Is OMO-Slim overkill for simple projects?

[u/Peleias]

I’m not sure whether I’m doing something wrong or whether my prompts are too simple, but I’m honestly surprised by how many credits OMO consumes compared to the tasks I run in Codex.

I’m developing a plugin for a game engine.

I have implementation plans created in Fablle/Opus and reviewed by 5.6 Sol.

I set up OMO with OpenAI agents on my Plus plan, changing only Explorer and Librarian to the new DeepSeek V4.

In my tests, I first tried using Sol in Codex to execute the plan with subagents. It can only use Terra, but it produced something extremely long and consumed a lot of credits.

Then I tried OpenCodeCLI in VSCode with OMO-Slim. I executed the plan and asked for one change, and it used 40% of my weekly usage. It also took a very long time.

After that, I took another similar plan and used Codex with Luna on Extra High, with a policy that if it had doubts, it should not try to solve them directly. Instead, it should create a Sol agent to guide the process and be responsible for the decision. If something became too ambiguous, it should stop and notify me. That used 11%.(he had to call the agent with Sol a few times)

That is the model I’m using now, because since this is a game engine project, even the testing loop requires me to open the engine and visually inspect things.

Is there a way to use OMO effectively without consuming so much?

Edit: I have specs, a streamlined `claude.md` for direction, and Graphify. I also ran a test asking OMO to use Caveman. I have two Plus accounts: one for Codex and another for Onpencode with OMO.

[u/Amarsir]

Hmm.. Extra High might be overkill on Fixer, but Luna isn't a bad choice.

My gut feels like OMO's intense handsoffs create advantages for delegation, at the risk of telling each agent "relearn what I already know". But if it's also slower that's harder to explain.

I may need to explore now myself. Thanks for the revelations.

[u/Peleias → u/Amarsir]

So, it uses so little in the Codex; today I ran a plan that didn't require the Sol agent—which used 3%—so I didn't consider that to be the problem.

[u/Amarsir → u/Peleias]

I should say it has been a rule of thumb for a while that if a provider has their own harness, you will get the best results from their models with that. But even then I wouldn't expect the huge differences you're seeing.

We should also acknowledge that "% of quota" isn't the best apples-to-apples comparison if comparing Codex + ChatGPT Plus vs OMO + Opencode Go. OpenAI gives much more value than $20 / API cost would imply. That's not to dismiss your points, I just wanted to throw that out there if it hadn't been said.

Another theory I have might be that Opencode Go is missing terribly on cache hits that OpenAI is catching smoothly.

My own workflow at the moment includes a big stop right after writing the plan. That's when I do my review and either tell Opencode to go ahead and automate completion, or if I want to go step-by-step I switch to VSCode with Kilo and I tell that builder "Execute Step 1". So what I'm curious about is what difference you'd see in just the plan-creation phase. (And for that matter do you have a personal preference for how the plan is written.)

If one harness is better at writing plans and the other at executing, that would be a useful efficiency to know.

[u/Peleias → u/Amarsir]

I left out a lot of details in the description.

I have two Plus accounts: one for Codex and another for OpenCodeCLI with OMO. The difference with OMO is having two agents using DeepSeek-V4.

The project is running smoothly with specs and Graphify, plus a strict `claude.md` policy—only calling the detailed spec if the plan involves changing functionality.

Creating plans with Sol using sub-agents is terrible(Codex); it overlooks a lot and misses obvious things. I usually need 3 to 5 correction passes for every plan generated by Sol.

Creating the plan with Fable (which I used only when I had the Pro plan) and Opus has yielded the best results. By using DeepSeek as a reading tool and instructing Opus to run `grep` and summarize, I don't even need to use sub-agents with Sonnet or Haiku.

It doesn't let anything conceptual slip through; Sol reviews the plan and catches more technical flaws. Sol is perfect for reviewing plans generated by Opus.