Gemini 3 Pro advertises 10M tokens of context. At 1M tokens, 8-needle MRCR drops to 26.3%. Advertised ≠ usable. Long-context evaluation tells you the actual capacity of the model you are shipping on.
You have a 200-page contract. The model claims a 1M-token context. You paste the contract in and ask: "What is the termination clause?" The model answers — but answers from the cover page because the termination clause sits at 120k tokens deep, past where the model actually attends.
This is the 2026 context-capacity gap. Spec sheets say 1M or 10M. Reality says 60-70% of that is usable, and "usable" depends on the task.
Retrieval (single needle in haystack): near-perfect up to the advertised max on frontier models.
Multi-hop / aggregation: degrades sharply past ~128k on most models.
Reasoning over dispersed facts: the first task to fail.
Long-context evaluation measures these axes. This lesson names the benchmarks, what each actually measures, and how to build a custom needle test for your domain.
Full Access lesson
Keep going. The rest of this lesson is one step away.
Unlock all 503 lessons, video, code, quizzes and every future update. Pay in your own currency with Paystack.