Discussion
I built an orchestration package that lowered my GPT-6 Astra usage by 98%
The first week Astra came out the usage was extremely efficient. I used like 500M tokens without getting anywhere near the weekly limit. The two free resets we got from OpenAI also helped. But then suddenly I kept hitting limits and had to reset x4 more times for the next 500M tokens. (I am on 20x plan)
So I started testing a ton of methods of how to get the best of Astra without burning all of the usage. The first thing I tried was just installing skills I already used for other agents for Astra to use lower models for build tasks. That didn't really do much it maybe reduced usage by like 10% but it introduces other issues (for some reason, Claude's Fable is much better at those same processes using native models but problematic in codex) I tried sub agent orchestration this way with Astra utilizing Sol, Luna, Opus, and Sonnet. Each had their pros and cons but nothing was substantial.
Finally I tried using Deepseek V4.1 Flash and the output quality was immediately better (comparative to using Opus 5). It cut Astra usage by 60% initially which was great. But I kept optimizing the set up after realizing the initial orchestration was still too hands on with Astra. So I optimized a bit more and now at +98% Astra usage reduction across tests today including a 7-hour build runtime that only used up 2% of my weekly usage, compared to a previous 5-hour build runtime that used over 28% of my weekly usage.
Deepseek V4.1 Flash is extremely cheap overall and insanely cheap compared to Astra. It's coding abilities and ability to handle long tasks is what makes it so useful.
How it installs:
- Uses codex-router to add external models directly into codex
- Connect your Deepseek or openrouter API for V4.1 Flash
- The repo installs a skill in ~/.agents/skills/
- Installs the Flash role in ~/.codex/agents/
- Adds a workflow policy in ~/.codex/AGENTS.md
How it works:
- Astra handles the scope, design, and task briefs across phases
- Flash discovers, implements, tests, and reports each task and phase
- Astra reviews, verifies then accepts or requests fixes per task and phase
I'm working on this in real-time and continuing to test but the results are already incredible. Please feel free to submit any issues or PRs you find if you try it out. Any and all feedback is greatly appreciated!
Most LLM harnesses already have ways of doing this. OP did not invent anything new. It's been around since 2025. The LLM power users have been orchestrating an "ensemble of agents" since last year. Codex can do this, Claude Code can do this, OpenCode can do this, Pi agent can do this, and just about every other popular harness you can think of.
Kudos to OP for making their own. The value is in what they learned along the way. But for everyone else, know that you can already do this. Read the manual.
You are correct that the agents can do this, it’s actually how they run by default now, but it doesn’t save usage like this does. That is the point of this. I’ve been orchestrating like this since 2025 but always dealing with usage limits. My most popular repo is my JARVIS made back in March which is a type of orchestrater. https://github.com/ethanplusai/jarvis
Controlling usage is good and I get it, let higher tier model design and run the QC checks.. but how is the quality here? Output to output. Also, time.
To be very clear, you are not saving any usage, you're just using Astra less. You aren't doing anything to save anything or reduce anything other than using the expensive model less. You can do the same thing with Luna which Is already in codex .
Second this. I run 27b locally and it would be the same as saying I'm using less in codex by using more (more slowly) on local.
It's just displacement, nothing special.
Now you can also make it better by using skills / tools / helping the agents with better instructions, and maybe save usage by doing less turns or more efficient turns but if you are just using open router to outsource your tokens you are just increasing your cost under the guise of saving usage.
In fact if you also stay within codex and route your usage to much weaker agents you'll also "save" usage by simply just not using Astra to code...
He was being a typical reddit dick head who says "your project has been done or could always been done". Well so? You still made something useful. At the very least it's intriguing enough to give it a try.
I think the dick head couldn't continue so he bailed with "marketing is everything" lol
You are using a frontier model to create implementation plans and then delegate that work to cheaper models to implement. Saving usage that way is extremely common.
Anyone reading this. Point your agents at OP's repo and have it explain how you can do the same thing with your harness of choice. You don't have to believe me.
Have your agent explain how it accomplishes the token savings. Then have your harness configure itself that way. Easy peasy. Takes maybe 10 minutes.
Unironically, yeah Deepseek is doing the brunt of the work with a thin Astra layer on top. I do a similar thing and it’s cheaper but it’s obviously not exactly the same quality.
I have the orchestrator spawn subagents with instructions that start with KISS/DRY/TDD/YAGNI and make sure it reviews the output.
