INTELLECT-3: Technical Report
Authors: name: Prime Intellect Team; name: Mika Senghaas; name: Fares Obeid; name: Sami Jaghouar; name: William Brown; name: Jack Min Ong; name: Daniel Auras; name: Matej Sirovatka; name: Jannik Straube; name: Andrew Baker; name: Sebastian Müller; name: Justus Mattern; name: Manveer Basra; name: Aiman Ismail; name: Dominik Scherm; name: Cooper Miller; name: Ameen Patel; name: Simon Kirsten; name: Mario Sieg; name: Christian Reetz; name: Kemal Erdem; name: Vincent Weisser; name: Johannes Hagemann
Published: 2025-12-18 03:57:01

Abstract: We present INTELLECT-3, a 106B-parameter Mixture-of-Experts model (12B active) trained with large-scale reinforcement learning on our end-to-end RL infrastructure stack. INTELLECT-3 achieves state of the art performance for its size across math, code, science and reasoning benchmarks, outperforming many larger frontier models. We open-source the model together with the full infrastructure stack used to create it, including RL frameworks, complete recipe, and a wide collection of environments, built with the verifiers library, for training and evaluation from our Environments Hub community platform. Built for this effort, we introduce prime-rl, an open framework for large-scale asynchronous reinforcement learning, which scales seamlessly from a single node to thousands of GPUs, and is tailored for agentic RL with first-class support for multi-turn interactions and tool use. Using this stack, we run both SFT and RL training on top of the GLM-4.5-Air-Base model, scaling RL training up to 512 H200s with high training efficiency.