Fastino GLiNER2.5-Decide: A Small Model Built for Decisions

Not every AI problem requires a massive language model.

Fastino's GLiNER2.5-Decide is a good example.

The company has released a 340-million-parameter open-weight model designed specifically for structured decision-making. It can classify text, return probabilities and confidence scores, enforce constraints and extract structured information.

The model is available under the Apache 2.0 license and can run locally, including in air-gapped environments.

That last point is important for enterprise applications.

Consider a support system.

The task isn't necessarily to write a beautiful paragraph.

It might simply need to decide:

Billing issue? Yes.
Priority? High.
Escalation required? Yes.

Using a large generative model for every such decision can be expensive and unnecessarily slow.

A smaller specialized model can potentially handle these structured tasks much more efficiently.

Fastino reports a median latency of 167.3 milliseconds on a 48-vCPU Intel Xeon system for its short-document test configuration, with substantially lower latency on tested GPUs.

The model also performed strongly on Fastino's internal 17-dataset benchmark, where the company reports a 60.1% average score and leadership on 9 of the 17 datasets.

Those results are company-reported, so they should be interpreted as an internal benchmark rather than a universal ranking.

But the architectural idea is more important than the score.

AI systems don't always need one giant model.

A production architecture could use:

Large model → complex reasoning
Small model → classification
Rules engine → deterministic decisions
Human → high-impact exceptions

GLiNER2.5-Decide fits neatly into that model.

The future of enterprise AI may therefore be less about finding one model that does everything and more about using the smallest capable model for each job.