ChatGPT Astra vs Claude Fable 5.1: Who is ahead, and what you need to know

Two flagship AI models landed within 48 hours of each other in early September 2026, and somehow OpenAI and Anthropic settled on the exact same price without comparing notes: $10 per million input tokens, $50 per million output tokens. Identical, down to the dollar. If you're comparing ChatGPT Astra (its formal name is GPT-6 Astra, API id gpt-6-astra, though most people just search for "ChatGPT Astra") against Claude Fable 5.1 and assuming the matching sticker price settles the question, stop right there. It doesn't. Two details buried a few pages into each company's pricing docs, one governing what happens past 272,000 input tokens and the other governing what it costs to reread something the model already saw, mean the real bill on these two models can diverge by 25 to 45 percent depending on how you actually use them. This post works that math out in full, and along the way catches a benchmark comparison that most "Astra vs Fable" posts online are currently getting wrong.

One honest note before we go further. Both of these models are days old as of September 6, 2026: GPT-6 Astra shipped September 3, and Claude Fable 5.1 shipped September 1. OpenAI and Anthropic ship new models, pricing tweaks, and feature updates at a genuinely fast clip, so treat everything below as accurate as of this writing, and confirm the current lineup, pricing, and availability directly on each platform before you commit real budget to either one.

Quick scope note too, since we've covered adjacent ground before. Our ChatGPT vs Claude piece compares the two platforms as whole products, apps, subscriptions, image generation, Custom GPTs, and so on. This post is narrower, and if you're building on the API, more useful right now: it's about these two specific new models, what they cost per token, and why an identical headline price hides two very different bills.

Quick Overview: ChatGPT Astra vs Claude Fable 5.1 at a Glance

What Is GPT-6 Astra (ChatGPT Astra)?

GPT-6 Astra is OpenAI's newest flagship model, released September 3, 2026, with the API identifier gpt-6-astra. Most people search for it as ChatGPT Astra, since that's the name it shows up under inside the ChatGPT app, so we'll use both names here and lean on "Astra" for short from this point on.

OpenAI staged the rollout rather than flipping it on for everyone at once. On launch day, access was limited to OpenAI's Trusted Access and Daybreak programs. From there it reached Pro, Enterprise, and Business Premium users across ChatGPT Work, Codex, and the API, with Plus and Business access still rolling out as of this writing. If you're on a lower tier, don't assume you already have it, check your own plan.

The bigger headline is a safety one. Astra is the first OpenAI model classified at the "Critical" cybersecurity capability level under the company's own Preparedness Framework, and it found two previously unknown vulnerabilities in V8, Chrome's JavaScript engine, during evaluation. That capability comes with a leash attached: standard access refuses advanced cybersecurity work like active exploit discovery. OpenAI President Greg Brockman called it the company's "most intelligent and, also very importantly, our most aligned model yet."

On OpenAI's own published benchmarks, Astra scores ARC-AGI-3 at 99.9%, ARC-AGI-2 at 95%, ARC-AGI-1 at 98.5%, and FrontierMath Tier 4 at 97.6%. On coding-adjacent tests it reports DeepSWE v1.1 at 74.1% and Terminal-Bench 4.0 at 57.7 to 57.9%. On its own security benchmarks it hits ExploitBench 100% and ExploitGym 42.4%, up from the prior GPT-5.6 Sol's 30.3%. On OSWorld V2-Offline, a computer-use benchmark, it scores 72.6%, versus Sol's 65.7% (more on why that number needs careful handling below). One internal OpenAI measure reportedly saw task completion time drop from roughly 75 minutes to around 40.

A few other specs: max output is 128,000 tokens, knowledge cutoff is April 30, 2026, and it takes text and image input but only produces text output. Batch and Flex run at 50% of standard pricing; Fast mode runs at 2x. Weights are closed, no self-hosting. Full technical and pricing detail lives on OpenAI's own model docs.

What Is Claude Fable 5.1?

Claude Fable 5.1 is Anthropic's current flagship, released September 1, 2026, alongside a separate limited-availability model called Claude Mythos 5.1. Its API identifier is claude-fable-5-1. Anthropic positions it as built for the hardest knowledge work and coding problems, tuned specifically for long-running, multi-step agentic tasks rather than quick single-turn answers.

