Conclusion#We presented Context-1, a 20B parameter agentic search model that reaches the Pareto frontier of retrieval performance with respect to cost and latency. On our generated benchmarks, Context-1 matches or exceeds models that are orders of magnitude larger — and when run in a 4x parallel configuration, it does so while remaining cheaper than a single call to those models. These gains hold across public benchmarks as well: on BrowseComp-Plus, SealQA, FRAMES, and HLE, Context-1 delivers retrieval quality comparable to frontier LLMs at a fraction of the compute.
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