Which model won the tests?

GPT-6 Astra scored 43/50 versus 19/50 in this compatibility-API run.

GPT-6 Astra vs Claude Fable 5.1: โมเดลใดจะชนะ?

Q: รุ่นใดมีหน้าต่างบริบทที่ใหญ่กว่า?

GPT-6 Astra lists 1.05M tokens; Claude Fable 5.1 lists 1M.

2026-09-08
07:58
โอลิเวีย คาร์เตอร์
อัปเดตครั้งสุดท้ายเมื่อวันที่ 8 กันยายน 2026

GPT-6 Astra and Claude Fable 5.1 start at the same headline API prices, but they did not behave the same in our matched compatibility-API tests. Astra returned more usable visible output across reasoning, writing, coding, long-context retrieval, and strategy. Fable tied the long-context accuracy test and wrote credible launch copy, yet several runs were truncated or blank in this particular route.

ลองใช้ GPT Series บน GlobalGPT

บทสรุปสั้นๆ

Best observed overall

GPT-6 Astra · 43/50

Accuracy tie

Long-context retrieval · 10/10 each

ข้อควรระวังหลัก

Compatibility routing may explain Fable’s blank outputs.

Choose GPT-6 Astra when you need a dependable first-pass answer across mixed analytical and technical work. Choose Claude Fable 5.1 when your own endpoint has been validated for visible-output handling and you value its long-context interpretation or measured prose. This is not a universal model ranking: it is one controlled run through Broly Anywhere on September 8, 2026.

GPT-6 Astra vs Claude Fable 5.1 at a glance

สนาม	GPT-6 Astra	Claude Fable 5.1
API ID	gpt-6-astra	claude-fable-5-1
บริบท	1.05 ล้านโทเค็น	1 ล้านโทเค็น
กำลังออกสูงสุด	โทเค็น 128K	โทเค็น 128K
ข้อมูลนำเข้าพื้นฐาน	$10 / 1M	$10 / 1M
Cached input / hit	$1 / 1M	$0.25 / 1M
ผลลัพธ์	$50 / 1M	$50 / 1M
ชุด	50% of standard	$5 input / $25 output per 1M
เหตุผล	ความพยายามในการให้เหตุผลที่ปรับแต่งได้	Adaptive thinking, always on; high default
Observed test score	43/50	19/50

Claude Fable 5.1 model overview with pricing and positioning — Anthropic lists Fable 5.1 as a slower model for demanding reasoning and long-horizon agentic work.

For deeper model context, see our GPT-6 Astra review, Claude Fable 5.1 guide, และ Claude model comparison.

Pricing, caching, context, and limits

Both models list $10 per million base input tokens and $50 per million output tokens. The operational difference is caching. GPT-6 Astra lists $1 per million cached input tokens and a $12.50 cache-write rate. Fable 5.1 lists $0.25 cache hits, $12.50 five-minute writes, and $20 one-hour writes per million tokens. Anthropic says the full 1M context remains at standard pricing; Astra uses higher prices once input passes 272K tokens.

Claude Fable 5.1 API pricing table — Official Fable 5.1 input, output, and cache pricing captured September 8, 2026.

Claude Fable 5.1 context and maximum output limits — The official overview shows a 1M-token context window, 128K maximum output, and June 2026 reliable knowledge cutoff.

Read the full GPT-6 Astra pricing breakdown or compare it with our Claude Fable pricing guide. Provider list prices do not automatically equal GlobalGPT credits or compatibility-layer billing.

Five matched API tests

We sent the same task to each model ID through the same OpenAI-compatible endpoint. Tests 1-4 allowed up to 2,000 output tokens. Test 5 used 1,200 for both after the gateway dropped longer non-streaming requests around 30 seconds. Every score uses five rules defined before testing. A blank result receives no credit for invisible work.

API Test 1 · Formula board

Reasoning: SaaS cost decision

10/10 vs 1/10

Astra completed the financial model; Fable stopped during assumptions.