It's available to Pro, Max, Team, and Enterprise subscribers inside claude.ai, plus developers through the Claude Platform API and the major cloud providers. One detail worth knowing if you already pay for Claude: Max-tier and other premium seats can spend up to 50% of their weekly usage limits on Fable-class models at no extra cost, so a chunk of this model's cost may already be baked into a subscription you have.

On Anthropic's own published benchmarks, Fable 5.1 scores SWE-bench Verified at 95.0%, SWE-bench Pro at 81.2%, and SWE-bench Multilingual at 89.1%. On general reasoning, it hits ARC-AGI-2 at 90%, ARC-AGI-1 at 97.5%, GPQA Diamond at 93.4%, and MMLU-Pro at 92.4%. On agentic and coding-adjacent tests it reports Terminal-Bench 4.0 at 55.8% and DeepSWE v1.1 at 67.4%. On OSWorld 2.0's August 2026 task release, it scores 77.9% on partial credit and 41.7% on strict credit, a distinction that matters a lot and gets its own section below.

A few more specs: max output is 128,000 tokens, same ceiling as Astra. Batch runs at 50% off ($5/$25 per million tokens). US-only inference via the inference_geo parameter applies a 1.1x multiplier across every pricing category. Weights are closed and API-only, same as Astra.

ChatGPT Astra vs Claude Fable 5.1: Feature-by-Feature Comparison

Pricing structure: same sticker, two different rulebooks

Here's the headline everyone already reported: both models charge exactly $10 per million input tokens and $50 per million output tokens. Match to the dollar. If pricing were the only variable, this would be a coin flip. It isn't, because both companies attach a rule to that headline number that only shows up once you read past the first line of the pricing page.

Astra's rule: cross 272,000 input tokens in a single request, and OpenAI doesn't just charge more for the tokens over that line, it re-rates the entire request. Input jumps to $20 per million, cached input to $2 per million, cache writes to $25 per million, and output to $75 per million, applied to every token in that request, not just the overage.

Picture a toll road that charges a flat rate up to mile 272. The moment your odometer ticks past that marker, the booth doesn't just charge extra for the miles after 272, it reaches back and re-bills your entire trip at the higher rate, including the first 272 miles you already drove at the cheap price. That's what happens to your whole request's bill the instant it crosses Astra's line, a retroactive cost, not a marginal one.

Fable's rule is the opposite: there isn't one. Anthropic's own docs state plainly that a 900,000-token request bills at exactly the same per-token rate as a 9,000-token request. The entire 1 million token context window prices flat, no matter how full you fill it.

Cache reads: a 4x gap hiding in plain sight

The second gap is in what it costs to reread something the model has already processed, the core mechanic behind prompt caching. Astra's cache read rate is $1 per million tokens, 10% of its $10 standard input price. Fable's cache read rate is $0.25 per million tokens, 2.5% of its own $10 standard input price, four times cheaper than Astra's.

Think of it as reheating a meal you already cooked. If the original dish cost a dollar to make, Astra charges you a dime to microwave the leftovers. Fable charges about two and a half cents for the exact same reheated plate. Every reused token in a long agent loop, a repeated system prompt, a big document you keep referencing, hits this rate, so the gap compounds fast the more your workload reuses context.

Interestingly, the two aren't apart on every caching number. Astra's cache write rate, $12.50 per million tokens, matches Fable's own 5-minute cache write rate exactly, also $12.50 per million (Fable's longer 1-hour cache write runs $20 per million). It's specifically the read side, the part you pay every time cached content actually gets reused, where the two companies picked very different numbers.

Benchmarks: what each vendor claims

Each company published its own benchmark suite, and unsurprisingly, each one wins on its own scorecard. On the two coding-adjacent tests both companies happened to publish in common, Terminal-Bench 4.0 and DeepSWE v1.1, Astra edges ahead on both: 57.7 to 57.9% versus Fable's 55.8% on Terminal-Bench, and 74.1% versus 67.4% on DeepSWE. Read only vendor blog posts and Astra looks like the sharper coding model, full stop.

