Likely works even better with fireworks ai since they have proper grammar support

Of course it works, Jev is nothing but an API breakthrough

Jev is rumored to be a 30B model, and it's input price is MUCH cheaper than similarly sized models. The maker is also heavily focused on having a profitable product, so it's unlikely to be subsidizing the cost, especially since they say they have more demand than what they can serve.

You can get Gemma 4 26B A4B at the exact same token input price of $0.042/M. GPT-5 nano is not much more expensive at $0.05.

https://openrouter.ai/google/gemma-4-26b-a4b-it

Ah sweet it’s like Jev but several order of magnitude more expensive, and slower too.

Now this is how[0] we get some of the most magical Star Trek technology that eludes us to this day, such as automatic doors. Because if you notice, they work much, much better than real-life ones, because they seem to be doing something like this:

  if(within 10 meters of door then) {
    if(Jev(
       [A] Intends to go through, expects doors to open
       [B] Approaches with no intent to pass
       [C] Passing by, loiters, or otherwise
       [D] Other
    ) == most definitely A) {
      // open doors, +/- identity/security/interlocks check
    } else {
      // ignore
    }
  }

Keywords: ambient awareness, understanding of intent.

Most interactive tech on Star Trek is like this - from phasers to consoles to communicators to voice interactions with the ship's computer. The computer seems to be aware of the user and surrounding, and actively infers intent from context, to DWIM ("do what I mean") and when they mean it, instead of doing dumb things[1] on simple triggers.

--

[0] - The direction, not final implementation - surely we can work out how to do it more efficiently than wrapping around final stage of LLM. But the point is, multimodal.

[1] - Obviously it's a fictional show, but in this, both Watsonian and Doylist explanations align near-perfectly: this is/portrays advanced technology, that Just Works and doesn't do stupid shit. Same intent recognition algorithm is there - fictionally in the computer, in reality in the minds of on-set technicians.

How does the model get context to decide ABCD ?

Presumably this is much less good than Jev, because the normal LLM models have been trained with RLHF and to be agents. Especially on a large model, I'd expect it to decide in an earlier layer.

I'd hope whatever Jev's Reinforcement Learning for Calibrated Decisions (RLCD) does is better at training the models to give accurate probabilities in the weights.