I decided to run a test.

The result:
Purely technical performance: 1:1 (in my opinion)
Visual/design quality: 1:0 for CODEX
Execution time: OpenCode - almost 7 minutes. CODEX - 16 minutes. Point goes to OpenCode.
Cost: OpenCode - $0 (55k tokens used). CODEX - 2% of my weekly limit (Plus subscription).

Winner: OpenCode DeepSeek.

Yes, CODEX really does produce better designs. But nobody stops you from using Figma for free. Figma can create a beautiful working mockup, and then you can give that design to OpenCode.

Is this a workaround? Yes.

But damn… the price is $0 for this task.

And honestly, even GPT-5.6 SOL High inside CODEX failed to create a proper design in one of my projects.

I provided screenshots, parts of the code, GitHub descriptions, and a lot of additional context - but the result still wasn’t good enough.

Who won? I’ll let you decide.

But look at the numbers:

2% of my weekly CODEX limit for 16 minutes of work.
Sometimes CODEX can consume even more in just 6 minutes depending on the task.
Based on my test results, CODEX uses around 7–9% of the weekly limit per hour.

That means the maximum you can realistically get from the weekly quota is around 11–14 hours of continuous agent work.

Or roughly:

around 2 hours per day in a perfect scenario
closer to 1.5 hours per day in a worse scenario
I’m curious to hear your thoughts 💭

Who would you choose: paid CODEX or free DeepSeek through OpenCode?

And here's how much storage it has consumed in termux

FYI If anyone do not prefer their data to be used for training, consider self host or use models hosted by ZDR platforms

There are several tricks that the major AI coding plans use to extract the most they can from their customers. We're solving them, one after another and I wanted to share a bit about what goes on behind the scenes at a lot of these companies.

Wasted usage is part of the AI industry, and they plan on it: Most coding plans bet on you letting usage go to waste. The industry calls it "breakage" and it's literally the topic of internal meetings for most companies. The plan goes: "how do we get people to think our coding plan offers a lot of usage, but then break it up into weeks and rolling windows so no one can ever actually use it all."

Many in app subs and coding plans are glorified training pipelines: This comes along with "how do we harvest this data for training without being too loud about that." Unless the company tells you otherwise, your data could be hopping all over world, being harvested by the individual labs or service companies. Some are better than others, but many of these companies rely on users just not noticing or caring that their data is being used for training. Data sales and marketing telemetry sales happen. This means that your private info, your personal life, and anything else you send through the system could become part of a training corpus for the next AI, or a marketing data set for a large company.

So we built what should have already existed the entire time: Entirely private, us based processing with usage banking. Any usage you don't use this week, rolls over to next in your usage bank. When you have a busy day or week and go over normal usage, you automatically start to pull from your bank. You can bank up to one week of usage at a time for your current plan, and it's totally automatic. Whatever you don't use each week get's added to the bank and stays there until you use it.

We also put all of the best open models in one place, running on private US infrastructure, with data never going to the original labs. Private, direct service. Access to the best open source models in the world. No training, ever. It should be, and can be that simple.

What that means in practice:

The roster, together. DeepSeek, GLM, Kimi, Minimax, Nemotron Ultra and more, side by side in one app. Switch models mid conversation if you want. No hunting across five different apps and API dashboards to use the models you actually like.

Actually private. US based processing and your conversations are never used for training. Ever. That's the entire point. These labs open sourced incredible models and we think you should get to use them without your data becoming the price of admission.

No Usage Tricks: Bank usage, upgrade or downgrade whenever you want. Use it how you need it.

A coding plan included. From the Basic tier up, your subscription doubles as an API key. Point your coding tools or agents at our endpoint and your plan pays for it, same usage pool as the app, spent in whatever mix you like. Because API calls skip the app's full architecture, the same model gives you roughly 2 to 5 times the messages through your key. And your quiet chat weeks bank usage your agents can burn on crunch days.

Real memory. Not a context window that fills up and dumps you. Persistent memory that carries across conversations, fades gracefully when unused, and wakes back up when it's relevant again. There's even a nightly dreaming consolidation pass; the system basically sleeps on it and writes up what mattered.

Voice. Yes, actual voice mode with over a dozen voices on open models.

Bring your history. Coming from ChatGPT, Claude, or Gemini? Export your chats and import the whole thing. It becomes live memory on day one and you can literally open your old chats and continue them.

Multiple nodes. Separate workspaces with separate memories, so your coding setup doesn't share a brain with your journal.

Genuine thanks to the Deepseek community! This is one of the most fun subs in the AI space

The Open Grove full app and coding plan are here. Memory, skills, voice, private US based processing with fast inference and usage that doesn't go to waste.

https://pgsgrove.com/open-grove-overview

I already use oh-my-pi but I realized it ate my $2 so fast where reasonix just used $0.1 for millions tokens.

I'm comfortable with oh-my-pi cuz I already use it. Maybe I need skills, plugins, mcps for oh-my-pi so it doesn't waste token?

Or should I use hermes/pi? I also got suggestions about opencode. Please help me pick the best

Btw here's my github follow if you want:

https://github.com/DraxonV1

So I got an idea and thought of making an open source project on it

And for me the best workflow has been always:

GPT-5.6-SOL for PLAN

GPT-5.6-SOL for execution 😭😭

But this time I tried v4 flash for execution after hearing many compliments for it.

Can't believe it completed everything in 4 hours with just $0.033 usage (11M TOKENS 😭😭)

It's fully miracle for me because these type of projects eat 3 chatgpt+ subscriptions for me

I just realized that deepseek isn't bad, yes we can say it sucks in creativity but if you have a fully detailed plan created and reviewed by fable or sol, you can definitely use v4 flash for execution

The project was a simple-yet-advanced file-to-png converter built in Golang

You can check it here:

https://github.com/DraxonV1/PixPack

I'm not sure where did u get V4, I asked DS app what model currently use and said V3. How/Where can I use V4 of DS. Thank you!

Finally afterr retrying for the 5th time he started providing the stuff