Claude Fable 5.1 and Mythos 5.1 vs GPT-6 Astra: the full comparison

Two frontier launches, three days apart

Anthropic published Claude Fable 5.1 alongside a restricted variant, Claude Mythos 5.1, on 1 September 2026. Two days later OpenAI published GPT-6 Astra. The pairing is unusually clean for comparison purposes: same month, same headline price, similar context capacity, and heavy overlap in the benchmarks each vendor chose to publish. It is also unusually noisy, because the two labs report different suites, different grading modes, and different harnesses for the tests they do share.^[1][2]

The short version. Fable 5.1 is the general-purpose frontier model: coding, agentic work, scientific research, and knowledge work, available immediately on every major cloud. Mythos 5.1 is the same model with reduced safeguards for cybersecurity and life-sciences work, gated behind verification programmes and restricted to US organisations. GPT-6 Astra is a computer-use and long-horizon agent model that OpenAI designated at its Critical cybersecurity threshold, initially limited to organisations inside its Trusted Access and Daybreak programmes.^[1][5]

A note on sourcing before the numbers. Anthropic's launch page is publicly readable and every Fable and Mythos figure below is taken from it or from a secondary summary of it. OpenAI's Astra page blocks automated retrieval, so the Astra figures are drawn from independent write-ups that reproduce the launch tables. Where two sources disagree, both readings appear below rather than one silently winning.^[2][4][5][6]

The API contract side by side

Capacity is close to identical. Both models take about a million tokens of input and emit up to 128,000 tokens, both accept text and images and return text only, and both expose a reasoning-effort dial rather than separate fast and slow model identifiers. Astra extends that dial upward with xhigh and max tiers above high; Fable 5.1 defaults to high on the Claude API, and Anthropic states that its low and medium settings already match or beat the previous Fable 5 generation.^[3][5]

The differences that will actually change your integration are the billing boundary and the availability surface. Astra bills long prompts differently above 272,000 input tokens, so prompt size becomes a pricing cliff rather than a smooth curve. Fable 5.1 shipped on day one across the Claude API, Claude Code, Claude Cowork, claude.ai, AWS Bedrock, Google Cloud, and Microsoft Foundry; Astra started narrower, with ChatGPT access rolling out over several days and API access limited to selected organisations.^[3][6]

Identical sticker price, very different cache economics

Both models cost $10 per million input tokens and $50 per million output tokens. That equivalence hides the most consequential number of the whole comparison: cache reads cost $0.25 per million on Fable 5.1 and $1.00 per million on Astra. Anthropic cut its cache-read rate by 75% specifically to make long agent loops affordable, and estimates real savings of roughly 25% on typical token-billed workloads and up to about 45% on highly agentic ones.^[1][3][6]

The reason is structural. An agent that reruns a large system prompt, a repository map, and a growing scratchpad across forty tool calls reads the same tokens dozens of times. In that shape, cache reads dominate the bill, and a four-times difference in the cache rate outweighs a two-point difference on any benchmark. Astra pushes back in the other direction with a batch and flex tier at half rate, which suits offline evaluation and bulk processing but not interactive agents.^[6]

One trap worth writing into your cost model now: Astra's long-context surcharge is not a rounding error. Cross 272,000 input tokens and the input and cache rates double while output rises by half, so a single oversized prompt can cost more than double what a naive per-token estimate predicts.^[6]

Coding and terminal benchmarks

This is the closest fight on the sheet. On Terminal-Bench 4.0, secondary coverage of the Astra launch puts it at 57.7% against Fable 5.1's 55.8% — a gap of under two points, well inside the range that prompt scaffolding, harness design, and retry policy can move. Mythos 5.1, the same Anthropic weights with looser safeguards, scores 60.9% and tops the column, which tells you how much of a frontier model's measured coding ability is gated by refusal behaviour rather than capability.^[1][4]

Where Fable 5.1 separates itself is scientific and long-horizon terminal work. It more than doubles its predecessor on Terminal-Bench-Science 0.1, moving from 24.7% to 52.6% against Opus 5's 29.0% and GPT-5.6 Sol's 22.4%. AutomationBench shows the same pattern: 31.4% against 17.1% for Fable 5, a near-doubling on a suite built for unattended multi-step execution. Astra's counter-punch lands on DeepSWE v1.1 at 74.1% and on binary reverse-engineering, where SRE-Bench puts it at 88.0% on a single attempt against Sol's 55.9%.^[1][4][5]