View exact task

You advise a bootstrapped B2B SaaS. Compare Plan A and Plan B for the next 12 months. Plan A: $18,000 migration now, $2,900/month fixed, 0.08% transaction fee, expected monthly GMV $1.2M for months 1-4 and $1.8M for months 5-12, 99.95% SLA. Plan B: no migration fee, $4,100/month fixed, 0.03% transaction fee, expected conversion is 1.5% lower for the first 3 months, 99.9% SLA. Each hour of outage costs $7,500. Assume SLA downtime is fully realized, a month is 30 days, and conversion loss reduces GMV by 1.5%. Calculate the 12-month expected cost of each plan, show formulas, identify the break-even monthly GMV after month 3, run a sensitivity check at half and double expected outage cost, then recommend one plan. Return sections: Assumptions, Calculations, Sensitivity, Recommendation. Round currency to the nearest dollar. Do not add facts.

GPT-6 Astra

## Assumptions

– Plan B uses the same baseline GMV forecast as Plan A, reduced by 1.5% in months 1–3.
– Expected cost includes migration, fixed fees, transaction fees, outage losses, and conversion-related lost GMV. **Lost GMV is counted dollar-for-dollar as opportunity cost**, not lost profit, because no margin is provided.
– Transaction fees apply to GMV after any conversion reduction.
– SLA downtime is fully realized over 12 × 30 × 24 = **8,640 hours**.
– The break-even calculation after month 3 compares ongoing monthly costs; Plan A’s migration fee is then a sunk cost.

## Calculations

### GMV and conversion loss

**Plan A annual GMV:**

\[
4(\$1{,}200{,}000)+8(\$1{,}800{,}000)=\$19{,}200{,}000
\]

**Plan B conversion-related GMV loss:**

\[
3(\$1{,}200{,}000)(1.5\%)=\$54{,}000
\]

**Plan B annual GMV:**

\[
\$19{,}200{,}000-\$54{,}000=\$19{,}146{,}000
\]

### SLA outage costs

**Plan A:**

\[
8{,}640(1-99.95\%)=4.32\text{ hours}
\]

\[
4.32(\$7{,}500)=\$32{,}400
\]

**Plan B:**

\[
8{,}640(1-99.9\%)=8.64\text{ hours}
\]

\[
8.64(\$7{,}500)=\$64{,}800
\]

### Total 12-month expected cost

| Cost component | Plan A | Plan B |
|—|—:|—:|
| Migration | $18,000 | $0 |
| Fixed fees | $34,800 | $49,200 |
| Transaction fees | $15,360 | $5,744 |
| Outage losses | $32,400 | $64,800 |
| Conversion-related lost GMV | $0 | $54,000 |
| **Total expected cost** | **$100,560** | **$173,744** |

Formulas:

\[
C_A=\$18{,}000+12(\$2{,}900)+0.0008(\$19{,}200{,}000)+\$32{,}400
=\boxed{\$100{,}560}
\]

\[
C_B=12(\$4{,}100)+0.0003(\$19{,}146{,}000)+\$64{,}800+\$54{,}000
=\boxed{\$173{,}744}
\]

**Plan A saves $73,184 over 12 months.**

### Break-even monthly GMV after month 3

Let \(G\) be monthly GMV. Monthly outage costs are $2,700 for A and $5,400 for B.

\[
\$2{,}900+0.0008G+\$2{,}700
=
\$4{,}100+0.0003G+\$5{,}400
\]

\[
0.0005G=\$3{,}900
\]

\[
\boxed{G=\$7{,}800{,}000\text{ per month}}
\]

Including outage losses, **A is cheaper below $7.8 million monthly GMV; B is cheaper above it**.

## Sensitivity

Holding everything else constant and changing the cost per outage hour:

| Outage cost per hour | Plan A annual cost | Plan B annual cost | Plan A savings |
|—|—:|—:|—:|
| Half: $3,750 | $84,360 | $141,344 | $56,984 |
| Base: $7,500 | $100,560 | $173,744 | $73,184 |
| Double: $15,000 | $132,960 | $238,544 | $105,584 |

Plan A remains cheaper in all three scenarios.

## Recommendation

**Choose Plan A**, provided the business can fund the **$18,000 upfront migration**. It has the lower 12-month expected cost, saves **$73,184** in the base case, and remains preferable at both half and double the assumed outage cost.