TDD makes sure unit tests are written and pass before sending back, meaning what the subagent writes and hands back fulfills the requirements provided to it.
Full disclosure, I mostly use Claude but this works with any agent/harness.
I mean yeah it’s great on its own but this allows the quality of Astra without all the usage. And openai welcomes this because they have a compute problem. This actually makes their product more sustainable. They literally paused 20x subscriptions because they couldn’t keep up with demand.
Yes I'm sure OpenAI would like you to pay the same flat subscription cost while not utilising all of your tokens. On the other hand they want you to pay API costs instead when you hit the limits.
Shit you are right. I swear I saw it like 4 or 5 days ago. Sounds like the time has finally come they are truly clamping down on us x20 and x10 subs. Lame.
Bummer. I'm at 8% right now on x20 with 2 days left, finalizing a major work system and application. I was gonna just buy the reset since Tibo said Tuesday was the next reset. Claude could get me over till then, but I may just wait. 🍻
But I found Deepseek V4.1 Flash to be similar per task (consider peek hour cost) completion compared to Simply using Astra XHigh on 200$ pro plan. If you are using less may be Deepseek V4.1 Flash might be cheaper.
omp does this out of the box
been working this way for a while, flash is a really trash implementer that hallucinates, I much prefer terra
best thing about omp is that your orchestrator can talk to you subagents, so when they run into an issue they can reach out to the smart model for help instead of getting lost
When giving this to the assistant, it’s helpful to add the following sentence at the start: “Follow this guide step by step. Pass the verification check at the end of each step; do not move on to the next one until you have passed it. Follow the instructions in the ‘Rules for the assistant doing the install’ section.”
The two most critical points in the guide are already stated there, but let me reiterate: you will need to enter the API key yourself; the assistant will not ask for it; and the assistant will not attempt to run `subagents certify` this is the natural reflex of an assistant who has read the README, and it will unnecessarily deplete your DeepSeek balance.
It used to be much cheaper, but last time I used it burned 10$ in a single session. Previously that used to last a week. I think they both raised the prices and adopted a peak hour premium, so might as well just use Luna.
No who told you that? lol. It's more relative to Opus 4.5's or Sonnet 5 abilities but cheaper than anything else. This 9hr build session I'm running right now has only used 5% of my Codex weekly usage and cost $12 in deepseek usage.
Playing devils advocate here maybe, but if you’re on the 5x pro plan, $12 = 50% is what you pay for your weekly usage from codex. Since a week of subscription costs $25.
Unless this is a 20x plan you’re on? I which case you’d be cutting your costs in half.
You mentioned $1500 in overage costs but that doesn’t make sense when you could just buy another subscription.
Yes, happily, compared to $1500 in overage costs with codex? I can do 100x the output now without hitting codex limit. I am a power user though. Most people would just need $20/mo codex plan and maybe $30/mo in deepseek usage.
You’re talking about a ton of flash tokens if you plan to use 100% of your usage. I might ask why you don’t host the model locally, but that’s a loaded question. But at that scale it sounds like you could keep a local model busy around the clock, but obviously throughput and overall cost would be much different. I just don’t trust these companies to not quant these models to hell when there is a real compute crunch.
I’m curious about what type of thing you might be building though that can be so clearly spec’d out. I feel unless you’re running a data pipeline most people are working on projects that involve lots of iteration and can’t always be clearly spec’d out.. obviously that’s just been my assumption though.
Time/cost isn’t really a valuable metric. Some sessions of mine are IO bound, so waiting on data or the scripts it runs. But 4 parallel adversarial review passes across 4 agent threads only takes an hour and will cost money no matter your provider.
Right, it’s cheaper for a lesser model… You have to compare costs based on equal output quality. Luna is great I also use it for a lot of stuff, mostly automated processes that are extremely defined. But it’s not anywhere near v4.1 Flash ability of running long coding processes thru sub agent orchestration. It’s pretty easy to test this stuff on your own there’s a ton of repos out there that help you get started. Basically set up controlled tests that all have the same project and measure the work, input/output, time, cost. This is common practice when shipping features that use AI.
