Same price, opposite strengths. One of them builds better, the other operates better, and neither company will tell you what your weekly limit actually is.
Point at a category to see what decided it.
Edge is an editorial call on the published evidence, not a measurement. Hover or tap any category name for the reasoning; every figure behind it is sourced at the foot of the page.
The models are the draw. The plans are how you get them, and they are shaped very differently.
OpenAI's $100 tier arrived in April 2026, after Codex crossed three million weekly users, offering 5× the Codex access of Plus at Anthropic's price point.
Neither company publishes a weekly token number. Both publish structure, and the structure differs far more than the shared price suggests.
One bucket against two. Most of the burn complaints on both platforms trace back to this.
Spend 70% on Opus and Sonnet and you hit the overall wall at 100%, even though Fable never touched its 50% share. Waiting out the 5-hour clock does not reset the weekly one.
The separation is real, but the chat pool is small. 50 messages a week is a handful of hard questions. Computer use lands in the coding pool, so it competes with your coding.
Launch-week burn on both sides. Individual reports, not measured averages.
| Model | Plan | What happened |
|---|---|---|
| Fable 5.1 | Max 20x | Full 5-hour quota gone in 52 minutes from a single prompt |
| Fable 5.1 | Max 20x | 45 minutes of coding across 3 chats took the 5-hour session and 38% of the weekly Fable allowance |
| Fable 5.1 | Max | "Burns out the usage limits on Low effort within 5–10 minutes" |
| Fable 5.1 | Max | Cap hit in 4 minutes on cache-heavy work; cache writes ran over 65% of real cost |
| Astra | Pro | "Eats a full week in a day. The only thing keeping Codex usable is OpenAI handing out resets every 48 hours" |
| Astra | Plus | "One proper Astra prompt can use all of your 5 hour usage limit" |
Subagents inherit the lead model by default. One prompt spawning nine Fable subagents is what produced the 52-minute figure. Pinning subagents to Sonnet 5 or Haiku 4.5 moves that fan-out off the expensive meter entirely.
Both companies are squeezing. Only one of them tells you in advance.
Anthropic's 5× and 20× apply to the 5-hour session, not the week. The Kahn v. Anthropic suit alleges real weekly throughput for Max 20x runs closer to 6–8× Pro. Treat that as an allegation, but treat the multiplier as a session number either way. OpenAI publishes no weekly number at all.
Reverse-engineered, not official. Included because they are the only per-plan figures in existence.
| Plan | Per 5 hours | Per week | Confidence |
|---|---|---|---|
| Claude Max 5x | ~225 messages | ~140–280 Sonnet-h, ~15–35 Opus-h | estimate |
| Claude Max 20x | ~900 messages | not published | estimate |
| Pro $100, Astra | 25–225 local msgs | not published | official range |
| Pro $100, Sol | 50–500 local msgs | not published | official range |
| Plus $20, Astra | 5–45 local msgs | not published | official range |
What it means: Claude gives you a dated, quantified cut you can plan against. ChatGPT gives you a larger burst budget and free resets, but the number can move without notice. At $100 a month, a limit you can plan against is worth more than one that is occasionally larger.
Closer than either launch post suggests. Astra takes the multi-step trajectory benchmarks; Fable takes patch accuracy and the one neutral composite.
LiveCodeBench 90.52%, CursorBench 3.2.0 73.4% up from 70.5%, and Terminal-Bench-Science 52.6%, more than double Fable 5's 24.7%. OpenAI has published no matched figures for these.
One tester's 15 real-world use cases scored Astra 10 wins to Fable's 5. Astra also finished tasks on ~21K tokens where Fable used ~64K.
What it means: repo-scale refactors and correct patches favour Fable's SWE-bench Verified 95.0% and the neutral composite. Long autonomous terminal trajectories favour Astra's DeepSWE and Terminal-Bench wins. Nobody should switch a $100 subscription over a 3-point composite gap.
The most one-sided category on this page, and the only one with no numbers behind it at all.
Every design comparison published so far is an uncontrolled hands-on. Nobody has run identical prompts side by side under matched conditions. What follows is consensus from repeated independent testing, not measurement.
One-shots polished, custom UI. Repeatedly called the best design model available. Strongest where work is iterated toward a finished product.
Weakness: slower to a good first draft on marketing pages. Needs iteration where Astra sometimes does not.
Real layering and parallax depth rather than flat AI output. One tester found its first attempt matched a Fable design refined over ~15 iterations. Also handles multimodal editing.
Weakness: a recurring green-tinted card-and-grid look, and a tendency to turn any request into a landing page. A taste failure, which more quota does not fix.