Its ongoing break-even threshold of **$7.8 million monthly GMV** is well above the forecast of $1.2–$1.8 million. Even excluding conversion-related lost GMV from the comparison, Plan A is cheaper by **$19,184** over the year.

HTTP 200 · 28.98s · 242 input · 1,427 completion · score 10/10

Claude Fable 5.1

# Assumptions
– Horizon: 12 months; 1 month = 30 days = 720 hours.
– SLA downtime fully realized: Plan A downtime = (1 − 0.9995) × 720 = 0.36 h/month; Plan B = (1 − 0.999) × 720 = 0.72 h/month.
– Outage cost = $7,500/hour (base case).
– GMV: $1,200,000/month for months 1–4; $1,800,000/month for months 5–12.
– Plan B conversion loss: GMV redu

HTTP 200 · 26.01s · 357 input · 2,000 completion · score 1/10

API Test 2 · Constraint checklist

Writing: product launch edit

10/10 vs 8/10

Both produced usable copy, but Fable introduced details absent from the notes.

View exact task

Turn these messy product notes into a 220-280 word launch announcement for IT administrators. Notes: Acme Relay 3.0; policy simulator before deploy; rollback is one click; audit export CSV and JSON; SSO now supports Okta and Entra ID; beta users cut failed policy pushes from 14% to 3%; data is from 42 beta workspaces over 30 days and is not a guarantee; available today on Business and Enterprise; Starter gets read-only simulator next quarter; do not claim zero downtime; tone calm, specific, credible; CTA is "Open the policy simulator." Include a headline, a two-sentence lead, three bullet points, the beta-data caveat, and the exact CTA. Avoid superlatives, hype, and invented quotations.

GPT-6 Astra

# Acme Relay 3.0: Policy Simulation Before Deployment

Acme Relay 3.0 is available today on Business and Enterprise, bringing pre-deployment policy simulation, one-click rollback, and additional options for audit exports and single sign-on. For IT administrators managing policy changes, this release puts the focus on reviewing changes before deployment and making recovery and audit tasks more straightforward.

The release includes three updates to the policy workflow:

– **Simulate policies before deployment.** Use the policy simulator to review a proposed policy before deploying it. This adds a review step ahead of deployment, rather than making a live push the first opportunity to evaluate a change.
– **Roll back with one click.** When a deployed policy needs to be reversed, one-click rollback provides a direct recovery action. Simulation and rollback address different parts of the workflow: reviewing a change beforehand and reversing it afterward.
– **Export audit data as CSV or JSON.** Choose between two export formats for your audit processes. Administrators can select the format that fits their existing reporting or data-handling requirements.

SSO now supports Okta and Microsoft Entra ID, adding identity-provider options for teams using those services.

Across 42 beta workspaces over 30 days, beta users reduced failed policy pushes from 14% to 3%. These results reflect that beta sample and observation period; they are not a guarantee of results in your environment.

Business and Enterprise teams can access the release today. Starter will receive a read-only version of the policy simulator next quarter.

Open the policy simulator.

HTTP 200 · 15.47s · 165 input · 500 completion · score 10/10

Claude Fable 5.1

**Acme Relay 3.0 is available today for Business and Enterprise**

Acme Relay 3.0 adds a policy simulator, one-click rollback, and expanded audit exports to help administrators deploy configuration changes with fewer surprises. Business and Enterprise workspaces can use these features starting today.

Here is what changed for administrators:

– **Policy simulator before deploy.** Run a proposed policy against your current workspace state and review the projected effects on users, groups, and devices before anything is pushed.
– **One-click rollback.** If a deployed policy behaves unexpectedly, revert to the previous version from the deployment record. Rollback restores the prior policy state; a brief disruption during the transition is still possible.
– **Audit export in CSV and JSON.** Export policy history, deployment events, and simulator runs for spreadsheet review or ingestion into your logging pipeline.

Single sign-on now supports Okta and Microsoft Entra ID. Administrators using either provider can enable it from the identity settings page without a separate connector.

During the beta, participating administrators reduced failed policy pushes from 14% to 3%. That figure comes from 42 beta workspaces over 30 days. It reflects those environments and their change volume, and it is not a guarantee of results in yours.

