GPT-6 Astra vs Claude Fable 5.1: Performance, Pricing, and Which to Use

What Is GPT-6 Astra?

GPT-6 Astra is OpenAI's frontier flagship, succeeding GPT-5.6 Sol, and it is built around agentic execution rather than chat. OpenAI positions it on computer use, professional work, and software engineering, and it is the first OpenAI model to cross the "Critical" cybersecurity threshold in the company's Preparedness Framework. It runs on a 1,050,000-token context window with a 128K max output and an April 30, 2026, knowledge cutoff.

Two things stand out in the announcement. In Codex, Astra keeps notes across context windows instead of compacting them into a summary, so earlier windows stay searchable, and it decides when to ask a clarifying question rather than always guessing or always asking.

For the full feature list, benchmark tables, and access details, see our GPT-6 Astra guide. It is also worth reading alongside our coverage of its predecessor in GPT-5.6 Sol, Terra, and Luna.

What Is Claude Fable 5.1?

Claude Fable 5.1 is Anthropic's generally available frontier model for demanding reasoning and long-horizon agentic work. It runs a 1M token context window with 128K max output, adaptive thinking that is always on, and a June 2026 knowledge cutoff. Anthropic's docs rate its latency as slower than Claude Opus 5 and Claude Sonnet 5, both of which sit below it on price.

Claude Mythos 5.1 is the same model with different safeguards, available by invitation through Project Glasswing. The headline change for everyone else is the cache read price, cut 75% to $0.25 per million tokens, while list rates stayed put.

Our Claude Fable 5.1 guide has the full benchmark breakdown, and our Claude Fable 5.1 API tutorial builds a repository-aware developer agent with it, including a real cost breakdown across effort levels.

GPT-6 Astra vs Claude Fable 5.1: Head-to-Head Comparison

The short version: OpenAI's comparison table shows Astra ahead of Fable 5.1 on nearly every row it published, and Artificial Analysis shows the reverse on both of its indices. Which one you believe depends on how much weight you give a vendor scoring its own competitor.

Coding and agentic workflows

Astra leads on the coding benchmarks OpenAI chose to publish, but the margins are thin and the independent index disagrees. On Terminal-Bench 4.0, which tests software engineering, system configuration, and data analysis in a terminal, OpenAI reports 57.7% for Astra against 55.8% for Fable 5.1. Anthropic publishes the same 55.8% figure, so that row at least is uncontested.

The gap widens on DeepSWE v1.1, where Astra reaches 74.1% against 67.4%, and closes almost entirely on FrontierCode 1.1 Main, where 53.3% against 50.9% sits inside the range Claude Fable 5 (53.5%) and Claude Opus 5 (53.4%) already occupied.

That last row is the one I keep coming back to. Artificial Analysis puts Fable 5.1 in Claude Code at 70, the highest score on its Coding Agent Index, and Astra in Codex at 67, roughly level with Claude Opus 5 and Claude Fable 5. Two caveats deserve salt here:

• The two models ran in different harnesses, Codex against Claude Code, so part of that 3-point gap belongs to the scaffolding rather than the model. Our Codex vs Claude Code comparison covers how differently those two behave.
• Astra gets there far more cheaply. Artificial Analysis measured it using one third of the tokens of GPT-5.6 Sol at max effort in Codex, and one fifth of the tokens of Claude Opus 5 at xhigh.

Astra wins the published coding rows by a nose and wins the efficiency argument outright, while Fable 5.1 holds the only independent coding-agent score that clears them both.

Reasoning and scientific work

This is the cleanest split in the whole comparison, and it goes both ways. Astra takes the math and science rows: 97.6% against 87.8% on FrontierMath Tier 4 v2, 96.0% against 93.7% on GPQA Diamond, and 64.6% against 52.6% on Terminal-Bench Science 0.1, the agentic research benchmark Anthropic had built its own launch around.

Fable 5.1 takes Humanity's Last Exam with tools, 65.0% against 57.2%, and it is not close. Artificial Analysis backs the direction of that result independently, scoring Fable 5.1 at 66 on its Intelligence Index against 61 for Astra, the highest score it has ever measured.

One caveat on that 66. Artificial Analysis ran Fable 5.1 with Anthropic's default server-side fallback, which routes safety-flagged requests to Claude Opus 4.8 or Claude Opus 5, and the fallback served roughly 4% of output tokens across the index. The score is Fable 5.1, as most people will actually call it, not the raw model in isolation.

If your work is graduate-level math or science, Astra. If it is the kind of broad, hard reasoning Humanity's Last Exam probes, Fable 5.1.

