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

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.

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.

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.

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.

4x lower is so mathematically awkward.

> 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

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.