If you use Qwen3.8 27B to manage, to orchestrate, do do shallow research, and then make it ask claude you will notice that Qwen can get out of Haiku, Sonnet and Opus almost as good answers... I was researching how good the model asnswers where, I can't get to work with anything but Fable, but Qwen can get good work from any of them. My theory is that if you make Qwen understand you, Qwen can elaborate as much as needed to get any of those models get good results, because it's filling in details. So using Qwen to direct claude also get's you amazing results way way cheaper...

Update: let me elaborate more, I read the HN entry about the team of 5 Qwens being close to fable quality... so I embarked to add to my harness (https://github.com/alainnothere/privibe because we all have one right?) and once I had added the orchestrate tool I had to now measure somehow it it's able to improve quality, so I claude and then Qwen to run a experiment to call my harness passing the model to use and ask them to do something, 3 rounds I think and see what quality came out of it... and as per the grading... qwen and claude agreed that the quality of the response comming out the harness using the orchestrate was VERY CLOSE, with opus and fable providing more "in my opinion you should" which matches my experience on why they are better.

One more update: I can't get copilot models to work decently, it may be my way of working... but I can get Qwen to follow what I try to do... if Qwen get's me he can then fire copilot cli and get amazing results by using copilot... bonus points seems like copilot cli is cheaper with the AIC (cost of the requests) so you can make the copilot subscription longer

Jev is pretty cool for augmenting llms in a lot of ways. We use it in our own agent orchestrator[1] to help route ideas/prompts to running agent sessions. I find when I have a lot of parallel agents running its hard to reason about which agent I should be talking to and Jev makes that call for me.

[1] https://github.com/codealong/codealong

This is very cool. But also, it’s a benchmark/result for a very specific experiment/task: identity resolution

Despite the title, the article is not making claims of, or demonstrating general usage improvements or general coding agent improvements

Found similar results, but the economics of the models involved have changed so quickly that it becomes so task specific on what the right thing to do was.

With an assemblage of models like Jev + Opus or Jev + Qwen, Jev would often do the right thing when deciding to escalate to another model - I was impressed that it would usually make the right call, especially if you gave it good awareness of what the other model capabilities were. "ESCALATE to opus if you need good vision awareness".

When Haiku 5.5 came out though, for my tasks, the cost differences disappeared almost immediately. Haiku was good enough / fast enough for so many things that introducing a decider layer didn't offer huge wins. Jev's "millisecond" level performance was nice but would get dwarfed by other latencies in my chain ( web browsing ), that it didn't make a measurable difference in my tasks.

Where is the Jev that can replace 20 Opus sessions asking each other for help in a coordinated way? I want to shift towards a local model that "asks for help" in an effective way, but it still seems a bit out of reach. I mean, you could build your own 27B or 35B or A3B or bumblebee and spend a week or two carefully forming it into something useful, but it feels like that would be too specific and it would be thrown out when something like qwen4 is released. Maybe not, I don't know. Opus still exists as it is, in a "subsidized" form that's hard to step down from.

This sort of stuff has me believing that decision model controlled/prioritized context is the future of context management beyond the raw AGENTS.md file.

Why give all the context to an agent, when the context can also be curated, updated and made concise automatically for each new problem.

Excellent read. I use the same hybrid approach for a lot of security engineering work and it’s working well after initial calibrations, for example for our own harness we rebuilt auto mode exactly like this to judge tool calls and intent.

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.

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.

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.

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)

> 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

4x lower is so mathematically awkward.

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.

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.

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

What about Sonnet? Haiku? Does it need Opus specifically?

Jev is interesting because it adds "muscle memory" to LLMs. I think we're approaching some fusion of systems that approaches the human brain here.

i'm so numb to these headlines nowadays... i guess i've been disappointed by how effective these approaches end up being for actual work but i feel like it's easy to produce these headlines with "proof" that doesnt prove anything besides the sandboxed scenario it was run in

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