Computer use and professional artifacts

Astra owns this dimension, mostly because Anthropic has not published comparable numbers. OpenAI reports 72.6% for Astra on OSWorld 2.0's offline set against 70.2% for Claude Opus 5, and 92.7% on ScreenSpot-Pro against 87.3% for Claude Fable 5. Anthropic's own Fable 5.1 figures, 77.9% partial and 41.7% strict, come from a different task release and grading setup.

The artifact numbers are less ambiguous: 95.9% on BenchCAD against 84.3%, and 41.4% against 31.4% on AutomationBench.

For agents who click through real software and produce slides or CAD output, Astra is the stronger pick.

Safety, cybersecurity, and alignment

Astra's alignment numbers are the most under-discussed part of its launch. On OpenAI's internal computer-use safety benchmark, where lower is better, it scores 2.4% against 9.5% for Fable 5.1.

On cybersecurity, it is in another bracket: 100% on ExploitBench, and 86 of 226 FrontierCyber challenges solved against 34 for GPT-5.6 Sol in Irregular's independent testing. Anthropic loosened Fable 5.1 enough to find vulnerabilities but not exploit them.

Two caveats, both worth the salt:

• OpenAI reports Astra's written reasoning is harder to monitor than Sol's, because it solves problems in fewer written steps.
• UK AISI found Astra running simulated supply-chain attacks in 2 of 500 samples, even when the scope disallowed internet access, down from 60 of 499 when the scope was left ambiguous.

Astra is both the safer agent and the more capable attacker, hence the gating.

Pricing: what you actually pay

List rates are identical, so on the rate card, the only difference between these two models comes from caching and long context. That framing only holds if both models spend the same number of tokens on the same task, and they do not, which is why the measured per-task figures further down matter more than the rate card does.

Token rates side by side

Input, output, cache writes, and the batch discount all match to the cent. The cache read line is the exception, and it is a 4x gap: Anthropic cuts Fable 5.1's cache reads to $0.25, while OpenAI prices Astra's at $1.00, a 90% discount on its input rate.

Above 272K input tokens, the gap widens to 8x, because OpenAI's surcharge doubles cache reads to $2.00 alongside input, while Anthropic's pricing docs put the full 1M token context window at standard rates. Note how low that threshold sits relative to what Astra advertises: 272K is roughly a quarter of its own 1.05M window, so any request using more than a quarter of the context it ships with is billed at the higher tier.

Cache reads are the rate that compounds across a long loop, because an agent resends the same system prompt, tool definitions, and repository context on every turn. Our guide to prompt caching covers the mechanics of the cache write-cache read distinction, if that distinction is new to you.

What a real workload costs on the rate card

Everything in this table is list-rate arithmetic on a fixed token shape, not a measured result. It answers the question "if both models spent identical tokens, what would the rate card charge?" The next section answers the question of what they actually spend.

The formula is the same for every row: (volume ÷ 1M) × rate, summed across fresh input, cached reads, cache writes, and output. The cache-heavy row assumes a 100K prefix written once and read back 1,000 times, plus 5K fresh input and 1K output per request.

The over-threshold retrieval row is the headline. Keep each request under 272K input tokens, and the two models cost exactly the same $150. Push the same 10M tokens through in requests that each exceed 272K, and Astra jumps to $275 while Fable 5.1 stays at $150, because Anthropic prices the full 1M-token context window at standard rates.

The cache-heavy loop is the one most readers will hit. At 1,000 cached reads of a 100K prefix, the $0.75 gap per million cached tokens turns into a $75 difference on an otherwise identical workload. Note the shape of that workload, though: it is deliberately output-light, 1K output tokens per request against 100K cache reads. That is the one shape where Fable 5.1's cache advantage decides the bill.

What each task actually costs

Once you measure real token consumption instead of assuming it, the picture flips. Artificial Analysis prices every model per Intelligence Index task, and its method already includes input, cache hit, cache write, reasoning, and answer tokens, so Fable 5.1's cheaper cache is baked into the number.

At max effort, Fable 5.1 costs 2.25 times what Astra does for the same set of tasks at the same list price. Artificial Analysis reaches the same conclusion on its coding index, where Astra matches Fable 5 "at less than half the cost, driven by significant token efficiency gains". Astra's range runs down to $0.46 per task at low effort.

Fable 5.1 is not being punished by its rate card here, but by its output volume. Artificial Analysis measured it using about 1.7x the output tokens of Fable 5 at max effort, which is why it costs 20% more per task than its predecessor despite the cache cut. Strip the cut out, and the figure would be roughly $5.16, so the discount is doing real work, just not enough of it.

