Evaluating open LLMs for agentic analysis orchestration in a typical biomedical lab.

Area of Science:

• Biomedical Data Analysis
• Artificial Intelligence
• Computational Biology

Background:

• Agentic tools, powered by large language models (LLMs), are poised to dominate routine biomedical data analysis.
• High per-call inference costs of frontier LLMs currently limit their widespread adoption for these tasks.

Purpose of the Study:

• To evaluate if free, locally-runnable open-weight LLMs can execute repetitive biomedical data analysis steps with accuracy comparable to frontier models.
• To identify cost-effective LLM solutions for large-scale biomedical data processing.

Main Methods:

• Claude's Opus was used to generate detailed execution plans for per-sample variant calling.
• Six open-weight LLMs (released in 2026) were tested against these plans on desktop GPUs.
• Performance was validated using a 36-cell error-injection matrix.

Main Results:

• The open-weight model qwen3.6:27b successfully replicated frontier accuracy across all tested plans.
• qwen3.6:27b demonstrated cell-for-cell equivalence with Opus on the error-injection matrix.
• The implementer LLM operated effectively on affordable hardware (sub-$2,000 Jetson or Mac Mini).

Conclusions:

• Open-weight LLMs are capable of matching frontier accuracy in complex biomedical data analysis tasks.
• Local execution of open-weight LLMs offers a cost-effective alternative to proprietary models for routine analysis.
• A framework and associated artifacts are provided for ongoing evaluation of rapidly evolving open-weight LLMs.