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...
> 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.
How is this different than just telling the model you’re using to fill in the gaps itself?
If you’re not manually reviewing the intermediate hop, how does that add to the “understanding you”?
there is no difference... each case may be different, I have a local qwen gpu setup... and is fast... but not as fast as claude.
Fable is amazing and if it cost the same as my qwen setup I'll be running fable and never had qwen in first place.
But Fable is crazy expensive, Qwen is not as fast but cheaper... Haiku is fucking dumb IN MY HANDS... BUT my local qwen is quick enough to be briefed, get the idea and delegate.
I work with Fable mostly delegating to opus/sonnet and it's cheaper than just fable, I can't stand working with opus or the other models.
Why Qwen directing the others?
For the things claude refuse, Qwen can make very specific questions and still get an answer without triggering the thing.
I do the opposite of this, use Qwen3.8-27B as subagents, Opus 5.5 as orchestrator and planner, never implementer, and it cuts my token usage significantly. So it is using Claude to direct Qwen. Never thought of the other way around, will give this a try soon as possible.
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 had the same idea, I extended opencode-warden to use a local decision model to validate tool inputs and outputs. It seems to work... ok? d1 is fast enough to run locally on an old 1080 GTX. Not sure if I can recommend relying on it.
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.
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.
Yes × 1000...
Jev is the classic type of scientific discovery, any human can see it's effectiveness without actually trying it. We were only deprived the discovery because of the incredible noises created by the AGI/ASI ferver....
Seems to me that the important factor is that Jev has well-calibrated confidence for this task. Which secondary expensive model it defers to is probably less critical in this technique as long as that model has higher performance than Jev.
How is this different than just telling the model you’re using to fill in the gaps itself?
If you’re not manually reviewing the intermediate hop, how does that add to the “understanding you”?
Why Qwen directing the others? For the things claude refuse, Qwen can make very specific questions and still get an answer without triggering the thing.
GPT-6 Luna is a small LLM, not a decision model :P
Why give all the context to an agent, when the context can also be curated, updated and made concise automatically for each new problem.