You are paying that premium for something. Fable 5.1 scores 5 points higher on the same index, and 4 points higher at xhigh. Whether 5 points is worth 2.25x is your call and not mine, but the version of this comparison where identical list prices mean identical bills does not survive contact with the measurements.

OpenAI's own captions point the same way, for what a vendor's self-report is worth. It puts Astra's estimated API cost per task at roughly 31% below Fable 5.1 on Terminal-Bench Science 0.1, roughly 63% below on Terminal-Bench 4.0, and roughly 86% below on BenchCAD, measured at the effort settings OpenAI chose for each model.

How GPT-6 Astra and Claude Fable 5.1 Performed

Astra slightly won our test overall, though both models shipped a working simulation on the first attempt.

The test

I ran one hard task against both models under identical conditions rather than several shallow ones. The test selected for this comparison is a from-scratch physics simulation built as a single HTML file, chosen because it probes exactly what both vendors claim to have improved: sustained correctness across a long build, with no library to lean on.

This instance is a freshened version of that test. The published variant used a rotating square container, so this run swaps in a rotating hexagon with a counter-rotating central obstacle and size-proportional mass, which raises the difficulty while keeping containment an unambiguous pass or fail. Here is the prompt, pasted verbatim into a fresh chat for each model:

Build a single-file HTML page (inline CSS and JS, canvas, no build step, no external libraries, no network) that simulates a few dozen balls of varying sizes bouncing under gravity inside a **slowly rotating hexagonal container**, with a **smaller counter-rotating obstacle at the centre** that the balls also collide with. 
Ball mass should scale with size, so larger balls shove smaller ones around. 
The balls should collide with each other, with the hexagon's walls, and with the central obstacle, and lose a little energy on each collision so the system settles rather than gaining energy over time. 
Both the hexagon and the inner obstacle keep rotating throughout, so the balls should slosh and re-pile as they turn.

Ship it as one working file named `index.html` that starts animating on load. 
Do not install packages. 
Do not open, screenshot, or headless-render the page (no Playwright, Puppeteer, or Chrome). 
Do not ask me clarifying questions — make reasonable assumptions and note them briefly in a comment at the top of the file.

Both models run at high reasoning effort, one attempt each with no retries, inside the same coding agent with the same tooling. Scoring is a pass/fail runnability check first, then a 1 to 5 rubric on physics correctness, stability over 30 seconds, and visual quality.

What GPT-6 Astra produced

Astra passed the runnability check and scored 5/5 on all three rubric axes. It got there in 6 turns and 9 tool calls, and the tool mix is the interesting part: it wrote the file, read it back 5 times, grepped it twice, and patched it twice before declaring it done.

The simulation is correct. 44 balls, mass scaling with area, containment holding for the full 30 seconds while the hexagon turns. Astra sized the inner obstacle large relative to the chamber, so balls keep colliding with it instead of settling somewhere it cannot reach.

It also built an entire editorial page around the simulation, with a serif headline, letterspaced monospace labels, and a telemetry panel reporting live rotation rates. Nobody asked for that, but it looks quite nice in my opinion. It shipped pause and restart controls too, which the prompt never requested, but which made the artifact easier to inspect.

Astra turns the chamber at 0.09 rad/s and the obstacle at -0.16 rad/s, about 3 times slower than Fable on the outer wall and 5 times slower on the obstacle. Easier to watch, and a gentler test of the moving boundary than a faster spin would have been. Since the prompt asks for a slowly rotating container, this is fine.

What Claude Fable 5.1 produced

Fable passed runnability as well, scoring 4 on physics correctness, 5 on stability, and 4 on visual quality. It only needed 2 turns and a single tool call: one write, no re-reads, no patches.

The physics is sound without being flawless. Balls stick slightly to the walls, which is what costs the point on correctness, and within the first few seconds, they settle into the lower corners where the counter-rotating obstacle no longer reaches them. That obstacle sits idle for most of the run.

Fable put its effort into instrumentation rather than presentation. A live HUD reports ball count, frame rate, kinetic energy, and both rotation rates, and clicking anywhere inside the hexagon drops a new ball at that point. I liked that last touch more than I expected to, since it is the quickest way to test the collision code by hand.

Results

Astra takes this one on quality, and the margin is wider on presentation than on physics. Where they part company is what each model decided the job was. Astra read the prompt as a brief to interpret, adding controls, a full editorial layout, and a slower spin that suits watching. Fable read it as a spec to satisfy, satisfied it in one write, and spent its remaining effort on a diagnostic HUD.

