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Context engineering and AI cost

Does a bigger context window make AI agents more accurate?

Alexandru Lupascu, CTO & Co-founder · Updated

No: a bigger context window lets an AI agent hold more tokens, up to 1M tokens on current Claude models such as Claude Opus 5.5 and Claude Sonnet 5.5, but it does not make the model use those tokens more accurately. Anthropic's context window documentation says "more context isn't automatically better" and that "accuracy and recall degrade" as token count grows. A bigger window helps when a task truly needs a lot of text at once. It does not help when the extra tokens are old turns, raw tool output or outdated facts.

What does research say about context length and accuracy?

Three studies point the same way:

  • Lost in the Middle (Liu et al., TACL 2023): accuracy is highest when the relevant passage sits at the start or end of the input and "significantly degrades" in the middle, "even for explicitly long-context models."
  • Context Rot (Chroma; Hong, Troynikov and Huber, July 2025): across all models tested, focused prompts of about 300 tokens scored "significantly higher" than full prompts of about 113,000 tokens that contained the same answer.
  • Multi-IF (He et al., arXiv 2410.15553): all 14 models tested followed instructions less reliably with each added turn; o1-preview fell from 0.877 average accuracy on turn one to 0.707 on turn three.

The common finding: more tokens and more turns make the same task harder, so the window size sets a limit, not a level of accuracy.

When does a bigger context window help?

A bigger window helps when the model must read one large input as a whole, and Anthropic's documentation notes that "a larger context window allows the model to handle more complex and lengthy prompts." It also gives a long-running agent headroom before it must compact. Size still costs money: on Claude 4.6 and later models (except Claude Haiku 5.5), Anthropic bills the 1M window at standard rates, and its pricing page states that "a 900k-token request is billed at the same per-token rate as a 9k-token request", so the larger request costs about 100 times as much. Lost in the Middle also found that going beyond 20 retrieved documents improved accuracy by only about 1.5% for GPT-3.5-Turbo and about 1% for Claude-1.3, while increasing input length "and thus latency and cost."

What should you do instead of filling the window?

Treat the window as headroom and decide what goes into it on each turn:

  1. Measure accuracy on your own tasks twice: with the full session and with a trimmed context holding only the facts the task needs.
  2. Retrieve fewer, better items and rerank them before they reach the model.
  3. Put the instruction and the key facts at the start or end of the input.
  4. Clear old tool results and compact long sessions, as Anthropic's context engineering guide recommends.
  5. In long conversations, restate the standing instructions where the model will see them on the current turn.

Background: what context rot is and how to reduce agent token costs.

Where does elephant.broker fit?

elephant.broker takes the opposite approach to a bigger window: on every turn the runtime builds a smaller working set from your tools, teammates and past sessions, ranks and prunes it, and places it where the model pays attention, with your model unchanged. elephant.broker reports on its website +36% better accuracy, with stale-state errors falling from 47% to 11%, and 40-80% less context per task. These are elephant.broker's own reported results, not an independent or head-to-head test. The runtime adds a retrieve and rerank step to every turn. More in what context engineering is.

Sources

Product details come from each vendor's public documentation as viewed on 2026-10-28 and may have changed. Product names are trademarks of their respective owners; elephant.broker is not affiliated with them.