Independent scoring complicates that story. Artificial Analysis's Intelligence Index v4.2, a blend of ten separate evaluations including AA-Briefcase, GDPval-AA v2, tau-cubed-Banking, Terminal-Bench v2.1, SciCode, Humanity's Last Exam, GDP.pdf, CritPt, AA-Omniscience, and AA-LCR v1.1, currently puts Fable 5.1 at 57, ranked #1 on their overall leaderboard, against Astra at 55 (measured at Astra's max setting). Their separate Coding Agent Index, which blends Deep SWE, Terminal Bench, and a repo Q&A test, has Fable running inside Claude Code at 70 against Astra at 67, flipping the coding lead back toward Fable on the one independent, coding-specific measure available. A third independent source, BenchLM's aggregate scoring, puts Fable ahead overall too, 82.95 to 81.05, leading specifically on Agentic (78.7 vs 70.4), Coding (84.2 vs 75.3), and Knowledge (86.4 vs 81.7) categories, while Astra leads on Reasoning (88.8 vs 79.4).

Speed is close to a wash rather than a clean win either way. At matched maximum-effort settings, Artificial Analysis clocks Astra at 71.3 tokens per second against Fable's 70.5, essentially tied. Push each model to its fastest available setting and Astra's "xhigh" mode reaches 81.2 tokens per second against Fable at its lowest effort setting at 56.9, but that's comparing one model's ceiling to the other's floor, not a fair matchup. At comparable effort levels, call speed a draw.

The OSWorld trap: why "72.6% vs 41.7%" is comparing apples to engines

Here's where it's worth slowing down, because a specific number is spreading across comparison posts right now, and it's wrong. Several sites are printing "Astra 72.6% vs Fable 41.7%" on the OSWorld computer-use benchmark as if it were one clean, apples-to-apples row. It isn't.

Astra's 72.6% is scored on OSWorld V2-Offline, one specific benchmark variant. Fable 5.1's numbers come from a different release entirely, OSWorld 2.0's August 2026 task set, where Anthropic reports two separate figures: 77.9% on partial credit and 41.7% on strict credit. Partial credit gives a model credit for correctly completing most steps of a multi-step task even if the final step slips. Strict credit only counts the task as a win if every single step lands cleanly, no partial points.

Stack Astra's offline score against Fable's strict score, and Fable looks like it's running at barely half of Astra's capability. Stack it against Fable's own partial score instead, 77.9%, and the two models look close to even. Neither comparison is actually fair, because OSWorld V2-Offline and OSWorld 2.0 aren't the same test, run on the same task set, scored the same way. The honest answer is that no clean, like-for-like OSWorld comparison between these two models exists yet. Treat any chart claiming otherwise, however confident it looks, as comparing two different rulers and calling it one measurement.

Access and rollout: who can actually use these today

Neither model is available to every user yet. Astra's staged rollout means Plus and Business ChatGPT users may still be waiting. Fable 5.1 is live for Pro, Max, Team, and Enterprise subscribers on claude.ai and for developers on the API, a somewhat broader footprint, though Anthropic's separate Mythos 5.1 remains limited-availability only. Check your own plan before assuming you already have either model.

ChatGPT Astra vs Claude Fable 5.1 Pricing: The Real Math

Sticker price says these two models cost the same. Real usage says otherwise. Here's the full rate card, followed by two worked scenarios so you can see exactly where the numbers split.

Scenario A: the 272K cliff, in real dollars

Say you send a request with 280,000 input tokens and get back 20,000 output tokens, no caching involved. That's not an exotic edge case, it's a moderately long document plus a detailed response.

Here's the arithmetic behind those numbers. Astra at 280K: 280,000 tokens times $20/million input, plus 20,000 times $75/million output, comes to $5.60 plus $1.50, or $7.10. Trim the same job to 272,000 input tokens and it's 272,000 times $10/million plus 20,000 times $50/million, or $2.72 plus $1.00, $3.72 total. Fable 5.1 on the identical 280,000-token job: 280,000 times $10/million plus 20,000 times $50/million, or $2.80 plus $1.00, $3.80 total.

The takeaway worth remembering: 8,000 extra input tokens, roughly six pages of text, nearly doubles Astra's bill for that request, from $3.72 to $7.10. Trim your prompt below 272K and you cut the cost by close to half. Or skip the trimming exercise entirely and run the job on Fable, which charges $3.80 for the identical 280,000-token job, about 46% less than Astra pays once it crosses its own cliff, despite an identical per-token sticker price on both models. These numbers assume no caching, a single request, and standard (non-batch, non-fast) rates on both sides, rerun them with your own token counts before trusting them blindly.

