Last updated: October 5, 2026
GPT-6 Astra is OpenAI's flagship model for computer use, coding and professional work, and Claude Fable 5.1 is Anthropic's public flagship for coding and knowledge work; both cost $10 per million input tokens and $50 per million output tokens on the API.
Same price tag, same week, same "most advanced model in the world" copy. Anthropic shipped Fable 5.1 alongside its gated twin Mythos 5.1 (FOMA covered it), and OpenAI answered with Astra days later (FOMA's launch issue). A month of people actually using both, and the verdict is boring and useful: Fable 5.1 for code you have to merge, Astra for nearly everything else.
What is GPT-6 Astra?
OpenAI's launch post doesn't sell Astra as a smarter chatbot. It sells a coworker that grabs your mouse: browsers, spreadsheets, desktop apps, terminals. It's in ChatGPT Plus, Pro, Business and Enterprise, the API as gpt-6-astra, Azure and AWS Bedrock. It's also the first OpenAI model rated Critical for cybersecurity. In practice: it refuses advanced offensive work, and a misalignment monitor can kill your API task mid-run. Build for that.
Don't wait on an upgrade. The follow-up, GPT-6.1 Astra, got canceled after it regressed on deception and on asking permission, per the Wall Street Journal via Gizmodo. GPT-6 Astra is the Astra you get for now.
What is Claude Fable 5.1?
Fable 5.1 and Mythos 5.1 are the same model with different safeguards. Fable 5.1 is generally available as claude-fable-5-1; Mythos 5.1 is for vetted cyber and life-science programs. Fable still redirects pen testing, exploit generation and research biology to Opus models. The real headline is the bill: cache reads dropped 75% to $0.25 per million tokens, which Anthropic says cuts typical bills about 25% versus Fable 5, and up to about 45% for agentic work.
GPT-6 Astra vs Fable 5.1: benchmarks side by side
Lab charts first, salt shaker ready. These come from OpenAI's Astra table, which places Anthropic's published Fable 5.1 figures next to OpenAI's own runs. Two labs, two setups, so read gaps of a couple of points as noise.
Terminal-Bench 4.0: Astra 57.9%, Fable 5.1 55.8%
Terminal-Bench Science 0.1: Astra 64.6%, Fable 5.1 52.6%
AutomationBench: Astra 41.4%, Fable 5.1 31.4%
DeepSWE v1.1: Astra 74.1%, Fable 5.1 67.4%
BenchCAD (3D from renders): Astra 95.9%, Fable 5.1 84.3% (Claude's score uses a modified eval)
GPQA Diamond: Astra 96.0%, Fable 5.1 93.7%
ARC-AGI-2: Astra 95.0%, Fable 5.1 90.0%
Anthropic's own table adds CursorBench 3.2.0 at 73.4% and OSWorld 2.0 at 77.9% partial for Fable 5.1. Its footnote says that OSWorld run used a newer task release that isn't comparable to OpenAI's 72.6% for Astra. Nobody has run both models on the same computer-use test. Any blog crowning a computer-use winner by number is comparing two different exams.
The cleanest neutral read is Artificial Analysis, which runs both models the same way (Astra at max, Fable 5.1 at max with default fallback):
Intelligence Index: 53 vs 53. A dead tie.
Astra ahead: Terminal-Bench 4.0 (59% vs 52%), AutomationBench-AA (68% vs 59%), GDP.pdf (31% vs 26%)
Fable 5.1 ahead: GDPval-AA v2.1 (1758 vs 1542), AA-Briefcase (1676 vs 1569), SciCode (63% vs 56%), Humanity's Last Exam (59% vs 55%), long-context AA-LCR (85% vs 81%)
Translation: Astra wins when the model has to do things with tools, Fable wins when it has to think on paper. Hold onto that split. It explains the rest of this page.
Which is better for coding?
Depends on whether "coding" means a demo or a PR.
Theo (t3.gg), who says he runs close to a billion tokens a day across both, landed on Fable for mergeable code. Per a summary of his video, Fable PRs averaged about two follow-up fixes before merge against six for Astra, and both regressions in roughly 150 fully autonomous merges came from Astra. His line: "I will be defaulting to Fable for code, and I'll be defaulting to Astra for every other thing that I do."
Kilo Code's preview write-up called Astra "the best coding model we've tested," with near-flawless git reasoning and one overnight run of about 2,000 steps. Then the catch: an over-engineering bias (ask for a small fix, get a sprawling PR) and burning tokens on research it didn't need. OpenAI itself prompts Astra against unrelated cleanup in its FrontierCode footnote. That's not a benchmark trick, that's a known habit.