Computer use, agents, and long context

Computer use is Astra's strongest public claim, and it is the row you must read most carefully. OpenAI reports 72.6% on OSWorld 2.0 against 65.7% for GPT-5.6 Sol, with time per task falling from roughly 75 minutes to about 40 — a latency improvement of around 47% that matters more for economics than the accuracy delta does. Anthropic reports OSWorld 2.0 twice, at 77.9% partial credit and 41.7% strict, and the strict number is the one that resembles production, where a task either completed or it did not.^[1][4][5]

Those two OSWorld figures are not directly comparable. Anthropic's 77.9% partial and OpenAI's 72.6% offline-graded run use different grading modes on different harnesses, and stacking them into a single leaderboard row produces a conclusion neither lab published. The honest reading is that both models made a large generational jump on desktop control and that Astra additionally bought a substantial latency reduction.

Long-context retrieval is Astra's cleanest win. On OpenAI's MRCR v2 eight-needle test it holds 100% in the 256K–512K band and 96.3% from 512K to 1M, against 91.5% and 73.8% for Sol. If your workload genuinely puts eight hundred thousand tokens into a prompt and expects precise recall, that is a real and measured difference — and also the exact regime where Astra's long-context surcharge doubles your input bill.^[2][5]

Knowledge work, maths, and the index scores

Maths is not close. Astra reports 97.6% on FrontierMath Tier 4 v2 against 87.8% for Fable 5.1, 83.0% for Sol, and 73.2% for Opus 5 — effectively saturating a suite that was designed to be unsaturable. It also posts 96.0% on GPQA Diamond. If your workload is mathematical derivation or graduate-level scientific reasoning, that column is the single most decision-relevant thing on this page.^[4][5]

Knowledge work runs the other way. Fable 5.1 takes GDPval-AA v2 at 1853 Elo against Opus 5's 1824 and Sol's 1711, and leads Humanity's Last Exam both without tools (60.9%) and with them (65.0%). One secondary source places Astra at 57.2% on the tools-enabled variant, which would put it behind both recent Claude generations on that suite; treat the exact figure as unconfirmed, but the direction is consistent across write-ups. Artificial Analysis, an independent aggregator rather than either vendor, scores Fable 5.1 at 65.7 on its Intelligence Index against 61.2 for Astra.^[1][4][6]

That is the shape of the whole comparison in one paragraph. Astra wins the verifiable, single-answer, high-ceiling tests — maths, exploitation, retrieval, desktop control. Fable 5.1 wins the messy, judged, multidisciplinary ones — professional deliverables, research synthesis, tool-assisted reasoning. Which family is 'ahead' depends entirely on which of those two shapes your business actually pays for.

Five ways these tables can mislead you

Every number above is vendor-reported or reproduced from vendor reporting. None of it is a controlled head-to-head run by an independent party on identical harnesses, and there are five specific failure modes to keep in view before any of it reaches a procurement decision.

• Best-of-effort selection. OpenAI reports each model's best score at any reasoning effort. The table therefore says which configuration reached the highest point, not which one reached it at acceptable cost or latency. A 57.7% at max effort and a 55.8% at high effort are not the same purchase.
• Harness dependence. Astra's 99.9% on ARC-AGI-3 comes from a provider adapter harness. Independent testing through stateless API calls reportedly lands between 17% and 63% depending on reasoning tier. The model did not get worse; the scaffolding did.
• Version drift. Gemini 3.8 Flash scores about 90.8% on Terminal-Bench 2.1 and roughly 19.1% on Terminal-Bench 4.0. Same family of benchmark, same model, seventy points apart. A score without a version is not evidence.
• Grading mode. The same OSWorld run yields 77.9% with partial credit and 41.7% strict. Vendors are free to choose; you are not free to compare across the choice.
• Contamination. Astra reports 100% on ExploitBench, but 39.0% on a contamination-controlled variant restricted to June–August 2026 material. The controlled figure is still a large jump over Sol's 5.5% — it is simply the one that means something.

