Million-Token Context Doesn't Mean Better Performance
FinanceYF5 · x · 2026-07-14
A post quotes a Prime Intellect engineer arguing that while everyone is chasing million-token contexts, the real bottleneck isn't capacity, but the inability to reason effectively.
For example, GPT-5.5 achieves roughly 80% retrieval accuracy at a 256k context, but drops to 36% when expanded to 1 million. This is described as classic context rot: the longer the context, the harder it is for the model to reliably retrieve key information.
The post concludes that:
- Simply expanding context size may not be enough to fix agents
- More practical approaches include continuous learning, training custom trajectories, and learning from feedback in real-world environments
Related event: Million-Token Context Doesn't Equal Better Retrieval(2 posts)→
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