Deep Principle releases Hydrogen-1.0 open-weight model for chemistry and materials

Deep Principle released Hydrogen-1.0 on 2 November 2026 as an open-weight foundation model for chemistry, materials science and life sciences. Post-trained from Z.ai’s GLM-5.3 using supervised fine-tuning and resumable reinforcement learning across Megatron and SGLang on NVIDIA CUDA and AMD ROCm clusters, the model trains on curated scientific corpora, electronic lab notebook records and multi-step discovery trajectories generated with Deep Principle’s Mira agent.

Unquantised BF16 checkpoints and separate NVFP4 and MXFP4 quantised repositories are available on Hugging Face and ModelScope under the MIT licence, alongside Apache 2.0 inference guides, hosted access on the NeuroSpark API and integration in the Mira AI Scientist platform. Deep Principle renders its benchmark comparisons dynamically on HTML canvas rather than static chart images, reporting leads on scientific computing and forward reaction prediction while trailing GPT-6 Astra by 3.33 points on nuclear magnetic resonance structure elucidation. Deep Principle has not published per-token NeuroSpark API pricing in the launch announcement.

Key numbers

• 38.3% on FrontierScience-Research, ahead of GPT-6 Astra at 33.3%, plus 93.0% on GPQA-Diamond.
• 5.13 points ahead of GPT-6 Astra (max thinking) on scientific computing and 4.76 points ahead on forward reaction prediction.
• 4.318 eV mean HOMO-LUMO gap after five closed-loop transition-metal design rounds, versus 3.689 eV for GPT-6 Astra and 3.353 eV for Kimi-K3.
• Full BF16, NVFP4 and MXFP4 weights released under the MIT licence on Hugging Face and ModelScope.