We found an approach to get Jev-like properties from standard LLMs like GLM-5.3-Flash.
The core idea is to craft the input prompt so that the first output token answers the question. This makes it possible to get a decision with a single forward pass.
In the blog post, we describe the approach in detail for GLM-5.3-Flash and vLLM. We benchmark this setup against Jev and Laya. We find that our setup is on-par with Jev in terms of accuracy and speed and that it substantially outperforms Laya.
Still, in terms of costs per decision, Jev is several x better than our setup. In turn, our setup supports vision inputs.
There's some speculation that Jev is essentially an open weight model with novel post-training (RLCD). So, if these folks have competitive accuracy with just the base model, it could raise some questions about the necessity of Jev's architecture. You generally don't want to find yourself competing only on price.
The core idea is to craft the input prompt so that the first output token answers the question. This makes it possible to get a decision with a single forward pass.
In the blog post, we describe the approach in detail for GLM-5.3-Flash and vLLM. We benchmark this setup against Jev and Laya. We find that our setup is on-par with Jev in terms of accuracy and speed and that it substantially outperforms Laya.
Still, in terms of costs per decision, Jev is several x better than our setup. In turn, our setup supports vision inputs.
My question is why not use Jev instead? It's faster and cheaper.
These questions are answered by the OP (Same speed, image support) - additionally, GLM is open weight.
There's some speculation that Jev is essentially an open weight model with novel post-training (RLCD). So, if these folks have competitive accuracy with just the base model, it could raise some questions about the necessity of Jev's architecture. You generally don't want to find yourself competing only on price.
Fyi, I haven't tested this yet.