Safeguards, gating, and why Mythos exists

Both labs shipped a capability they consider dangerous, and both responded by splitting access rather than by refusing to ship. Anthropic's answer is a second model: Mythos 5.1 carries the same weights with loosened cybersecurity and life-sciences safeguards, reachable only through its Cyber Verification Program and Life Sciences Verification Program, and only by US organisations. OpenAI's answer is a threshold designation: Astra is the first model it has placed at the Critical cybersecurity level, having discovered two previously unknown V8 vulnerabilities, and standard access refuses exploit-discovery work outright, halting tasks rather than pausing for approval.^[1][5]

Anthropic paired the loosened variant with sharply reduced false positives on the general model — about 60% fewer cybersecurity safeguard interventions and an 85% reduction in false positives on benign biology requests. That is the practical complaint most security and life-sciences teams had about the previous generation, and it is worth more day to day than a benchmark point.^[1]

Astra's alignment numbers are, on the published evidence, the strongest of the group — and this is the one place where a vendor's own safety reporting is also its strongest competitive claim. On computer-use safety, where lower is better, it reports 2.4% against 9.5% for Fable 5.1, 11.5% for Opus 5, and 22.0% for Sol. For any deployment that lets a model click things in a real environment, that column deserves more weight than Terminal-Bench.^[4]

What the same work looks like on low-cost models

The budget tier of September 2026 is not the budget tier of two years ago. Open-weight models now post SWE-bench Verified results within single-digit points of Western frontier systems — DeepSeek V4-Pro at 80.6%, Qwen3.6-27B at 77.2%, with Kimi K3 reported as high as 93.4% on one independent evaluation and GLM-5.2 at 81.0 on Terminal-Bench 2.1. Meanwhile Qwen3.7 Flash lists at three cents per million input tokens, roughly one three-hundredth of a frontier model's input rate.^[8][10][11]

The catch is that the cheap tier's scores cluster on the benchmarks where it was optimised, and collapse on the newest agentic suites. Gemini 3.8 Flash is the cleanest illustration: 90.8% on Terminal-Bench 2.1, about 19.1% on Terminal-Bench 4.0, and priced at $0.75 and $3.75 per million until 31 December 2026, after which both rates double. A model that looks like a frontier substitute on last year's suite is not one on this year's.^[9]

One agentic task, priced across the whole field

Abstract price-per-token comparisons hide the shape of agent traffic. So here is one concrete workload, priced identically for every model. The task: a long-running engineering agent that reads 1,000,000 prompt tokens across about forty model calls, of which 85% are served from cache, and writes 50,000 output tokens. Cache-read rates are used where published; where a provider does not publish one, the model below assumes 10% of the input rate, which is the common industry shape and is flagged as an assumption rather than a fact.

The result spans three orders of magnitude. Fable 5.1 and Astra land within 15% of each other despite the identical sticker price — the gap is entirely the cache-read rate, and it runs in Anthropic's favour. Opus 5, one generation back, does the same run for 58% of Fable's price. And the cheap tier does it for pennies.

• Assumptions: 1M prompt tokens read, 85% cache hit rate, 50K output tokens, single attempt, no tool-invocation fees.
• Published cache rates used for Fable 5.1 ($0.25), Astra ($1.00), and Opus 5 ($0.50); 10% of input rate assumed elsewhere.
• Astra's above-272K surcharge is excluded; a single prompt over that boundary doubles its input and cache lines.
• Gemini 3.8 Flash pricing doubles on 1 January 2027, which moves its row to roughly $0.72 on the same workload.

The number that actually decides the tier: cost per success

Token cost is the wrong denominator. What a business pays for is a completed task, so divide by the pass rate — and then add what a failed run costs in human time. Using Terminal-Bench 4.0 as the pass-rate anchor and assuming $25 of engineer time to triage, roll back, and re-launch each failed attempt, the ranking inverts violently.

Read the third column first. All four frontier and near-frontier options land between $23 and $28 per completed task, which is to say they are indistinguishable given the error bars on a vendor benchmark. Gemini 3.8 Flash, at roughly a twelfth of the token price, costs about four times as much per completed task, because a 19.1% pass rate means more than five attempts and four cleanups on average.

