Open-weight AI for GMP teams

Which AI models can handle GMP work?

GMP Bench scores open-weight large language models on pharmaceutical quality, manufacturing, and compliance tasks.

Most GMP work runs on data you cannot hand to a third party. Batch records, deviation investigations, and process know-how stay inside the company. So GMP Bench focuses on models whose weights you can download, pin to a version, and (optionally) run on hardware you control. The leaderboard shows which of them are good enough for real GMP work.

Compare Models Share a GMP Test Case

GMP Bench helps you decide

A model can sound impressive in a chat window and still struggle with regulated pharmaceutical work. GMP Bench looks at the questions quality teams actually need answered.

Can it reason about GMP?

We test whether a model understands regulations, quality systems, and the language used in GMP manufacturing.

Can it help with paperwork?

We measure how well models draft and structure common documents like SOPs, deviation summaries, CAPAs, and batch record narratives.

Can you actually run it?

Not every open-weight model fits in a company server room. The leaderboard separates models that run on a single GPU server from ones that need datacentre hardware, so you can filter down to what your IT department can host.

Why this is important

GMP work creates a lot of critical documentation

In pharmaceutical manufacturing, documentation is not admin overhead. Batch records, SOPs, deviations, CAPAs, validation summaries, environmental monitoring reports, and training records are part of how product quality and patient safety are protected.

AI can speed up document work

Many GMP documents can be drafted faster when a quality or manufacturing expert uses AI as a drafting partner, reviewer, or checklist assistant.

GMP data is often sensitive

Teams may handle patient information, manufacturing know-how, supplier details, investigations, and intellectual property. That makes data control more important than in many other AI use cases.

Closed models are hard to validate

A GMP system has to stay in a validated state, and a hosted model can change version underneath you. With open weights you pin an exact version, rerun the benchmark when you choose to upgrade, and keep the evidence.

Top open-weight models

The highest scoring open-weight models across the benchmark. Self-hostable models run on 70B parameters or fewer.

View full leaderboard

#	Model	Score	Deployment
1	GLM-5.3-Flash	90.4%	Cloud-scale
2	Hy4 Preview	90.0%	Cloud-scale
3	Qwen3.8 Max (2.4T-A95B)	89.4%	Cloud-scale
4	Kimi K3	89.0%	Cloud-scale
5	GLM-5.3	88.6%	Cloud-scale

What is tested?

The test cases are designed around the two things a pharma user usually needs from AI: correct GMP understanding and useful draft output.

GMP knowledge

Questions cover ICH guidelines, FDA CFR Part 211, EU GMP Annex requirements, and pharmacopeia standards. Answers are checked against verified references.

Task completion

Models draft practical outputs such as SOP sections, deviation reports, CAPA plans, and batch record narratives. Scoring looks at accuracy, completeness, and regulatory fit.

Real-world relevance

Test cases can be submitted and reviewed by pharmaceutical professionals so the benchmark stays connected to the work done in GMP-regulated environments.

How it works

Submit a test case

Propose a GMP question or document task with a reference answer or scoring rubric.

Models are evaluated

Each model runs the test case. Knowledge answers are checked against references. Document tasks are scored with a rubric.

Compare results

Compare scores by task type, by model creator, and by whether the model runs on hardware you own.

Built & maintained by Toon Lambrechts / Qualified AI