{"version":1,"type":"story","url":"https://digestai.news/story/iquestlab-releases-iquest-q1-a-320b-parameter-moe-model-for-coding-age","json":"https://digestai.news/story/iquestlab-releases-iquest-q1-a-320b-parameter-moe-model-for-coding-age.json","markdown":"https://digestai.news/story/iquestlab-releases-iquest-q1-a-320b-parameter-moe-model-for-coding-age.md","slug":"iquestlab-releases-iquest-q1-a-320b-parameter-moe-model-for-coding-age","headline":"IQuestLab releases IQuest-Q1, a 320B-parameter MoE model for coding agents","summary":"IQuestLab announced IQuest-Q1, a mixture-of-experts model with about 320 billion parameters, designed for agentic coding, reasoning, and multi-step tool use. The model activates roughly 15 billion parameters per token and lacks native multimodal capabilities. It is available via an OpenAI-compatible API, with deployment instructions provided for SGLang and vLLM frameworks.\n\nIQuest-Q1 scored on benchmarks like mini-SWE-agent and IQuest-CLIBench, though no external validation has been confirmed. The lab notes it remains an early-stage model with limitations in output reliability, tool integration, and real-world CLI task handling. Users are advised to review generated code and verify results manually. The model’s context window is capped at 512K tokens, and the lab recommends specific settings for optimal performance, including temperature 1.0, top-p 0.95, and top-k 20.","keyPoints":["IQuest-Q1 is a 320B-parameter MoE model from IQuestLab for coding agents, with 15B active per token","OpenAI-compatible API deployment supported via SGLang or vLLM, with 512K-token context limit","Model lacks multimodal input and requires manual verification of generated code and reasoning"],"whyItMatters":"IQuest-Q1 expands the tooling for AI-driven coding agents, though its early-stage status and reliance on manual oversight may limit immediate adoption.","category":{"slug":"models","name":"Generative AI & Models","url":"https://digestai.news/category/models"},"entities":{"companies":["IQuestLab"],"models":["IQuest-Q1","Claude Code 2.1.140","Codex 0.142"],"people":[]},"firstPublishedAt":"2026-09-29T10:24:10Z","updatedAt":"2026-09-29T10:24:10Z","sourceCount":1,"hasPrimarySource":true,"sources":[{"outlet":"huggingface.co","title":"IQuestLab/IQuest-Q1 · Hugging Face","url":"https://huggingface.co/IQuestLab/IQuest-Q1","publishedAt":"2026-09-29T10:24:10Z","type":"primary","primary":true,"lead":true}],"sourceNotes":null,"discussions":[{"site":"Reddit","url":"https://www.reddit.com/r/LocalLLaMA/comments/1wt6gkp/iquestlabiquestq1_hugging_face/","points":null}],"thread":null,"cite":{"text":"Digest AI, \"IQuestLab releases IQuest-Q1, a 320B-parameter MoE model for coding agents\", 29 September 2026, https://digestai.news/story/iquestlab-releases-iquest-q1-a-320b-parameter-moe-model-for-coding-age","publisher":"Digest AI","title":"IQuestLab releases IQuest-Q1, a 320B-parameter MoE model for coding agents","datePublished":"2026-09-29T10:24:10Z","url":"https://digestai.news/story/iquestlab-releases-iquest-q1-a-320b-parameter-moe-model-for-coding-age"},"generatedBy":"Written by Digest AI's editorial model from the linked sources; the sources are the record.","license":"Headlines, digests and key points are written by Digest AI and may be quoted with a link to the story page. Linked articles belong to their publishers. Terms: https://digestai.news/terms#reuse"}