AllSpark's Open-Source Iris Search Agent Rivals Closed-Source Tools with Context Management

AllSpark Research has launched Iris, an open-source AI search agent available in two sizes — Iris-mini (35B total, 3B active parameters) and Iris-pro (397B total, 17B active parameters) — both built on a Mixture-of-Experts architecture with 256K context windows and released under the Apache 2.0 license. Iris achieves strong benchmark scores, including 88.6 on BrowseComp and 92.9 on DeepSearchQA for the pro model, competing closely with previously dominant closed-source search agents. A key finding from the research is that intelligent context management — rather than simply scaling up parameters — is the primary driver of performance, with the smaller Iris-mini benefiting more from context strategies than the larger model. The agents were trained using an alternating supervised fine-tuning and reinforcement learning process run against live web search, not static offline data. By releasing model weights, context management strategies, and data construction methods together, AllSpark aims to close a two-year gap between open- and closed-source search agent capabilities.