Starter workspaces will receive a read-only version of the policy simulator next quarter. We will publish the rollout date in the release notes.

If you manage a Business or Enterprise workspace, a reasonable first step is to pick one existing policy, run it through the simulator against a small test group, and compare the projected outcome with what you expected.

**Open the policy simulator.**

HTTP 200 · 26.01s · 263 input · 1,881 completion · score 8/10

API Test 3 · Bug and test console

Coding: TypeScript concurrency

9/10 vs 0/10

Astra returned a strong diagnosis and partial tests; Fable returned no visible answer in this route.

View exact task

Diagnose and fix this TypeScript function. It should fetch every URL with at most 3 requests in flight, preserve input order, reject after all started work settles if any request failed, and never start new work after the first failure. Explain the race conditions, provide corrected TypeScript without external packages, and include four focused tests as code.

async function fetchAll(urls: string[]): Promise<string[]> {
  const results: string[] = [];
  let index = 0;
  async function worker() {
    while (index < urls.length) {
      const current = index;
      const response = await fetch(urls[index++]);
      results.push(await response.text());
      if (!response.ok) throw new Error(`HTTP ${response.status} at ${current}`);
    }
  }
  await Promise.all([worker(), worker(), worker()]);
  return results;
}

GPT-6 Astra

### Diagnosis

– **Results are out of order.** `push()` records completion order, not input order.
– **`Promise.all()` rejects too early.** It does not wait for the other workers to finish after one rejects, nor does it cancel them.
– **Other workers keep starting requests after a failure.** There is no shared stop flag.
– **HTTP failure detection is delayed.** Checking `response.ok` after `response.text()` lets other workers start more requests while an already-known HTTP error’s body is being read.
– **The index increment is not itself a race.** JavaScript executes the synchronous check-and-increment without interleaving another worker. The problematic interleaving happens across `await` boundaries.

Here, “first failure” means the first failure **observed by this function**. A request remains in flight through its body read.

### Corrected TypeScript

The optional fetcher makes testing possible without network access or external packages.

“`ts
type FetchResponse = Pick<Response, "ok" | "status" | "text">;
type Fetcher = (url: string) => Promise<FetchResponse>;

async function fetchAll(
  urls: string[],
  fetcher: Fetcher = fetch,
): Promise<string[]> {
  const results = new Array<string>(urls.length);
  let next = 0;
  let failed = false;
  let firstError: unknown;

  async function worker(): Promise<void> {
    while (!failed && next < urls.length) {
      // No await between checking the stop flag and starting this request.
      const current = next++;

      try {
        const response = await fetcher(urls[current]);

        // Detect HTTP failure immediately; don’t start reading its body.
        if (!response.ok) {
          throw new Error(`HTTP ${response.status} at ${current}`);
        }

        results[current] = await response.text();
      } catch (error) {
        if (!failed) {
          failed = true;
          firstError = error;
        }
        return;
      }
    }
  }

  // Workers capture request failures, so this waits for every worker.
  await Promise.all(
    Array.from({ length: Math.min(3, urls.length) }, () => worker()),
  );

  // Keep a separate boolean: a promise can reject with undefined.
  if (failed) throw firstError;

  return results;
}
“`

Once a worker observes a failure, no worker can claim another URL. Already-started fetches and body reads are allowed to settle; only then does `fetchAll` reject with the first observed error.

### Four focused tests

Run alongside the implementation in an environment with TypeScript’s DOM types. The fetcher is fully mocked.

“`ts
function assert(condition: unknown, message: string): asserts condition {
  if (!condition) throw new Error(message);
}

function deferred<T>() {
  let resolve!: (value: T | PromiseLike<T>) => void;
  let reject!: (reason?: unknown) => void;

  const promise = new Promise<T>((res, rej) => {
    resolve = res;
    reject = rej;
  });

  return { promise, resolve, reject };
}

// Let all currently queued promise continuations run.
const tick = () =>
  new Promise<void>((resolve) => setTimeout(resolve, 0));

function ok(value: string): FetchResponse {
  return {
    ok: true,
    status: 200,
    text: async () => value,
  };
}

function fixture(count: number) {
  const requests = Array.from(
    { length: count },
    () => deferred<FetchResponse>(),
  );
  const calls: number[] = [];

  const fetcher: Fetcher = (url) => {
    const index = Number(url);
    calls.push(index);
    return requests[index].promise;
  };

  const state: {
    settled: boolean;
    rejected: boolean;
    value?: string[];
    error?: unknown;
  } = {
    settled: false,
    rejected: false,
  };

