Longer Context Windows Might Not Be The Best For AI

The prevailing assumption about AI is that more information available to a model means better reasoning and more reliable outputs. But recent research shows that expanding a model's context window actually degrades its ability to follow instructions precisely (Together AI, 2025).

The important thing to understand here is that attention capacity is finite. When a model must distribute attention across a larger input, each individual token receives less focused attention. In short prompts where instructions appear early and clearly, the model concentrates its attention mechanisms effectively. The instructions are few, the focus sharp, and the compliance high. But as context grows by adding background information, document context, conversation history, or additional requirements, the model's attention becomes diluted across all the additional tokens (Unite AI, 2025). Instructions that appeared prominent in short text become buried in context. The model literally loses focus on them.

While this might seem like a problem with how these models are trained, it’s actually an architectural problem. Transformer models, which power virtually all large language models, use attention mechanisms that scale quadratically with input length. Doubling the input size does not just double the computation. It quadruples it, and the attention distribution across all tokens becomes proportionally weaker. A model optimized to distribute attention effectively across 8,000 tokens becomes systematically worse at identifying which tokens are actually important when given 64,000 tokens (Together AI, 2025).

Research tested frontier language models including GPT-4o, Claude 3.5, and DeepSeek-R1 on instruction-following tasks. Models failed to follow instructions more than 75% of the time during reasoning processes l. The failure rate increased directly with task difficulty. As problems became more complex, instruction-following degraded further. But the underlying cause was not task complexity. It was context length. Models that performed acceptably on short-context versions of the same tasks collapsed when given the tasks within longer documents or with additional context layers (Together AI, 2025).

The second mechanism making this worse is what researchers call the "lost in the middle" problem. Instructions appearing in the middle of long prompts receive systematically less attention than instructions at the beginning or end (Unite AI, 2025). This is not because models are architecturally biased toward first and last positions, though some evidence suggests that too. It is because when attention is diluted across thousands of tokens, the relative importance weighting breaks down. An instruction buried among paragraphs of context effectively becomes invisible.

The third mechanism is conflicting instructions. Long contexts often contain multiple instructions, many of them conflicting or overlapping. A model asked to "be concise" but also "explain thoroughly" faces a genuine constraint conflict. When context is short, the model can resolve these conflicts because it is focusing on the complete instruction set. When context is long, the model often partially processes each instruction, missing some entirely and producing outputs that violate multiple requirements simultaneously.

Organizations attempting to solve this through prompt engineering discovered the limitations of that approach. Repeatedly reminding the model to "follow every instruction" and "do not skip steps" helps marginally, but does not solve the underlying architectural constraint. The model is not choosing to ignore instructions. It is architecturally unable to distribute sufficient attention to all of them simultaneously when processing extended context.

This problem does not even improve significantly with model capability. GPT-4o, one of the most capable models available at the time of the experiment, still failed instruction-following more than 70% of the time on complex reasoning tasks embedded in longer contexts. Claude 3.5 performed similarly. DeepSeek-R1, specifically optimized for reasoning, also demonstrated substantial instruction-following failure rates when context exceeded certain thresholds (Together AI, 2025). The capability to solve complex problems does not translate to the capability to reliably follow instructions while solving them.

So, you can either have short context and reliable instruction-following, or long context and degraded instruction compliance. The trade-off appears to be engineered into the architecture rather than being a temporary limitation awaiting better training or fine-tuning.

Researchers propose several mitigation strategies. Fine-tuning models specifically on instruction-following tasks with complex reasoning improved compliance from 11% to 27% in tested cases(Together AI, 2025). Breaking long tasks into sequential steps where each step operates with shorter context windows helps preserve instruction adherence across multi-step workflows (Unite AI, 2025). Using external systems like Retrieval-Augmented Generation to supply only the most relevant context for each step reduces the dilution problem by keeping context windows tighter (Spyglass MTG, 2025).

But none of these solutions address the fundamental architectural constraint. They are workarounds rather than solutions. A model designed to distribute attention across tokens still distributes that attention equally across all tokens regardless of relevance Making this distribution more intelligent is theoretically possible but has not been achieved at scale in current systems.

Thus, the models with the largest context windows are not necessarily the best at handling complex tasks. A model with a 128,000-token context window might actually perform worse on instruction-following than a model with a 32,000-token window for the same problem, because the attention dilution problem is more severe. The optimal context window is not "as large as possible." It is "large enough to provide necessary context but small enough to maintain attention focus on instructions" (Unite AI, 2025).