LEWES, DE, December 21, 2025 (EZ Newswire) -- While AI companies compete to build ever-larger language models, one startup is proving smaller can be better — and vastly cheaper.

Particula Tech has released a suite of specialized AI models, each under 7 billion parameters, that outperform general-purpose large language models on specific business tasks while costing up to 97% less per operation.

The company's flagship model, Particula-JSON, achieves 99.8% accuracy on structured data extraction at $0.03 per million tokens. Comparable tasks using OpenAI or Anthropic cost up to $600 per million tokens, depending on configuration and model.

"Most businesses don't need a model that can write essays and generate code and answer trivia," said Sebastian Mondragon, Particula Tech's CEO. "They need a model that extracts invoice data perfectly, every time, for pennies."

The compressed models address a growing enterprise concern: AI costs that scale faster than value. As companies move from pilot projects to production deployments, per-operation costs become critical.

A logistics company processing 10 million documents monthly would spend $750,000 annually using a standard LLM API at $75 per million tokens. Particula's specialized model cuts that to $22,500 — a 97% reduction for the same task.

The trade-off is versatility. While ChatGPT handles any text task, Particula-JSON only extracts structured data. Particula-Classify only categorizes text. Particula-Code only generates code.

But for businesses with defined use cases, that limitation is the advantage. Smaller models run faster, require less hardware, and can be deployed on-premise without cloud dependencies.

"We've had clients switch from 70-billion parameter models to our 7-billion parameter alternatives and see accuracy go up," added Mondragon. "Task-specific training beats general capability for production workloads."