Scenario B: a cached agentic loop

Now picture an agent working through a 20-step task, where each step reuses 90,000 tokens of cached context (a big system prompt, a loaded codebase, a long document), adds 10,000 tokens of new input, and produces 2,000 tokens of output. This is a realistic shape for a coding agent or a research assistant working a multi-step job.

The per-step math: Astra charges 90,000 times $1/million for the cache read, plus 10,000 times $10/million for new input, plus 2,000 times $50/million for output, or $0.09 plus $0.10 plus $0.10, $0.29 a step, $5.80 across 20 steps. Fable charges 90,000 times $0.25/million for the cache read, plus the same $0.10 new input and $0.10 output, or $0.0225 plus $0.10 plus $0.10, $0.2225 a step, $4.45 across 20 steps. Fable comes out roughly 23% cheaper on this exact shape of work, purely from its lower cache read rate, with everything else about the workload held equal.

If any of this cost-modeling feels familiar, it should. Usage-based AI pricing has a habit of looking cheap on the sticker and creeping up once real usage kicks in. We've watched the same pattern play out with GitHub Copilot's mid-2026 shift to metered billing, where a flat-feeling $10 plan turned into an unpredictable bill the month usage-based Credits replaced a fixed monthly allowance. The lesson carries over directly here: run your own numbers against your actual workload shape before committing a production job to either model, using the two scenarios above as a starting template.

Use Cases: Choose GPT-6 Astra If... / Choose Claude Fable 5.1 If...

Choose GPT-6 Astra if:

• Your workload leans on raw reasoning benchmarks like ARC-AGI and FrontierMath, where Astra's vendor-published numbers lead clearly.

• Your prompts consistently stay well under 272,000 tokens, so the long-context cliff never actually triggers.

• You need Astra's security-research capability specifically, understanding that standard access is gated and won't do advanced exploit discovery work.

• You already have Trusted Access, Daybreak, or Pro/Enterprise/Business Premium access and want the newest OpenAI flagship today rather than waiting for a broader rollout.

• Maximum throughput at top speed settings matters more to you than close reasoning parity, since Astra's xhigh mode is the fastest setting either model offers.

Choose Claude Fable 5.1 if:

• Your workload regularly pushes past 272,000 input tokens, long documents, large codebases, or sprawling multi-file agent context, and you don't want a pricing cliff waiting there.

• You run agentic loops or any workflow that repeatedly rereads the same context window, where 4x cheaper cache reads compound fast.

• You'd rather trust an independent, third-party benchmark blend over vendor-published numbers alone, and Fable currently ranks #1 on Artificial Analysis' Intelligence Index.

• You're already a Claude Pro, Max, Team, or Enterprise subscriber, especially Max tier, where Fable-class usage is already included up to 50% of your weekly limits at no extra API cost.

• Predictable billing matters more to you than chasing the single highest reasoning score on a vendor's own chart.

Pros and Cons: ChatGPT Astra vs Claude Fable 5.1

GPT-6 Astra

Claude Fable 5.1

Final Verdict: ChatGPT Astra vs Claude Fable 5.1

So which one should you actually pick? Given the identical sticker price, the honest answer depends entirely on the shape of your workload, not on which model "wins" a benchmark chart.

If your prompts run short to medium, well under 272,000 tokens, and you're doing one-shot reasoning-heavy work, security research your plan actually permits, or you already have staged access and want the newest OpenAI flagship today, GPT-6 Astra's vendor-published edge on reasoning benchmarks is real. Use it.

If you're running anything that regularly crosses 272,000 tokens, long documents, sprawling codebases, multi-file agent context, or any workflow that loops and rereads the same context window repeatedly, Claude Fable 5.1 is the financially safer default. It has no cliff waiting to double your bill, and its cache reads cost a quarter of Astra's. The math above shows Fable coming in 46% cheaper on a request that crosses the cliff and 23% cheaper on a cached 20-step agent loop, purely from pricing mechanics, before you've even weighed which model reasons better.

And if coding specifically is your job, don't lean on either company's own marketing chart to decide. The vendor-published coding rows favor Astra, the one independent coding-agent measure favors Fable, and testing both against your own codebase, the same advice that applies when weighing an AI-native editor like Cursor against a plugin-style assistant, will tell you more than any benchmark table on this page.

Both companies will ship updates to these numbers soon enough. Confirm current pricing and access directly on OpenAI's model docs and Anthropic's pricing page before you commit a production workload to either one.