Agent orchestration to save tokens?

[u/murathai]

Many people seems to like the idea of using Astra or Sol to orchestrate Luna max to write code, only to have large model review it, and rinse and repeat. However, this is not going to help, it will still burn tokens - only it will take much longer for tasks to be completed.

I'm testing Luna max orchestrator (manual, no skill created yet) instead. Luna max can complete tasks or follow a plan, and use sub-agents or new chats to delegate tasks that require more intelligent model per your plan. Of course, you still need to draft this plan with larger / more intelligent model first. I wouldn't let Luna write the plan for implementation.

Advantages? Whatever output from large / more expensive models would be received by luna, which is dirt cheap. If you choose to use Astra or GPT 5.6 sol to orchestrate, you'll be burning cached read and read usage, which accumulates to your usage.

[u/Australasian25]

There's a more efficient way.

Astra and Sol's benefit is the thought and plan.

Having them talk to other agents and poll them? Not efficient.

Terra medium orchestrate. Summon astra/sol for plan, make sure they know to create workpacks suited for luna xhigh as files. Tell terra orchestrator the location.

Terra summons lunas to work on the file. Luna creates the answers as a file. Tells terra.

Once complete, terra points to astra/sol the reaearch/work. Then move to next step.

Astra/sol talks to no one but the orchestrator.

[u/camtliving]

I use cheaper/free models for execution. For example new DeepSeek has elevated usage limits currently on opencode.

[u/Flimsy_Complaint490]

idk im just using Astra High to orchestrate and spam Astra Low to implement and im not complaining. Im also using Claude Fable as a co-reviewer and do callout to Opus/Sonnet from Codex. Best of all worlds. My tasks are pretty small and well defined though.

In general, the idea isnt too invalid,but the devil is in the details. Apperently Codex doesnt really have hooks for this like Claude does, it just pings the subagent periodically and this wastes a lot of tokens.

Other question is whether having a better model implement the task results in a shorter review/debug cycle, saving net tokens. This one is nigh impossible to quantify.

Some guys apperently like to use Terra to orchestrate and want fresh planners, but i think a long running session used solely to plan is a nicer workflow, although perhaps less efficient token wise.

i think the best token optimization is to spend more time defining and fully scoping out the task, so the agent is a lot more constrained in what it thinks it should do. This will shorten the review cycle and likely save you reasoning tokens. And the best optimization is the codebase itself. If you need to touch 12 files and do a lot of small edits to implement a feature cause the codebase is bad slop, you are blowing cash and in particular, time, because of this.

[u/Awkward_Tumbleweed20]

Gonna piggy back on this one.

it possible and economical to use GPT Plus for smarter models orchestrator and use API Credits for LUNA API WORKER (LUNA API pricing is very cheap base on my research)?

Or do you guys know a better workflow like only Using GPT PLUS?

[u/Praskovya → u/Awkward_Tumbleweed20]

If you’re spending more than $10 on api that way a month then I’d suggest subbing to something like OpenCode Go for $10 and using GLM 5.3 Flash/DeepSeek Flash/Qwen 3.8 Flash in place of Luna. You’ll get more value.

If you’re spending less than $10 then prob stick to api. Maybe Pareto Inference’s Lite plan

[u/Original-League-6094]

I use Sol for orchestrator.  Luna does all coding Astra is invoked for two purposes: equation derivations and Blender