INTELLECT-3: Technical Report
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.
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Prime Intellect Team
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Mika Senghaas
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Fares Obeid
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Sami Jaghouar
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William Brown
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Jack Min Ong
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Daniel Auras
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Matej Sirovatka
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Jannik Straube
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Andrew Baker
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Sebastian Müller
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Justus Mattern
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Manveer Basra
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Aiman Ismail
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Dominik Scherm
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Cooper Miller
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Ameen Patel
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Simon Kirsten
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Mario Sieg
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Christian Reetz
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Kemal Erdem
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Vincent Weisser
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Johannes Hagemann
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2025-12-25T20:45:17.737Z
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https://cdn-avatars.huggingface.co/v1/production/uploads/606ae9ab0392350f35a22e37/7642Z9xUwSNf5oqFpGzyd.png
Johannes Hagemann
Johannes
user
Johannes
INTELLECT-3: Technical Report
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.
INTELLECT-3, a large Mixture-of-Experts model trained with reinforcement learning, achieves top performance across various benchmarks and is supported by an open-source RL infrastructure framework.
Mixture-of-Experts reinforcement learning RL infrastructure prime-rl asynchronous reinforcement learning GLM-4.5-Air-Base SFT H200s