Deep Principle Releases Hydrogen-1.0: A 753B Open-Weight Model for Scientific Discovery

Deep Principle has released Hydrogen-1.0, an open-weight scientific model for chemistry, materials science and life sciences.It is built by post-training Z.ai’s GLM-5.3 on domain data, laboratory records and agent discovery trajectories.

For developers, it offers open weights that report a FrontierScience-Research win over GPT-6-Astra.The weights ship under the MIT License.TL;DR Size: 753B parameters (MoE, ~40B active per the GLM-5.3 base), 1M-token context, 128K max output.

Runs on: 8 GPUs with SGLang (tested); NVFP4 for NVIDIA B200/B300, MXFP4 for AMD MI350X/MI355X.Performance: Beats GPT-6-Astra on FrontierScience-Research; roughly matches Kimi-K3 on GPQA-Diamond.Best: 38.3% on FrontierScience-Research vs 33.3% for GPT-6-Astra (self-reported).

Worst: Trails GPT-6-Astra (max thinking) by 3.33 points on NMR structure elucidation.What is Hydrogen-1.0?Hydrogen-1.0 is a scientific research assistant model from Deep Principle, a Hangzhou-based AI-for-materials company.The model card describes it as a GLM-5.

3 derivative for scientific problem solving.It targets question framing, experiment planning, results analysis and open-ended discovery.Deep Principle team states that proprietary models produced recent AI science results, but their discovery capabilities are not open.Hydrogen-1.

0 is its attempt at a reproducible, inspectable alternative.How was Hydrogen-1.0 trained?The pipeline combines supervised fine-tuning (SFT) with reinforcement learning (RL).Data comes from 4 sources: Domain knowledge: chemistry, materials science and life sciences data.

Research scenarios: task-specific feedback from Deep Principle scientists.Laboratory records: full-cycle electronic lab notebook records captured through Mira.Discovery trajectories: sequences of choices and feedback, including failed attempts.

Infrastructure builds on Megatron and SGLang across NVIDIA CUDA and AMD ROCm.The team keeps dynamic sparse attention causal across context-parallel shards.Muon optimizer state is moved between CPU, GPU and disk per backend.

RL uses resumable rollouts, and each token keeps its original behavior log-probability for importance correction.A layout adapter converts training checkpoints into BF16 and 128×128 blockwise FP8 inference weights.Full datasets and recipes are not released.How does Hydrogen-1.

0 perform on benchmarks?On public benchmarks, Deep Principle reports Hydrogen-1.0 outperforms GPT-6-Astra on FrontierScience-Research.The tweet thread from one of the founders’ report 93.0% on GPQA-Diamond.That improves on GLM-5.3 and roughly matches Kimi-K3, per the blog.

Internal research-task evals compare it with GPT-6-Astra at maximum thinking: Scientific computing: Hydrogen-1.0 leads by 5.13 percentage points.Forward reaction prediction: Hydrogen-1.0 leads by 4.76 percentage points.NMR structure elucidation: GPT-6-Astra leads by 3.33 points.

In transition metal complex design, models chased a larger HOMO-LUMO gap over 5 rounds.By round 5, Hydrogen-1.0 reached a mean best-so-far gap of 4.318 eV.GPT-6-Astra reached 3.689 eV and Kimi-K3 reached 3.353 eV.On 117 experiment-planning questions, Hydrogen-1.0 scored above Kimi-K3.

GPT-6-Astra and Opus-5.5 were excluded after refusing more than 50% of requests.window.addEventListener("message",function(e){if(e.data&&e.data.mtpH2xHeight){var f=document.getElementById("mtp-h2x-frame");if(f)f.style.height=e.data.mtpH2xHeight+"px";}}); What can it do in a real lab workflow?

The technical post shows 4 demos.Hydrogen-1.0 writes Python to build a polyethylene melt and runs GROMACS.It detects a topology error, repairs it, and analyzes the corrected 40 ns trajectory.It grows an Ireland-Claisen reaction network of 27 structures through 30 plausible reactions.

It ranks 7 candidate monoformylation products of 2-bromonaphthalene from a 1H NMR trace.It also writes a JSON protocol for a 12-vial Suzuki coupling study.A virtual lab compilation step catches an aspiration without a fresh tip before execution.How do you deploy Hydrogen-1.0?

Weights are on Hugging Face and ModelScope.Deep Principle team tested BF16 serving with SGLang on 8 GPUs using tensor parallelism of 8.NVFP4 targets NVIDIA B200 and B300.MXFP4 targets AMD Instinct MI350X and MI355X.The server exposes an OpenAI-compatible API with a reasoning_effort setting.

Hydrogen-1.0 vs GLM-5.3, Kimi K3 and GPT-6-Astra FeatureHydrogen-1.0GLM-5.3Kimi K3GPT-6-AstraDeveloperDeep PrincipleZ.aiMoonshot AIOpenAITotal params753B753B (HF)2.8TNot disclosedActive params~40B (inherited)40B (LM Studio)16 of 896 expertsNot disclosedContext1M1M1M1.

05MWeights / licenseOpen, MITOpen, GLM-5.3 LicenseOpenProprietaryModalityTextTextText + visionNot disclosed hereScience focusChemistry, materials, life sciencesCoding, agentsGeneralGeneralFrontierScience-Research38.3%Not disclosedNot disclosed33.3% (per Deep Principle)GPQA-Diamond93.

0%Not disclosedNot disclosed96.0% (OpenAI-reported)API price (in/out per 1M)NeuroSpark: Not disclosed$1.40 / $4.40 (Z.ai)$3.00 / $15.00$10.00 / $50.00 (OpenAI) Key Takeaways Hydrogen-1.0 is an MIT-licensed, 753B GLM-5.3 post-train for science.It reports 38.

3% on FrontierScience-Research, above GPT-6-Astra’s 33.3%.Training adds lab records and discovery trajectories, not just papers.It still trails GPT-6-Astra on NMR structure elucidation.NVFP4 and MXFP4 builds target Blackwell and MI350-class GPUs.

Check out the Technical blog, Model weights on Hugging Face and Usage notes.Also, feel free to follow us on Twitter and don’t forget to join our 150k+ML SubReddit and Subscribe to our Newsletter.Wait!are you on telegram?now you can join us on telegram as well.

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