A Jev that can ask Opus for help gives Opus accuracy at 4x lower cost

Posted by theanomaly 4 hours ago

A Jev that can ask Opus for help gives Opus accuracy at 4x lower cost(hightouch.com)

291 points | 64 commentspage 2

jwpapi 34 minutes ago|

Isn’t that by nature a deterministic problem?

boutell 3 hours ago||

Ideally, "system one" intelligence is good both at quick reactions and at knowing when to hold off and wait for "system two." Sure enough, that works with Jev.

I don't usually get excited about the high-level, "whoooa man" marketing-speak, but the parallel is striking.

johnshahbazian 1 hour ago||

I got very good results from JevK5: https://github.com/allebee/jevk5

It's what I use locally. For my workload, it was right up there with Jev.

ltsSmitty 4 hours ago||

Liked the read. I wish there were some code examples for how jev/Luna were actually done as I try wrapping my head around their new paradigm.

intervolz 3 hours ago||

4x lower is so mathematically awkward.

markcerqueira 3 hours ago||

That reads fine to me as a native-ish English speaker. What would read less awkward for you?

mjuarez 3 hours ago||

Like a comment on another thread, 0.25x would be the much better way to say this. It's 25% of the original value. It's also consistent: If 3x means 300% the original value, then 0.25x is 25% of the original value.

If you say 4x lower, that changes the semantics for lower than x vs higher than x, which is absurd.

jcelerier 1 hour ago||

> If you say 4x lower, that changes the semantics for lower than x vs higher than x, which is absurd.

where does it say in the rulebook that human languages have to have perfectly symmetric pairs of words?

ant6n 3 hours ago||

So like, -3x?

timClicks 3 hours ago||

More like 0.25x.

dangoodmanUT 3 hours ago||

We've been doing this for a while. Fine tuning the decision model helps a lot too. The problem is you still need a pretty low confidence threshold (e.g 10%), but often times that reduces the work by 70% with 0% quality loss (in our benchmarks)

Jaxkr 3 hours ago||

> We tested whether fast, cheap decision models like Jev and GPT-6 Luna

GPT-6 Luna is a small LLM, not a decision model :P

rowanseymour 3 hours ago||

No but it's the only OpenAI model with a decisions API https://developers.openai.com/api/docs/guides/decisions

paxy 3 hours ago|||

Small LLM + no reasoning + fixed JSON-only output = decision model

ax0ar 3 hours ago||

There's actually a decision model version of GPT-6 Luna... You are right to be confused though. OpenAI and their never ending naming games. https://openrouter.ai/openai/gpt-6-luna-decisions

lukethorham 3 hours ago||

I wonder if something like this is the undisclosed new approach from Anthropic that presumably has massive improvements by combining multiple models. Leveraging decision models as much as possible seems logical for extra speed and token efficiency.

couldaidothis 3 hours ago||

I think OpenAI and Anthropic have both been doing similar "routing" approaches for a while. Wasn't OpenAI one of the first to have a small model start responding to you for the first several tokens so that you got an instant response while the big model became available?

Similarly, Grok has been routing to Opus internally for a little while now I think.

2001zhaozhao 3 hours ago|||

> undisclosed new approach from Anthropic

Any source on that?

pearcoder 3 hours ago||

they do have the 'advisor' model setting now which also plays well with this

hadlock 3 hours ago||

One of the most immediate and practical uses for AI for most (all?) businesses is CRM enrichment; this is a great article, thanks for sharing.

xfalcox 3 hours ago|

Been doing this on Discourse for Spam and locale detection, mostly doing d3-turbo with Qwen 3.8 Flash Next Help.

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