Push that logic to its limit and the conclusion is stark. Solve for the pass rate a completely free model would need to beat Fable 5.1's all-in cost on this task, and the answer is about 48%. Below that, zero token cost cannot compensate for human cleanup. This single arithmetic fact is why frontier pricing survives a market where competent models cost a hundredth as much — and equally why the cheap tier wins outright wherever failures are caught automatically instead of by a person.

• All-in cost = (task cost + $25 failure cost) ÷ pass rate − $25. Pass rates are vendor-reported Terminal-Bench 4.0 figures, not measurements of your workload.
• Change the failure cost to $0.50 — the regime where a test suite catches the error and the agent simply retries — and the table reverses: Fable 5.1 costs about $7.94 per success, Gemini 3.8 Flash about $4.00, and a 10%-pass-rate budget model about $4.64.
• The decision variable is therefore not model quality or token price. It is whether failure is detected by a machine or by a person.

A routing design that uses all three tiers

None of this argues for picking one model. It argues for a cascade, where the expensive model is reserved for the steps whose failures are expensive, and the cheap tier absorbs the volume. The escalation trigger matters more than the tier assignment: a cascade without a defined promotion rule is just a cheap model with extra latency.

How to test this yourself in a week

Every table above should start an evaluation rather than end one. The launch figures tell you which two or three candidates deserve a holdout run; nothing published by either lab tells you how a model behaves on your repository, your documents, or your tool surface.

Build the holdout before you look at any output, and define acceptance in advance. For agentic tasks, score the whole trajectory — tool selection, argument correctness, error recovery, side effects — not just the final answer, because a model that reaches the right result after deleting a file has not succeeded.

• Pin the exact model ID and reasoning effort for every run, and log both. A best-of-effort benchmark row is not reproducible without them.
• Measure cached and uncached traffic separately, and confirm the cache-hit counts rather than assuming them — the entire Fable-versus-Astra cost gap lives in that field.
• Run one band below and one band above 272K input tokens if you use Astra, so the surcharge appears in your numbers instead of your invoice.
• Record p95 latency alongside accuracy. Astra's 47% task-time reduction on OSWorld is worth more than two benchmark points for interactive work.
• Track severe-failure rate separately from average score, and price each severe failure at what it actually costs a human to repair.
• Re-run the suite whenever the model, prompt, harness, or provider default changes, and date every result.

Comparing these models inside Kendr

Kendr keeps model identity, capabilities, context limits, pricing references, and benchmark evidence on inspectable profile pages, with comparison context in Rankings. Use the directory to shortlist candidates and the developer documentation to distinguish a public reference profile from a model your account can actually call — availability is a property of your credentials and the live catalogue, not of a blog post.

The routing table above is also the shape Kendr's managed pools are built around: cheap routes absorb the volume, a frontier route handles the steps where failure is expensive, and the escalation rule is explicit rather than implied.

• Browse the model directory to shortlist candidates by capability and price.
• Use Rankings for comparison context across published evidence.
• Read the developer documentation before assuming a model ID is callable on your plan.

Sources and evidence

Primary and authoritative sources used for factual claims. Company research and executive forecasts are labeled as such in the article.

1. 1
   Claude Fable 5.1 and Claude Mythos 5.1Anthropic · 2026-09-01
2. 2
   Introducing GPT-6 AstraOpenAI · 2026-09-03
3. 3
   Claude Fable 5.1: features, benchmarks and pricingDataCamp · 2026-09
4. 4
   GPT-6 Astra: features, benchmarks and pricingDataCamp · 2026-09
5. 5
   OpenAI releases GPT-6 Astra, a 1.05M-context computer-use modelMarkTechPost · 2026-09-03
6. 6
   GPT-6 Astra: price, access and what the benchmarks showDigital Applied · 2026-09
7. 7
   GPT-6 Astra API pricing, context window and benchmarksLLM Stats · 2026-09
8. 8
   LLM API pricing comparison, September 2026BenchLM · 2026-09
9. 9
   Gemini 3.8 Flash review: benchmarks, pricing and the catcheesel AI · 2026-09
10. 10
    Best open-source coding model 2026Morph · 2026
11. 11
    Cheapest LLM API in 2026, ranked by price per million tokensLLM Calculator · 2026-09