AllSpark Research

Iris-mini

AllSpark Research

Iris-mini is AllSpark Research’s open-weight search-specialized language model, published in the September 3 Iris paper. It is post-trained from Qwen3.6-35B-A3B through alternating supervised fine-tuning and reinforcement learning, learning what to search, how to read evidence, and when enough information has been collected. Its mixture-of-experts backbone has 35B total and 3B active parameters, with a 256K context. Apache-licensed weights and an evaluation harness are available. The checkpoint generates reasoning, tool calls, and final language answers; search itself is performed by external tools. Published scores depend strongly on context resets and retry policies.

Iris-pro

AllSpark Research

Iris-pro is AllSpark Research’s open-weight search-specialized language model, published in the September 3 Iris paper. It is post-trained from Qwen3.5-397B-A17B through alternating supervised fine-tuning and reinforcement learning, learning what to search, how to read evidence, and when enough information has been collected. Its mixture-of-experts backbone has 397B total and 17B active parameters, with a 256K context. Apache-licensed weights and an evaluation harness are available. The checkpoint generates reasoning, tool calls, and final language answers; search itself is performed by external tools. Published scores depend strongly on context resets and retry policies.