Unverified50% confidenceFactExact time
The context window refers to the total number of tokens a model can simultaneously "remember" and factor into reasoning within a single session, and is a hard bottleneck for long-horizon task capability. The Gemini series publicly claims support for million-level token contexts.
1
Sources
50%
Confidence
Long-term
Relevance
10/13/2026
First Seen
Sources
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Related Claims
UnverifiedPokémon games expose the shortcomings of current large models in persistent memory and spatial reasoning.67% similarUnverifiedIn long-chain, multi-iteration autonomous agent tasks, models repeatedly read and write large volumes of conversation history for self-verification, error recovery, and context maintenance, causing token consumption to far exceed single-turn Q&A scenarios (token inflation).67% similarUnverifiedReasoning models and verbose system prompts can significantly change the actual number of tokens consumed by a task.66% similarVerifiedMulti-turn conversation configuration addresses the context memory problem, determining how many rounds of historical information an agent can retain during continuous dialogue.66% similarUnverifiedIn loop scenarios, each iteration requires feeding the entire conversation history back into the model, causing per-iteration token consumption to increase incrementally due to context accumulation, resulting in a cost curve steeper than linear.65% similar
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