
  • Compare cost per pass.
  • Choose the cheapest model that clears the bar.

Models relevant to context windows

ModelProviderInput / 1MContextAvg score
GPT-5.2 ProOpenAI$21.322,000,00089.6
GPT-5.2 MaxOpenAI$21.391,000,00089.2
Claude Opus 4.5 ProAnthropic$20.98200,00089.1
GPT-5.1 ReasoningOpenAI$20.27400,00088.3
Claude Opus 4.5 ReasoningAnthropic$20.912,000,00088.2

Related

FAQ

What is the main takeaway of this context windows analysis?

An evaluation is only useful if it answers a decision. For context windows, decide first whether you are choosing a model, validating a price or checking a quality claim.

Are the figures official?

No. The figures come from the AI & LLM Index demo dataset. Prices and scores are compiled for comparison; benchmark values are modelled estimates.

How should I use this in a buying decision?

Use it to build a shortlist, then test the shortlist on your own examples. Check the model pages for current price and context details.