What it means: if design means a marketing site you generate once, Astra is competitive and faster. If it means an application interface you will refine, Fable is the stronger choice, and the gap here is the widest on the page.
Astra's clearest win, though smaller than the coverage implies. Most of the numbers quoted as a head-to-head are not one.
10 points absolute, about 32% relative. That is the honest size of Astra's measured advantage on computer use.
You will see "Astra 72.6% against Fable 77.9%" quoted as a ranking. It is not one. The two labs ran different task releases with different scoring conventions, and Fable 5.1 was never run on OpenAI's row. One tracker puts it plainly: do not mix harnesses; 72.6% and 77.9% are not one ranking.
| Benchmark | Astra | Fable 5.1 | Comparable? |
|---|---|---|---|
| AutomationBench | 41.4% | 31.4% | yes |
| OSWorld 2.0 | 72.6% | 77.9% / 41.7% | different harness |
| ScreenSpot-Pro | 92.7% | not published | no Fable figure |
| BrowseComp | 91.5% | not published | no Fable figure |
| OSWorld time per task | ~40 min | not published | vs Sol's ~75 min |
| Mind2Web speed | 1.9× faster | not published | vs Sol setup |
OpenAI's computer-use harness rewrite sped up GPT-5.6 Sol by roughly 60% as well. "Astra is 2× faster" bundles engineering that would have helped any model.
This drives more real-world frustration than the benchmark gap does.
An extension driving your own Chrome through the debugger API: your profile, your cookies, your logged-in sessions.
Documented failures: persistent connection failure from the Claude Code CLI; Claude Desktop's Cowork silently breaking Claude Code's Chrome integration and the reverse; a failed bridge at startup killing the connection for the whole session; reports of it moving very slowly; Chrome crashes; a prompt-injection vulnerability below v1.0.41.
Model by plan: Pro gets Haiku only. Max and above can point it at Opus or Sonnet. Browser work is more compute-intensive than chat, so it eats more of your limit.
Atlas was killed around 9 Aug 2026. OpenAI's stated reasoning: the browser is a feature, not the destination. Its parts split three ways: page Q&A into a Chrome extension, logins and downloads into the desktop app, and the agent itself onto a remote cloud browser on OpenAI's servers.
Upside: no local Chrome to crash, no debugger bridge to fail, no two products fighting over one browser. That entire class of failure does not exist.
Downside: it is not your browser. Astra runs credential-blind, driving an authenticated session without login details entering its context, but it pauses for approval at login walls and before consequential actions.
What it means: Astra is measurably better at business-workflow automation and substantially faster. If your frustration is capability, that is a real 10-point gain. If your frustration is plumbing, the cloud-browser architecture is the bigger win. Both platforms charge computer use against your coding budget; neither gives it a separate allowance.
If you avoided Claude because it refused security work, that was Fable 5. It was fixed on 1 September.
Fable 5's cyber classifier keyed on security-adjacent vocabulary rather than intent. It flagged systems programming terms, cloud reliability language, and the literal phrase "code review". Defensive vulnerability discovery was blocked at 90.0%. In Fable 5.1 that dropped to 7.0%, with roughly 60% fewer classifier interventions per session.
Both columns are hands-on test results, not policy documents. Fable tested against a deliberately broken PHP login handler; Astra tested via API against vulnerable PHP the testers owned.
| Task | Fable 5.1 | Astra |
|---|---|---|
| Source-code vulnerability review | allowed, full model | allowed |
| Patch and remediation | allowed | allowed |
| Regression test for the fix | allowed | allowed |
| Detection rule authoring | allowed | allowed |
| Defensive hardening | no downgrade | allowed |
| Penetration testing | falls back to Opus 4.8 | Daybreak Blue |
| Compiled binary analysis | falls back to Opus 4.8 | Daybreak Blue |
| Proof-of-concept exploit | falls back to Opus 4.8 | refused, HTTP 400 |
Astra's block fires at the classifier before the model sees the prompt. Stating authorisation and retrying unchanged made no difference. The classifier keys on task shape, not declared intent.
Fable silently downgrades to Opus 4.8 under a banner. In a long agent run the substitution can pass unnoticed, and mid-response flags bill at dual-model rates. Astra hard-fails. For automated auditing the hard fail is safer: a silent quality downgrade halfway through a security audit is exactly the failure you do not want in a security audit.
Its own system card shows every successful prompt-injection attack against Fable 5.1 was an attack on the fallback model. In browser automated mode, 21 of 29 successful attacks hit fallback models. The downgrade path is also the security-weak path.
