The quota difference between the Gemini $99 Ultra subscription and the $199 version is absurd. I don't understand why Google is doing this, but the Pro subscription is essentially useless, and the Ultra version for $100 offers so much less quota. I really can't wrap my mind around that. They should at least tell the users it. Because, as far as I can see, they are not quantifying the actual quota differences specifically for Antigravity. I had two senior advisors, five implementers work on the codebase, and one QA agent using my entire platform for many hours to collect UX and UI issues. It started yesterday evening, after I upgraded to the $200 package, a decision that proved far superior. The $100 subscription would have exhausted its quota after just 1.5 days of the same work intensity, leaving me at only 16 % of the weekly quota. After upgrading, the agents worked all night in Argon on high, continued today, and ran an extensive QA session that simulated real users on my live production platform, creating accounts, receiving emails, and starting from scratch. I wrote a skill and a prompt for that QA agent, and I’m blown away by how well it works. It’s the first time I’ve had such an effective QA agent. The same prompt failed on the AutoClaw platform of GLM (which mimics Grok) and on AntiGravity with the Flash model (Flash 3.8 on high). In contrast, Argon completed a two‑hour perfect human‑QA simulation. I’m about to write a loop that integrates this process into my Constellation senior advisor/implementer, then runs a human QA tester. So far, only 8 % of the weekly quota has been used, and no cheaper model was involved. When I call sub‑agents, I force them to run Argon on high. In my view, the $200 Geminu Ultra plan is the best deal for coding. It outperforms Anthropic and OpenAI, offering the most reliable and capable coding model available. This level of autonomous testing matches what I’ve only seen from OpenAI’s Codex. It might also be due to the workflow harness that I'm using, the extensive agents.md file, and the local project memory that my agents.md instructions automatically create and update after every action. I saw yesterday someone publishing tips on working with Argon, and I saw there that it was specifically said that Argon loves instructions and also loves the task to test its work and to get detailed test descriptions. This would make it essentially the absolute opposite of the showboat models of OpenAI and Anthropic. My workflow harness combined with Argon might be a match in heaven. I can't quantify how many magnitudes better than anything that I've experienced since I started using AI for coding. I’m considering packaging all of this and putting it online, if I find the time.