Benchmark Almanac

Mol-Instructions

Intern-S2-397B 54% leads since September 2026
Fading

Models answer molecule-oriented, protein-oriented and biomolecular text instructions, such as reaction prediction, molecule description and protein function questions. Scored per task with task metrics.

It is mainly an instruction-tuning dataset. Labs report its test split as a benchmark.

Released
June 2023
Kind
Genomics and life sciences
Level
Expert
Scored
Checked automatically
Answers
Short answer
Tasks
All public
Last in a launch post
September 2026, dropped before it saturated
Last model added
September 2026

In the authors’ words

Large Language Models (LLMs), with their remarkable task-handling capabilities and innovative outputs, have catalyzed significant advancements across a spectrum of fields. However, their proficiency within specialized domains such as biomolecular studies remains limited. To address this challenge, we introduce Mol-Instructions, a comprehensive instruction dataset designed for the biomolecular domain. Mol-Instructions encompasses three key components: molecule-oriented instructions, protein-oriented instructions, and biomolecular text instructions. Each component aims to improve the understanding and prediction capabilities of LLMs concerning biomolecular features and behaviors. Through extensive instruction tuning experiments on LLMs, we demonstrate the effectiveness of Mol-Instructions in enhancing large models' performance in the intricate realm of biomolecular studies, thus fostering progress in the biomolecular research community. Mol-Instructions is publicly available for ongoing research and will undergo regular updates to enhance its applicability.

Abstract of Mol-Instructions: A Large-Scale Biomolecular Instruction Dataset for Large Language Models (arXiv:2306.08018, CC0)
2
Models scored in lab cards
2
Launch posts list it, from 1 lab
1
System card reports it, from 1 lab

Intern-S2-397B leads with 54%

Best score per model as labs report them in their own cards. Labs pick their own settings, so these compare less cleanly than independent runs.

  1. 1 Intern-S2-397B 54%
  2. 2 Intern-S1-Pro 48.8%

Source: Labs’ model cards

#ModelScoreSettingDateSource
1 Intern-S2-397B 54% Sep 2026 Lab model card
2 Intern-S1-Pro 48.8% Feb 2026 Lab model card

2 models have held number one in labs’ own numbers since its release in June 2023

Lab claims count from the day the card came out, independent runs from the model’s release.

Shanghai AI Lab
Lab claims Not tested since Sep 2026

Source: Labs’ model cards

Scores over time

All the scores we have, one dot each, so a model tested at several settings appears more than once.

Reported by the lab in its model card Best so far
0% 20% 40% 60% 80% 100% 2026 Near the ceiling (85%) Intern-S2-397B Intern-S1-Pro 0% 20% 40% 60% 80% 100% 2026 Near the ceiling (85%) Intern-S2-397B Intern-S1-Pro

Source: Labs’ model cards

Labs headline it less often now, last in September 2026

Launch posts from every lab that has listed it, one tick per post. Tall colored ticks include it, and short gray ones leave it out.

Shanghai AI Lab
DateLaunch postScoreAs the post writes it
Sep 13, 2026 Intern-S2-397B 54 Mol-Instructions (Bio-molecular Instruction)
Feb 2, 2026 Intern-S1-Pro 48.8 Mol-Instructions

One system card reports it

Besides launch posts, we read the system cards, model cards and technical reports labs publish with a release. They report far more, including safety and frontier-risk tests.

DateDocumentScoreAs the document writes itSection
Mar 26, 2026 Intern-S1-Pro technical report 48.8 Mol-Instructions, Bio-molecular Instruction, non-thinking (Table 2) Capabilities

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