Snorkel AI vs vLLM

Snorkel AI's public changelog is entirely research blog posts covering AI agent benchmarks — OSWorld 2.0, Terminal-Bench 3.0 and 4.0, T² scaling laws, and continual learning evaluation. These are not product release notes but research contributions Snorkel is publishing to establish credibility in the AI evaluation and training space. The company appears to be repositioning from data-labeling infrastructure toward AI evaluation and training-data intelligence.

The consistent theme is that frontier AI agents fail at real-world tasks at far higher rates than benchmarks imply — OSWorld 2.0 shows 20.6% completion on long-horizon computer-use tasks, Terminal-Bench 3.0 has Claude Opus 5 at 43.5%. Snorkel is building a position as the entity that measures this gap and, by extension, sells the training data and tooling to close it. Terminal-Bench becoming a 'continuous benchmark' suggests a product motion, not just research.

Expect Snorkel to productize Terminal-Bench and OSWorld-class evaluations as a paid eval-as-a-service offering, targeting enterprise AI teams that need to benchmark agents against real workflows before deployment.