  // Attach both handlers immediately to avoid unhandled rejections.
  const done = fetchAll(
    requests.map((_, index) => String(index)),
    fetcher,
  ).then(
    (value) => {
      state.value = value;
      state.settled = true;
    },
    (error) => {
      state.error = error;
      state.rejected = true;
      state.settled = true;
    },
  );

  return { requests, calls, state, done };
}

// 1. At most three requests, refill freed slots, preserve input order.
async function testConcurrencyAndOrder() {
  const f = fixture(5);

  assert(f.calls.join() === "0,1,2", "Must start exactly three requests");

  f.requests[2].resolve(ok("C"));
  await tick();
  assert(f.calls.join() === "0,1,2,3", "Must refill only one freed slot");

  f.requests[0].resolve(ok("A"));
  await tick();
  assert(f.calls.join() === "0,1,2,3,4", "Must eventually start every URL");

  f.requests[4].resolve(ok("E"));
  f.requests[3].resolve(ok("D"));
  await tick();
  assert(!f.state.settled, "Must wait for the remaining request");

  f.requests[1].resolve(ok("B"));
  await f.done;

  assert(!f.state.rejected, "Expected success");
  assert(f.state.value?.join() === "A,B,C,D,E", "Wrong result order");
}

// 2. HTTP failure stops scheduling immediately and waits for a body read.
async function testHttpFailure() {
  const f = fixture(5);
  const body = deferred<string>();
  let bodyStarted = false;
  let errorBodyRead = false;

  f.requests[1].resolve({
    ok: true,
    status: 200,
    text: () => {
      bodyStarted = true;
      return body.promise;
    },
  });
  await tick();
  assert(bodyStarted, "Successful response body must be read");
  assert(f.calls.length === 3, "Body read must retain its worker slot");

  f.requests[0].resolve({
    ok: false,
    status: 503,
    text: async

HTTP 200 · 41.54s · 181 input · 2,000 completion · score 9/10

Claude Fable 5.1

[No visible answer was returned by this compatibility route despite reported completion-token usage.]

HTTP 200 · 29.44s · 277 input · 2,000 completion · score 0/10

API Test 4 · Context locator

Long context: planted amendment

10/10 vs 10/10

Both found and quoted the controlling Section 417 amendment.

View exact task

You will receive a long synthetic operations dossier after this instruction. Most sections say the EU retention period is 30 days. One later signed amendment says EU support-ticket attachments must be retained for 45 days starting 2026-10-01, while ticket message text remains 30 days. Find the controlling rule, quote the exact amendment sentence, name the section and effective date, explain why it overrides the repeated 30-day statements, and return JSON with keys controlling_rule, exact_quote, section, effective_date, superseded_claims, confidence. Use only the dossier. Do not infer missing text.

[The identical synthetic dossier contained approximately 100,000 GPT-route tokens and one signed amendment in Section 417.]

GPT-6 Astra

{
  "controlling_rule": "Starting 2026-10-01, EU support-ticket attachments must be retained for 45 days; ticket message text remains subject to 30-day retention.",
  "exact_quote": "EU support-ticket attachments must be retained for 45 days starting 2026-10-01, while ticket message text remains subject to the 30-day retention period.",
  "section": "Section 417 – Signed Retention Amendment",
  "effective_date": "2026-10-01",
  "superseded_claims": {
    "sections": "Sections 1–99",
    "exact_claim": "The standard EU retention period is 30 days.",
    "scope": "Superseded for EU support-ticket attachments starting 2026-10-01, not for ticket message text.",
    "reason": "Section 417 is signed by the Director of Compliance and explicitly states: \"This signed amendment controls over earlier general retention statements.\" The repeated general statements therefore do not override the amendment’s specific attachment rule."
  },
  "confidence": "High: the rule, effective date, and precedence are explicitly stated in the dossier."
}

HTTP 200 · 14.96s · 99,495 input · 244 completion · score 10/10

Claude Fable 5.1

**Controlling rule:** Section 417 (Signed Retention Amendment), effective 2026-10-01, signed by the Director of Compliance.