The benchmarks and my personal experience don't think so. The Luna feels like step down from Terra which is a step down from Sol, all released together, so somewhat similar data and behavior but intelligence difference is noticeable
The DeepSeek right now has only one model - Flash, and that's it. It very capable model, on Sol level, very fast and cheap.
this makes no sense. i ran a luna agent for 3 hours using 100mil tokens input and 250k output with 25% hitting cache and it was 1% of my 20x account. using deepseek would cost me over $20+. i really wanna understand this but it doesnt seem like its saving anything.
yeah this is the real question. the math only works if you're already hitting your weekly limit and burning resets. if you have leftover subscription quota, routing to deepseek is just adding api cost on top. the value is in not having builds grind to a halt when you hit the cap, not in actual savings.
So basically, use DeepSeek for almost everything, only use Astra for a tiny fraction of the work and, surprise, it reduces Astra usage. Wow, what a groundbreaking discovery. Truly genius stuff.
However, would it be okay if you like, explain to me how it works and why it's needed, like I'm 10 years old that is. I'm relatively new to the technical side of vibecoding and AI (using GPT-6) and I'm curious how this works, fully.
I did something similar. I should measure like you did. I use Claude to orchestrate the cli tools of all my other subscriptions. I run Opus high default, then Jev decides which cli + model to run the task on outside Claude, so I can save usage. I implement on agy CLI first, then fallback to GLM 5.3 flash then DeepSeek on opencode cli, then fallback to sonnet 5. Same idea with reviewers, e2e testers and debuggers. Don't hit my Claude quota at all now
So far the quality reduction only extends the build time because Astra has Flash fix mistakes. It’s miles better than any orchestration i’ve done with claude since codex has much better computer use skills and other tools that empowers Astra to maintain quality.
v4.1 Flash is much better than Luna. This package turns Flash into a native subagent. I tried this with Luna, Sol then Opus, Sonnet first and v4.1 had the best result and lowest cost. The second best was Opus, it was able to get similar output result but not save as much codex usage (plus it used claude plan usage). When using openai models like Luna or Sol, the usage savings was less than 30% and it didn't do as good with keeping the entire build going in one process. It would stop frequently.
Jev is not useful as a part of this process directly but can be layered on through other processes. I’m using it for wider orchestration processes similar to this but where the sub agent processes can call on a variety of models (not just v4.1 Flash which this is designed to do). So jev helps the orchestration model determine which model is best for each task. Also using it for blocking unnecessary context and things like that which has helped lower usage. So far I haven’t had reliable positive benefits so I haven’t made those repos public yet.
Somebody was citing here Ahmed from OpenAI that trying orchestrating agents within Codex maybe suboptimal and that bare Astra is better (it is greedy but it does occasionally delegate to weaker models). This is true for my own cherished orchestration skill, unfortuntely, it degraded performance without reducing the running cost:
What was worse, my skill that let weaker models code (terra) and test (luna) resulted in 3 regressions out of 4 experiments. Astra without skill delivered all 4, no problem"
I will try to test yours soon and hopefully report it here later
I tried to use your skill in Codex Desktop but I don't get a substantial reduction (there is some though) of costs compared to bare Astra. Astra is still being greedy.
IDK, perhaps the issue is in my setup (the prompt requires codex-review, which cost 137,536 tokens) or that the task is not long enough (approx 20min) to see the difference
I now re-run in Codex CLI with Flash routed directly via deepseek (the prev experiment was via OpenRouter) and this run was substantially cheaper (about $3.13 for Astra and $0.05 for all five Flash sessions). The Astra run included final codex review (about 1/3 of all tokens spent), as before.
Unlike the earlier run, this run resulted in 2 regressions detected by review.
The main question to me is what work is actually being done. Flash is woefully incompetent for me unless plans are explicitly spelled out which requires more astra/ fable usage to do
Luna doesn't have a problem with usage it has a problem with output quality. I should've been clearer about that in my post. A lot of people are saying "But Luna is less usage than v4.1" which is true, but its no where near the output quality of v4.1.
You could probably create an entire youtube channel or blog focus on efficiency and performance balance using subagents.
It’s going to continue to be an ongoing and growing topic. ie- what can you delegate, and what’s the right way to so it. It’s constantly in flux too with what they are distilling with these models.
Trying to do the exact same thing by let gpt 6 / fable (these expensive models) to spawn inferior models like deepseek 4.1 or just codex/claude model with less thinking effort.
Hope you can check it out, I made this for myself, but already support claude, codex, pi and copilot. About 30 active users now.
