# Hydrogen-1.0: Deep Principle's 753B open-weight science model, post-trained from GLM-5.3 under MIT

Canonical: https://theagenticweekly.com/news/deep-principle-hydrogen-1-0-open-weight-science-model · Evidence: Reported · First seen: 2026-11-02 20:35 UTC · Source published: not stated by the source · Brief published: 2026-11-02 21:05 UTC · Updated: 2026-11-02 21:05 UTC · Author: Agentic Weekly

> Hydrogen-1.0 is an open-weight scientific model from Deep Principle, which says it was built "by post-training GLM-5.3 on domain data from chemistry, materials science, and life sciences". Its Hugging Face listing is MIT-licensed and reports about 753 billion parameters; access requires accepting the gated repo's terms.

Models in this brief: [Hydrogen-1.0](https://theagenticweekly.com/models/hydrogen-1-0)

## What changed

- Deep Principle says Hydrogen-1.0 is GLM-5.3 post-trained on chemistry, materials science and life-sciences data plus records and agent trajectories from its own laboratories.
- The Hugging Face repo lists an MIT license, GLM-5.3 as the base model and 753,329,940,480 parameters in its safetensors weights; downloads are gated behind accepting the repo's terms.
- In Deep Principle's own evaluations it beats GPT-6-Astra on FrontierScience-Research and roughly matches Kimi-K3 on GPQA-Diamond; against Astra at maximum thinking it leads by 5.13 and 4.76 points on scientific computing and forward reactions but trails by 3.33 on NMR structure elucidation.
- On a five-round transition metal complex design test, the team reports a mean best-so-far HOMO-LUMO gap of 4.318 eV for Hydrogen-1.0, versus 3.689 eV for GPT-6-Astra and 3.353 eV for Kimi-K3.
- Training combines supervised fine-tuning with reinforcement learning on research outcomes, built on Megatron and SGLang across both NVIDIA CUDA and AMD ROCm.
- Four access routes are listed: weights on Hugging Face and ModelScope, an API through NeuroSpark, and the company's Mira AI Scientist app.

## Who it matters for

Research teams in chemistry, materials science and life sciences who want a self-hostable model they can inspect and fine-tune, and builders of lab or simulation agents who need protocol writing and experiment planning. General coding or chat users can ignore it: every benchmark in the announcement is scientific, and all of them are run by the vendor.

At roughly 753 billion parameters this is a multi-GPU deployment; the hosted API route is the practical way to try it without that hardware.

## In the source's words

> "Hydrogen-1.0 is available through four access routes"
> (Deep Principle blog, Introducing Hydrogen-1.0, https://blog.deepprinciple.com/introducing-hydrogen-1.0/)

> "Astra at maximum thinking still scores higher on NMR structure elucidation by 3.33 points."
> (Deep Principle blog, Introducing Hydrogen-1.0, https://blog.deepprinciple.com/introducing-hydrogen-1.0/)

## What we have not verified

- We did not run this release ourselves.
- All benchmark and discovery results are Deep Principle's own evaluations; several use internal test sets that have not been published.
- The Hugging Face model card is gated, so we read the repo metadata (license, base model, parameter count) but not the card text; context length and hardware requirements reported in press coverage are not confirmed here.
- We did not check NeuroSpark API pricing or availability.
- The announcement page carries no date, so the source publish time is left blank; the Hugging Face repo was last modified 2026-11-02 09:00 UTC.

## Timeline

- Source published: not stated by the source
- First seen by our watcher: 2026-11-02 20:35 UTC (via MarkTechPost)
- Brief published: 2026-11-02 21:05 UTC

## Sources

- [Deep Principle blog: Introducing Hydrogen-1.0](https://blog.deepprinciple.com/introducing-hydrogen-1.0/) (primary source) (fetched 2026-11-02)
- [Hugging Face: deep-principle/Hydrogen-1.0 (repo metadata)](https://huggingface.co/api/models/deep-principle/Hydrogen-1.0) (primary source) (fetched 2026-11-02)

## Related

- [Hydrogen-1.0: specs, price and alternatives](https://theagenticweekly.com/models/hydrogen-1-0)
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## How to cite

Agentic Weekly, "Hydrogen-1.0: Deep Principle's 753B open-weight science model, post-trained from GLM-5.3 under MIT", published 2026-11-02 21:05 UTC, https://theagenticweekly.com/news/deep-principle-hydrogen-1-0-open-weight-science-model