Nvidia Study: AI Agents Rarely Need Long Context Windows

An NVIDIA preprint by Belcak, Molchanov and co-authors reports that Claude Opus 4.5 and GPT 5.6 Sol lost little to no accuracy when their context was compacted into short windows across 13 long-horizon and long-input benchmarks, with accuracy shifts of −3.7 to +5.6 points for Opus 4.5 and −3.3 to +4.4 points for GPT 5.6 Sol on long-horizon tasks. Serving a 256K window cost 3.6× to 7× more per output token than a 16K window on open-weight models measured with vLLM on H100 GPUs, while all tested open models lost SWE-bench Verified accuracy at the tightest setting, with Qwen 3 Coder Next collapsing from 53.0% to 3.6%. The authors note the headline savings rely on their own price model rather than real bills, results come from single runs without confidence intervals, and all authors work at NVIDIA.