OpenAI's advanced cyber capability is off by default and needs an enterprise admin. Daybreak Blue covers defensive workflows and Daybreak Red covers exploit development; one team applied in July and had no decision seven weeks later. Anthropic gates Mythos 5.1 the same way.
Claude Code has /security-review built in for SQLi, XSS, auth flaws,
insecure data handling and dependency vulnerabilities. The GitHub Action variant is
explicitly not hardened against prompt injection, under CVE-2025-59536, arbitrary code
execution via injection in PR content. Trusted PRs only.
What it means: auditing and repairing vulnerabilities in your own source works on both, today, on the standard plans. Astra has the better raw ability and the more honest failure mode; Fable has the better workflow integration and needs no gate for source review. This is a tie, and it should not decide your purchase.
Worth knowing on either platform: a study of 2,390 prompts from the National Collegiate Cyber Defense Competition found safety-aligned models refuse defensive requests carrying security terminology at 2.72× the rate of neutral ones. It also found that stating you were authorised made refusals go up, because the model read the claim as a warning sign.
Identical list prices, very different consumption, and one cache detail that partly cancels Astra's lead once you are on a subscription rather than an API bill.
| Per million tokens | Fable 5.1 | Astra |
|---|---|---|
| Input | $10.00 | $10.00 |
| Output | $50.00 | $50.00 |
| Cache reads | $0.25, cut 75% in 5.1 | not published at parity |
| Fast mode | — | ~2.5× multiplier |
| Effort levels | low → max, mid-conversation | low → max |
Over 90% of tokens in a heavy Claude Code session are cache reads. Fable 5.1 cut those 75% to $0.25/M, and on a Max subscription they are included flat. Anthropic's estimate is 25% cheaper than Fable 5 for typical work, up to 45% for highly agentic work. Astra's raw efficiency is real, but it pays the same $10/$50 list with a 2.5× Fast-mode multiplier available to make it worse.
What it means: Astra is materially more token-efficient and stretches a fixed quota further. On identical benchmark tasks Claude Code used 4× more tokens, and a matched task ran $2.50 against $2.04, a 23% delta. Fable's spend buys thoroughness rather than waste, but you pay for it. On subscriptions the cache pricing narrows the gap more than the token counts suggest.
Both delegate to cheaper models with their own reasoning effort. Only one lets the cheap tier escape the expensive meter.
The advantage: those models sit outside the Fable 50% weekly sub-cap. The token fan-out that kills people lands on the cheap meter, not the expensive one.
The trap: subagents inherit the lead model by
default. Leave that unset and nine Fable subagents spawn from one prompt, the
documented cause of the 52-minute quota wipe. Set model explicitly.
The controls: per-agent model_reasoning_effort from
low through ultra. Cleaner ergonomics than Claude's; this is a first-class API.
The limitation: everything shares one Codex pool, metered at Astra's halved rate. There is no cheap meter to move the fan-out onto.
Anthropic's own documentation puts multi-agent workflows at roughly 4–7× the tokens of a single-agent session, with Agent Teams around 15×. OpenAI's docs are blunter: subagent workflows consume more tokens than comparable single-agent runs. Neither platform makes delegation free; Claude gives you somewhere cheaper to put it.
Both are month-to-month with no annual lock. Switching costs you a month, not a year.
On Fable: pin subagent models rather than inheriting; run the orchestrator at low and escalate per message, which preserves the cache; clear between unrelated tasks; keep CLAUDE.md short; watch both the all-model bar and the Fable bar, since they hit independently. On Astra: start at medium reasoning and escalate only on conflicting evidence; avoid Fast mode by default; bound task scope before starting; route triage to terra and luna.
Every gap in the evidence behind this page, listed.
| Claim | Status |
|---|---|
| Weekly token or message caps for either $100 plan | neither company publishes one |
| OSWorld 2.0 as an Astra-vs-Fable ranking | invalid, different harnesses |
| Fable 5.1's ScreenSpot-Pro score | never published |
| "Astra 95% vs Fable 40% on robot control" | unsourced social claim, ignore |
| The ~4× Astra limit cut of 6–7 Sep | user reports only, unconfirmed |
| Design comparisons | all uncontrolled hands-on |
| Max 20x real weekly throughput at 6–8× Pro | litigation allegation, untested |
| Community per-plan hour estimates | reverse-engineered |
| Most benchmark scores | vendor-published; exceptions are Artificial Analysis and Irregular |
| ExploitBench and ExploitGym figures | pre-mitigation; the shipping models refuse this work |
Everything here reflects 8 September 2026. Claude's limits change on the 14th. OpenAI's have moved twice in the past week. Check the date before you trust the page.