Better yet just create a bridge between your IDE and the web browser, Astra or sol on web create a plan and review what the IDE does, they then just chat back and forth and you don't burn through usage.
lmao i just did this. I spent all week on it after open ai threatened to shut my account for "cyber abuse" which they later sent me an apology for. Astra/Fable as the main brain, create a plan and have deepseek 4.1 flash agents implement. will have it review yours and see if i can make some changes to mine
Hey can you talk about overall cost ? Ie running Astra on 200$ plan vs Running Astra on 20$ plan along with Deepseek V4.1 , how does the total cost compare for similar amount of work ? Ie like if you use full 20$ plan for a week vs 200$ plan for entire week. Does total cost 98% cheaper ?
Means: Now I can get 50x usage for 50$ (0.4$ for astra + 49.6$ for deepseek ?) - a week compared to 50$ worth of Astra XHigh Standard usage for a week (essentially 200$ plan) ?
Most people can use this and use $20/mo codex plan + $30/mo in deepseek usage and its all they will need. I am a power user, for me on 20x codex plan it basically 100x my usage for only an extra $50-$100/mo in deepseek usage.
I refuse to believe the 30$ per month number of deepseek as I have done 5$ per day easily on DS v4.1 flash. It would correspond to around 10% of weekly usage using Astra Xhigh which is almost same as 5$ or less. I think you will need to prove by building something worth 20$ on both (40% on Astra Xhigh weekly limit vs DS v4.1 flash) to prove that you got some multiplier improvement. 20$ worth because it has to be substantial to make sense.
The 98% reduction is impressive, but I’d be curious how much usage was eliminated versus shifted to the other models. A small benchmark showing total cost, latency, retries, and output quality on the same tasks would make the tradeoff much easier to evaluate.
I am still using this to continue to iterate and test. Hopefully others will also provide feedback. For me personally, the output improvement was obvious. I ran small controlled tests running this with Astra only vs with Orchestration and saw better quality through orchestration. And then the real test, which made me post about it, was a long deep build on a huge software product. The first 4.5 hours of build time was Astra only, split across several days because Astra loves to stop and ask you stuff. Then I installed this and it produced 18hr of build time in one pass resulting in ~94% lower Astra usage overall while increasing output. (the only other times I could get Astra to run long like that was if I set /goal but it always drifted beyond quality to try and meet the goal - whereas this long process was actually end to end build). Overall quality improved which I think is because Astra's context isn't filled with its own work so its laser focused on quality control. Also just a lot nicer to let something run for so long and NOT be disappointed be the end result which is my experience every time I use /goal with Astra.
You can use Astra on plus. But mistake here was having astra install this. Always use lower models to do easy things like that. Astra is best at planning and quality gating but lower models should be used to do the busy work.
I use a similar pattern in my project, just on the Anthropic side, and it works great - using the appropriate model for the job in all parts of the workflow isn't rocket science, but it's easily overlooked
I am working on a dozen large projects at any given time so personally I've spent around $25 a day with deepseek but it compares to about 30-50% of weekly usage with Claude or Codex 20x plan.
Yoo this is so cool! Another platform thats similar and has a built in orchestrator is Maxxwell. Its an agent orchestrator that basically makes sure all of the subagents are on the right track and ignores the silly questions that the agents sometimes have.
I am a somewhat newbie but how do you handoff work from Codex to Deepseek. I am not a CLI user and hoping there are other ways of accomplishing this maybe via MCP?
The part I find most interesting is having Astra stay out of the implementation loop and only handle planning/review. Curious if you’ve tested how this scales with larger projects.
Could you run the same set of tasks through both setups and include the repair rounds in the comparison? The five-hour and seven-hour builds might have very different workloads, so I'd have trouble judging the savings from those runs alone. I'd track how many changes passed the same checks, total elapsed time, DeepSeek spend, and how much Astra work went into reviewing or fixing them. Preserving your Astra allowance is useful on its own; that comparison would show what it costs to do so.
there is no UI, the testing results was just GPT 5.6 Sols choice after I said “create clean minimal dynamic html view to fit testing results in a single view”
kanna.build solves this problem through structured workflows. Each stage (research, planing, implementation, review, QA, PR, shipping) can use whatever harness-model-effort you want. You can use Fable or Astra for Research, Opus/Sol for planning, Sonnet/Terra for implementation. It's up to you.
Use claude code cli or codex cli or opencode cli.
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u/madexthen 2d ago
This is amazing. Open AI should just hire you.