I think it's a bit early to call the race.

I think the abstraction is a useful one - a general purpose classifier that does not need to be specifically trained: Text in, structured judgement out - with a focus on speed and cost efficiency.

And since there is not yet a large body of benchmarks yet, I don't think have sufficiently explored how to measure these things.

But since there's a market and some hype this will soon happen.

(And it's not like TypesafeAI has a real moat or invented something entirely new here ~ they just managed to put things into one coherent perspecive)

That was a pretty simple task they gave it, and sure you can use BERT with sequence classification for simple classification tasks.

In our benchmarks, Jev did a LOT better at multi-step reasoning tasks than any open decision model we have tested so far, and it was also better than GLiDE which was specifically designed for that kind of task. And also better than Luna. On accuracy and also confidence calibration but also time and cost.

https://hard-decisions.anth.us/models/

Traditional classifier isn’t a direct equivalent though. Jev has some light abstraction/reasoning ability.

eg feed it a weather forecast and ask it whether I need an umbrella. It’s smart enough to make the connection between rain and umbrella.

So somewhere between classifier and fat LLM.

Ultimately boils down to right tool for the job

duh, this is not news. (general, fast and cheap) before decision models, you could pick only 2.

LLM as judges - generalized, but too slow. If you had to make millions of classifications a day, this will be the wrong approach. you won't/shouldn't use LLM to classify spam/no spam. hot dog/or something.

traditional classifiers, very specific 1 trick pony, super fast and cheap once built. If you need to make tons and tons of classifications, this would be the approach. but if you wanted a classifier right now for a novel problem, you need an expert to curate data, train and deploy.

decision models/jev - are generic, you can throw them at most generic classification problems, and they are good enough. it's a fine balance between general, fast and cheap. you get all 3

The key properties for using such models for conditional-branching decisions in agentic stacks (and related) is that they should be well-calibrated (under the definition chosen for the task) and informative (e.g., always predicting the mean might be "well-calibrated" in a theoretical sense for some chosen quantities of interest, but isn't particularly useful in practice).

The tricky thing with the neural networks is that the output logits are in effect a highly lossy compression of the epistemic (reducible) uncertainty, so even if the target calibration quantity is well-specified, it can be difficult to obtain in practice. A side-effect of this is that estimates in the high probability regions are not particularly stable under even modest co-variate shifts, which is a real problem if the estimates are being used for decision-making in a multi-step search graph that can lead to branches that are unlike what the model/estimator saw at training/calibration (if not altogether out-of-distribution). Here are a couple papers that describe how to approach those challenges:

[1] Similarity-Distance-Magnitude Activations. In Findings of the Association for Computational Linguistics: ACL 2026, pages 22037–22057, San Diego, California, United States. Association for Computational Linguistics.

[2] Introspectable, Updatable, and Uncertainty-aware Classification of Language Model Instruction-following. In Proceedings of the ACM Conference on AI and Agentic Systems (CAIS '26). Association for Computing Machinery, New York, NY, USA, 1259--1269.

There are plenty of benchmarks that show they do, too, though, so this is a single data point.

BART is quite an old model for this kind of test, and probably not a good very good choice for much these days. I'm working on replicating their benchmark on my own NLI model that targets zero-shot guardrail applications. I don't think it'll beat much larger models, but should give a better baseline for what a crossencoder can do.

https://huggingface.co/dleemiller/crossingguard-nli-l

Shouldn't LLMs intuitively be better with a high number of available options? This article only does simple prompts with only options to block or not block.

What is openai doing for their decisions API, a finetuned luna?

prompts that these evaluations were done are too trivial

The block/allow framing is the part I'd push on. I had a batch where automated checks passed all 99 outputs and reading each one by hand found 8 broken. The scorer only catches the failure modes its rubric already names, and a two-option guardrail bench inherits that ceiling whichever model sits behind it.