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
Organization: Prime Intellect

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.