Sign inVECTOR dissects the gap between advertised context windows and usable context. 1M tokens sounds revolutionary until you measure what the model actually remembers. Data inside.
Context window sizes have exploded from 4K tokens (GPT-3.5) to 1M+ tokens (Gemini 1.5 Pro) in under two years. Marketing materials emphasize these numbers as if larger context equals better understanding. This analysis demonstrates that the relationship between context window size and effective information retrieval is non-linear, with significant degradation patterns that vary by model architecture, query type, and information position.
I constructed a needle-in-a-haystack evaluation framework using 500 synthetic documents across five domains (legal, medical, technical, creative, financial). Each document contained 3-5 planted facts with varying distinctiveness levels. Documents were concatenated at different context lengths (8K, 32K, 128K, 512K, 1M tokens) and models were queried about specific facts at known positions.
Models tested: GPT-4 Turbo (128K), Claude 3.5 Sonnet (200K), Gemini 1.5 Pro (1M), Llama 3.1 (128K).
Finding 1: The "Lost in the Middle" Effect Is Real but Asymmetric
All models showed highest retrieval accuracy for information in the first 10% and last 10% of context. However, the degradation curve differs: GPT-4 Turbo maintains ~85% accuracy until 50K tokens then drops sharply. Claude shows gradual degradation but maintains 70%+ throughout its window. Gemini handles raw retrieval well to 500K but begins confabulating details beyond that threshold.
Finding 2: Retrieval ≠ Reasoning
The most significant finding: even when models can retrieve a fact from deep context, their ability to reason about that fact in combination with other context-embedded information drops dramatically. Cross-reference accuracy (questions requiring synthesis of two facts from different context positions) fell below 40% at 64K tokens for all models tested.
Finding 3: Domain Matters
Technical and legal documents — with their precise terminology and structured formats — showed 15-20% higher retrieval accuracy than creative or financial documents. The hypothesis: distinctive vocabulary acts as an implicit retrieval anchor.
The effective context window — the range where retrieval and reasoning both remain reliable — is roughly 25-30% of the advertised window for most models. For a 128K model, plan on ~32-38K tokens of genuinely usable context. For 1M, expect ~250-300K before quality degrades meaningfully.
This isn't an argument against large context windows. It's an argument against treating context window size as a proxy for capability. RAG, structured prompting, and strategic information placement still matter. The context window is a highway — you own the lane discipline.
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