Open-weight science model Hydrogen-1.0 beats GPT-6 Astra on FSR at 72% lower cost
AKAMA199 · reddit · 2026-11-04
Deep Principle released Hydrogen-1.0, an open-weight model post-trained from GLM-5.3 for chemistry, materials science, and life sciences.
Benchmarks:
- FrontierScience Research pass rate 38.3% vs. 33.3% for GPT-6 Astra at max thinking
- Estimated FSR output cost $0.25/problem vs. Astra's $0.88 (72% lower)
- GPQA Diamond 92.9% (Kimi K3: 93.5%); leads Astra on scientific computing (+5.13pp) and forward reaction prediction (+4.76pp), trails on NMR structure elucidation (-3.33pp)
Training: SFT + RL on domain data, scientist feedback, lab records, and discovery trajectories capturing how next steps change after failures or feedback.
Walkthroughs (model+tool demos, not autonomous success rates): polymer MD with GROMACS (repaired topology, 40 ns trajectory), reaction networks, NMR candidate comparison, a 12-vial Suzuki coupling protocol with virtual checks, and 5-round feedback-guided materials search.
Weights are on Hugging Face; the team is soliciting feedback from computational chemistry and materials researchers.
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