Astra’s Token Burn Is Unsustainable
I’ve been testing GPT-6 Astra in Codex against the type of long-running repository work I normally perform with GPT-5.6 Sol, and the difference in usage consumption is extreme enough that Astra is currently impractical for my workflow.
Test Configuration / Observed Results
• Model: GPT-6 Astra
• Reasoning effort: Medium
• Workload: Standard software-development task in an existing project repository
• Concurrent Codex activity: None
• Runtime: Approximately 40 minutes
• Files affected: 12 Python files
• Code created or modified: 758 lines
• Code deleted: 46 lines
• Weekly allowance before run: Approximately 87% remaining
• Weekly allowance after run: Approximately 47% remaining
• Weekly allowance consumed: Approximately 40 percentage points during a single run
I ran Astra against one of my standard project repositories. Before starting the task, my Codex weekly allowance showed approximately 87% remaining. Nothing else was running against the allowance.
The Astra task ran for approximately 40 minutes.
During that run, Astra modified or created 758 lines of code across 12 Python files and deleted 46 lines of code.
When the task completed, my weekly Codex allowance had fallen from approximately 87% to 47%.
That means a single approximately 40-minute Astra run consumed roughly 40 percentage points of my entire weekly Codex allowance.
The task produced useful work. The issue isn’t whether Astra can perform the work. The issue is the amount of weekly capacity required to produce it.
For comparison, I routinely perform this same general class of repository and software-engineering work using GPT-5.6 Sol. Sol can work continuously for extended periods and typically moves my weekly usage meter by roughly 2 to 3 percentage point per hour. It may take somewhat longer to complete a difficult task, but it gets the work done without making sustained use of Codex impractical.
I don’t question that Astra is a more capable model according to OpenAI’s evaluations. The relevant metric for my workflow, however, is useful completed work per unit of available capacity.
Astra produced approximately 758 new or modified lines across 12 Python files, with 46 lines deleted, in roughly 40 minutes. That’s certainly productive. But consuming approximately 40% of a weekly allowance to accomplish it makes that productivity difficult to use in practice.
At that rate, I could potentially perform only a few hours of serious Astra work before exhausting a resource that takes a week to replenish.
I initially wondered whether my experience was unusual, so I searched for other reports. It isn’t isolated.
Other Codex users are reporting very rapid Astra allowance depletion, including users exhausting available capacity within approximately 20 minutes, consuming substantial portions of Pro allowances in a single session, and switching back to GPT-5.6 Sol because Astra was consuming their allowance too quickly. There are now multiple reports in the OpenAI Codex GitHub issue tracker concerning Astra usage limits, quota depletion, and discrepancies between apparent token activity and allowance consumption.
OpenAI’s current documentation also explicitly states that Astra can consume included Work and Codex allowance faster than GPT-5.6 Sol.
What remains unclear is why the difference can be this large.
Is Astra intentionally weighted much more heavily against the weekly Codex allowance? Is additional reasoning or server-side agent activity being counted that isn’t readily visible to users? Is cached context accounted for differently? Or is there currently an issue with Astra usage metering during the rollout?
Whatever the mechanism, users need much better visibility into it.
Ideally, Codex should expose per-task and per-model allowance attribution so that we can see exactly how much weekly capacity a particular run consumed and why. Right now, the practical way to determine this is to record the usage percentage before a run, execute the task, and compare the meter afterward.
For the moment, I’ve switched back to GPT-5.6 Sol for production work.
Astra may be substantially more capable, but at the consumption rate I observed, I simply cannot justify using it. A model that consumes approximately 40% of my weekly allowance during a single 40-minute repository task prevents me from doing the sustained development work I use Codex for in the first place.
I’d be particularly interested in hearing from other developers who have compared Astra directly with GPT-5.6 Sol on sustained repository work. Are you seeing a similar difference in weekly allowance consumption?
References:
Related GitHub Issues in the OpenAI Codex Repository
GitHub Issue #43029 — GPT-6 Astra Ultra consumes approximately 30% of a Pro 20x weekly quota during a roughly one-hour task.
GitHub Issue #43201 — GPT-6 Astra usage limits consuming rapidly / short session length. User reports exhausting available capacity in approximately 20 minutes.
GitHub Issue #43222 — GPT-6 Astra weekly quota depletion appears disproportionate to local token telemetry in a same-day Astra-to-Sol comparison.
GitHub Issue #43141 — Astra usage limit reached again in under an hour after applying a usage reset.
GitHub Issue #43230 — Astra token burn drastically increased; Pro 20x user reports approximately 20% depletion shortly after a reset and a substantial difference compared with Sol.