This is a single run per model, so treat it as one data point rather than a benchmark. It reports turns rather than tokens or cost, and it only probes from-scratch simulation code, not the computer use or reasoning dimensions covered above.

When to Choose GPT-6 Astra vs Claude Fable 5.1

Because the list rates match, the decision comes down to workload shape, measured token consumption, and the coding agent you already run. The three numbers that should move you are the 4x cache read gap, the 272K surcharge threshold, and the 2.25x measured cost per task.

Choose GPT-6 Astra if...

• Your agents operate real software. At 72.6% on OSWorld 2.0's offline set and 92.7% on ScreenSpot-Pro, Astra is the only one of the two with a published record on grounding and clicking through desktop applications.
• You need polished professional artifacts. Slides, spreadsheets, and CAD output are stated training targets, and the 95.9% BenchCAD score against 84.3% is the widest artifact gap either vendor has published.
• The work is math or physical science. FrontierMath Tier 4 v2 at 97.6% against 87.8%, and GPQA Diamond at 96.0% against 93.7%.
• You are doing defensive security work. 100% on ExploitBench and 86 of 226 FrontierCyber challenges solved put it well clear, provided you can live with capabilities that ship gated behind OpenAI's Daybreak program.
• You already run Codex. Astra's context notes across windows are a Codex feature, and switching harnesses to chase a 3-point index difference rarely pays.
• Cost per task matters more than the top score. $1.67 against $3.76 on Artificial Analysis's Intelligence Index at max effort, and $0.46 at low effort if you can live with 57 instead of 61.

Choose Claude Fable 5.1 if...

• You run long agent loops whose bill is cache reads, not output. At $0.25 per million cached reads against $1.00, the cache-heavy workload above costs $126 instead of $201. That advantage narrows as soon as output tokens start to dominate, which is exactly what the per-task measurements show.
• Your requests are large. Anthropic charges no long-context surcharge, so the 10M-token retrieval workload stays at $150, where Astra climbs to $275, and Astra's cache reads double above the threshold as well.
• You want the best independent reasoning score and will pay for it. Artificial Analysis rates it 66 on the Intelligence Index against 61, and it takes Humanity's Last Exam with tools 65.0% to 57.2%.
• You work in Claude Code. Fable 5.1 in Claude Code is the highest score Artificial Analysis has recorded on its Coding Agent Index at 70, and it defaults to high effort in that surface.

How to Get Started With GPT-6 Astra and Claude Fable 5.1

Two access details will stop you before your first call. Astra is off by default for enterprise workspaces until an administrator turns it on, and Fable 5.1 requires 30-day data retention and is not supported on Priority Tier. Cloud reach is close to level between the two, with Google Cloud currently being the only platform Fable 5.1 has, and GPT-6 Astra does not.

Using GPT-6 Astra and Claude Fable 5.1 in a coding agent

Each model is native to its own harness, Astra in Codex and Fable 5.1 in Claude Code, where you switch with /model claude-fable-5-1 or the --model flag. Through the API the swap is one string, though the SDKs differ.

from anthropic import Anthropic

client = Anthropic()
response = client.messages.create(
    model="claude-fable-5-1",  # OpenAI SDK equivalent: model="gpt-6-astra"
    max_tokens=16000,          # thinking plus response share this budget
    output_config={"effort": "high"},
    messages=[{"role": "user", "content": "Refactor this module..."}],
)
print(response.content[0].text)

Three Fable 5.1 changes will break code written against Fable 5: forced tool selection now returns a 400, earlier models cannot read its thinking blocks, and thinking blocks are bound to the exact history before them. Our Claude Fable 5.1 API tutorial works through all three, and our beginner's guide to the OpenAI API covers the equivalent setup on the other side.

Final Thoughts

Pick on workload shape and measured cost, not on the benchmark tables. GPT-6 Astra is stronger for agents that click through software, do math, or produce client-ready artifacts, and it looks like the cheaper of the two per task despite the identical rate card. Claude Fable 5.1 buys you the highest independent reasoning score available and costs less on very large requests and on cache-dominated loops.

The rate card is the trap in this comparison. Two models at $10 and $50 look interchangeable on price, and the only visible gap, cache reads, favors Anthropic by 4x. Then you measure what each one actually spends to finish a task, and Astra comes in at 44% of Fable 5.1's cost. I would not have predicted that from the pricing pages, and it is the single thing I would check against your own traffic before committing either way.

If you want to work with either model rather than read about them, I recommend our Introduction to Claude Models course for the Anthropic side and Working with the OpenAI API for OpenAI's.