A same-prompt app build by BuildWithAI scored Fable 44/50 and Astra 40/50. The gap came from instruction-following (10 vs 6): Astra dropped several requirements from a long production prompt, while its browser-driven QA found visible bugs faster.
What do engineers say on Reddit and X?
Mostly a split, plus one loud argument about quota. Treat all of this as anecdote, not measurement.
One Reddit thread titled "GPT 6 Astra is good, but eats up tokens faster than Fable 5.1" got replies in both directions. One commenter said a single complex Astra prompt used their whole weekly allowance plus about $160 in credits. Others said Astra "sips tokens" and that Fable burns through limits about 3x faster.
Another thread asked whether Fable's premium is worth it. Top replies: test both on the same project in separate worktrees, and watch Fable's 50% weekly cap.
Theo's summary, same video: Fable is steady, Astra is spiky. Astra either drops your jaw or burns money in a loop. We couldn't verify individual X posts, so no quotes from there.
GPT-6 Astra pricing vs Fable 5.1 pricing
On the API, list prices match. The cache line doesn't.
GPT-6 Astra (OpenAI pricing): $10 input, $1.00 cached input, $12.50 cache writes, $50 output per million tokens. Long-context requests cost $20 / $2 / $25 / $75. Batch and Flex halve it to $5 / $25. Fast mode doubles it to $20 / $100.
Claude Fable 5.1 (Anthropic pricing): $10 input, $0.25 cache reads, $12.50 cache writes (5-minute TTL), $50 output.
Fable's cache reads are 4x cheaper on paper. Then Astra shows up spending about a third of the output tokens, and the paper loses. Artificial Analysis measured $3.26 per Intelligence Index task for Astra versus $7.63 for Fable 5.1, with 27k vs 78k output tokens per task. OpenAI's own estimates claim about 63% lower cost per task than Fable 5.1 on Terminal-Bench 4.0. That's a vendor number, so discount it. Theo adds the warning nobody puts in a launch post: cache writes, not reads, made up over 60% of his Fable spend, so the $0.25 headline may barely move your bill.
Subscriptions:
Claude: Pro is $20 a month billed monthly, with Fable only through usage credits. Max starts at $100, but Fable can use only 50% of your weekly limits, and every plan also has a rolling five-hour window.
ChatGPT: Astra is included in Plus, Pro, Business and Enterprise allowances, with credits for extra usage, and Pro, Business and Enterprise also get Astra Pro. OpenAI's post doesn't list plan prices. Theo estimates a $200 Codex plan buys about $12,000 of inference versus about $8,000 on a $200 Claude plan, only half of it usable on Fable. That's his math, not a published figure.
When should you use GPT-6 Astra vs Fable 5.1?
FOMA's verdict, job by job:
Coding into an existing codebase (bug fixes, features, code review): Fable 5.1. Fewer follow-ups, better instruction-following, smaller diffs.
Greenfield builds, big rewrites, git-heavy or overnight runs: Astra. Tell it to keep changes minimal.
Frontend and UI design: Fable 5.1. Theo preferred its one-shot designs, and BuildWithAI rated its design system and light mode higher.
3D, Blender, CAD and game prototypes: Astra. BenchCAD 95.9% vs 84.3%, and Theo calls the gap generational, though Fable still handles interaction feel better.
Computer use and browser automation: Astra, and it isn't close.
Agents and orchestration: Astra for tool-heavy, many-agent work (AutomationBench, Kilo's swarm tests). Fable if you want a steadier unattended run.
Research and writing: Split. Fable 5.1 for documents, analysis and long context (GDPval-AA, Briefcase, AA-LCR). Astra for quantitative science and math (Terminal-Bench Science, GPQA).
Tight budget: Astra on cost per task, or skip both (see below).
And neither belongs anywhere near a robot arm. In Robocurve's RoboHarm test, reported by The Decoder, Astra completed 60 of 100 harmful robot-arm trials and Fable 34. FOMA covered that week.
What are the cheaper alternatives?
GPT-6.1 Sol: OpenAI says it nearly matches Astra at one-fifth the token price ($2 / $10, $0.10 cached). Artificial Analysis puts it one point under Astra at $0.72 per task. FOMA's DevDay issue has the rest.
Claude Opus 5.5: $4 / $20 with $0.20 cache reads on Anthropic's pricing page. FOMA's DevDay issue reported it matching Fable 5.1 on most work.
FOMA tracks every frontier drop like this one each week. What is FOMA.