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xlayn | on: A Jev that can ask Opus for help gives Opus accuracy at 4x lower cost
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

drkleiner | parent | next
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.
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koolba | parent | next
> 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”?

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rao-v | parent | next
Oh man I’m been playing with little -> big vs big -> little sub-tasking a bunch.

My sense so far is it’s very much about problem domain “taste” and there is no one correct answer (that is token cost efficient).

Does the smartest agent need the full flavor of the context of the problem to make real progress (i.e what’s been tried, how you’ve influenced the approach) or does the smartest agent really need a crisp problem definition and context room to really work.

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ninininino | parent | next
Anthropic has a native solution to this which is that you can use `/advisor` to adjust config to enable using Sonnet but set Opus as an advisor or use Haiku and then set Sonnet or Opus as advisor that the weaker model will automatically call when it thinks something warrants it:

https://code.claude.com/docs/en/advisor

It allegedly will automatically judge when to call the stronger model, not sure how much instructions in prompts or AGENTS.md can steer it.

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Den_VR | parent | next
Thoughts on gemma?
jimbokun | parent | next
Add this to the things I never imagined when thinking about what a world with artificial intelligence might look like.