We Burned 92% of Our Codex 20x Allowance in Three Days Using Astra Extra High
Five active users burned 92% of our weekly Codex 20x allowance in three days using Astra Extra High. Here is how it compared with Sol and why we switched back.
We selected the smartest model, pushed the reasoning effort to Extra High, and let it loose on normal development work. Three days later, 92% of our weekly Codex allowance was gone.
The setup was a ChatGPT Pro 20x plan used by five active people on the same login. Everyone was working for at least eight hours a day across project development, coding, PDF processing and 3D Blender MCP tasks. We used GPT-6 Astra with Extra High effort throughout, across both local and cloud Codex.
This was not a controlled benchmark. It was a heavy, unusual real-world setup, and the percentages below were manually observed from the weekly meter. I did not keep screenshots, token logs or identical task replays. Treat this as a practical field note, not laboratory evidence.
TL;DR
- Five active users ran Astra Extra High for three consecutive workdays.
- Our weekly allowance dropped from 100% to 8%, with both local and cloud Codex in the mix.
- With the same five users and broadly comparable work, Sol Extra High normally lasted seven days with roughly 30–40% remaining.
- Astra felt slightly better at coding and seemed to need fewer bug-fix passes, but that impression was subjective.
- I did not notice a clear advantage in our Blender MCP or other computer-use work.
- For normal development work, the quality gain did not justify the much faster allowance depletion. Sol Extra High is our default again.
The setup
This matters because the result only makes sense in the context of how hard the account was being used.
| Setting | What we used |
|---|---|
| Plan | ChatGPT Pro 20x |
| Users | Five active users on the same login |
| Daily activity | At least eight hours per person |
| Model | GPT-6 Astra |
| Reasoning effort | Extra High |
| Surfaces | Local and cloud Codex |
| Workloads | Project development, coding, Blender MCP and PDF processing |
| Measurement | Manual observations from the weekly percentage meter |
Scroll to compare all columns
A necessary disclosure: OpenAI categorises Pro as an individual plan, while Business and Enterprise are the multi-user offerings in its official Codex pricing documentation. Five people sharing one Pro login is therefore not a normal team configuration, and I am not recommending it. I am disclosing it because leaving that detail out would make the result look more scientific and broadly representative than it is.
Three days, 92% gone
| Day | Start | End | Used that day | Cumulative used |
|---|---|---|---|---|
| Day 1 | 100% | 85% | 15 percentage points | 15 percentage points |
| Day 2 | 85% | 55% | 30 percentage points | 45 percentage points |
| Day 3 | 55% | 8% | 47 percentage points | 92 percentage points |
Scroll to compare all columns
Technically, we did not fully exhaust the allowance. Eight percent remained. Practically, though, the weekly pool was almost finished after only three days. The average depletion was about 30.7 percentage points per day.
At the stated minimum of eight hours each, five users over three days represents at least 120 person-hours of activity. That is heavy usage, but it is also why the pace surprised us: the same group had been able to work through a full week on Sol Extra High without coming close to empty.
How it compared with Sol Extra High
Our previous baseline was GPT-5.6 Sol at Extra High effort. With the same five people and a broadly similar mix of development work, it usually carried us to day seven with around 30–40% of the weekly allowance still available.
| Model and effort | Observation period | Allowance used | Average per day |
|---|---|---|---|
| Astra Extra High | 3 days | 92% | About 30.7 percentage points |
| Sol Extra High | 7 days | About 60–70% | About 8.6–10 percentage points |
Scroll to compare all columns
On those rough observations, Astra depleted the weekly allowance around 3.1–3.6 times faster per day. That is useful for planning, but it is not a clean model benchmark: the tasks were not replayed identically, the meter readings were estimates, and I have no screenshots or raw token logs to audit.
Is Astra really more token-hungry?
Quota-hungry is the more accurate phrase for what we observed. A percentage meter is not a token counter, and we cannot reverse-engineer the weekly limit from three readings. OpenAI says usage varies with the model, task size and complexity, local or cloud execution, context size, reasoning effort, tool calls, retrieval and caching. MCP servers also add context and consume limits.
The clearest official comparison is OpenAI's token-credit table, where Astra is priced at 2.5 times Sol across input, cached input and output tokens.
| Model | Input | Cached input | Output |
|---|---|---|---|
| GPT-6 Astra | 250 credits | 25 credits | 1,250 credits |
| GPT-5.6 Sol | 100 credits | 10 credits | 500 credits |
Scroll to compare all columns
Those credit rates do not prove that the Pro weekly meter uses exactly the same calculation. They do show that Astra is positioned as the more expensive model. In our workload, long repository context, PDFs, MCP tools and extended sessions may have amplified that difference.
There is an important counterpoint. OpenAI's official Astra guide reports stronger results with fewer output tokens and lower estimated API cost in some evaluations. So I would not make the blanket claim that Astra always generates more tokens. I can only say our Codex allowance disappeared much faster when we used Astra Extra High this way.
Was the extra capability worth it?
For our normal coding work, no. Astra felt a little sharper and perhaps needed fewer bug-fix passes than Sol. But that is a feel based on experience, not a scored comparison, and I cannot point to one decisive task where Astra saved enough time to repay the difference in allowance.
OpenAI positions Astra as its most intelligent model, with particular strength in computer use, browsing, software engineering and multistep work across code, browsers and professional tools. That may be where the model earns its premium. See the official model guide for that positioning.
OpenAI also published a Blender architectural-visualisation case study in which Astra generated an editable scene through Blender's Python API, produced preview renders, refined the work and corrected details. We tried comparable Blender MCP work, but I did not notice a clear Astra advantage in our own sessions. That does not disprove the capability; it only means the premium did not show up clearly for us.
What I will use from now on
GPT-5.6 Sol Extra High is enough for our everyday work. That includes feature development, bug fixing, routine refactors, PDF processing and most MCP-assisted tasks. It gives us the coding quality we need without making the weekly allowance feel like a three-day sprint.
I would still reach for Astra Extra High when the task genuinely deserves the most expensive option:
- Architecture decisions with expensive downstream consequences.
- Ambiguous problems spanning several systems or tools.
- Complex computer-use work where navigation and visual reasoning matter.
- Tasks where Sol has already failed or keeps getting stuck.
- High-stakes reviews and long, multistep jobs where one missed issue costs more than the quota.
The practical conclusion
Astra may be more capable, and I can believe it wins on the hardest computer-use and cross-tool tasks. But the most capable model is not automatically the best default. For our mix of daily development work, Sol Extra High gives us most of the coding quality we care about and enough allowance to keep working through the week.
Use Astra when the cost of getting the answer wrong is greater than the cost of the quota. For everything else, Sol Extra High is enough.