**Exact amendment sentence:**
"EU support-ticket attachments must be retained for 45 days starting 2026-10-01, while ticket message text remains subject to the 30-day retention period."

**Why it overrides the repeated 30-day statements:**
– Sections 1–99 each state only a *general* rule ("The standard EU retention period is 30 days") with no effective date, signature, or data-type specificity.
– Section 417 is (a) a signed amendment, (b) later in the dossier, (c) carries an explicit effective date, (d) is more specific (it targets EU support-ticket attachments as a distinct data category), and (e) contains its own supremacy clause: "This signed amendment controls over earlier general retention statements."
– The amendment is narrow: it changes retention only for EU support-ticket *attachments* (to 45 days from 2026-10-01). It expressly leaves ticket *message text* at 30 days, so the general 30-day rule is superseded only to the extent it would otherwise apply to attachments on or after 2026-10-01. The dossier does not state what applies to other EU data categories beyond the general 30-day rule, and it jumps from Section 99 to Section 417 with no intervening text, so nothing is inferred about missing sections.

“`json
{
  "controlling_rule": "From 2026-10-01, EU support-ticket attachments must be retained for 45 days; EU support-ticket message text remains subject to the 30-day retention period. The general 30-day EU retention statements continue to apply except as modified for attachments by this amendment.",
  "exact_quote": "EU support-ticket attachments must be retained for 45 days starting 2026-10-01, while ticket message text remains subject to the 30-day retention period.",
  "section": "Section 417 – Signed Retention Amendment (signed by: Director of Compliance)",
  "effective_date": "2026-10-01",
  "superseded_claims": [
    "Sections 1 through 99: ‘The standard EU retention period is 30 days.’ — superseded only insofar as applied to EU support-ticket attachments on or after 2026-10-01; remains in force for ticket message text and is not otherwise altered by the dossier."
  ],
  "confidence": 0.97
}
“`

HTTP 200 · 22.24s · 295,912 input · 1,053 completion · score 10/10

API Test 5 · Priority matrix

Strategy: 90-day GTM plan

4/10 vs 0/10

Astra began a grounded plan but was truncated; Fable returned no visible answer.

View exact task

Create a 90-day go-to-market strategy from these notes: developer API product; 18 interviews; strongest pain is retry handling; second pain is cost visibility; 7 teams use homegrown queues; 5 want EU data residency; 3 demand SOC 2 before trial; current activation 21%; users who send 100 requests in day one retain at 62% versus 19% otherwise; engineering can ship two medium projects; marketing has $24,000; sales has one AE; no paid search history; community has 8,400 members; proposed ideas include SDK retry middleware, cost dashboard, EU region, SOC 2, paid search, webinar, templates. Prioritize exactly four initiatives. Return a table with initiative, evidence, owner, week range, budget, success metric, dependency, and risk; then list rejected ideas with reasons; then give weekly leading indicators and stop/continue thresholds. Total allocated marketing budget must not exceed $24,000. Clearly separate observed facts from assumptions.

GPT-6 Astra

## Strategy: lead with reliable retries, then make costs visible

Concentrate the next 90 days on developer teams managing retries themselves. Use the existing community and one AE to recruit users, improve first-day value, and convert successful pilots—not to launch a broad acquisition campaign.

### Facts versus assumptions

**Observed facts**
– Across 18 interviews, retry handling was the strongest pain; cost visibility was second.
– Seven teams use homegrown queues; five want EU residency; three require SOC 2 before trial. These groups may overlap.
– Current activation is 21%.
– Users sending 100 requests on day one retain at 62%, versus 19% otherwise. This is an association, not proof that increasing requests causes retention.
– Capacity: two medium engineering projects, $24,000 marketing budget, one AE, and an 8,400-member community.
– There is no paid-search history.

**Planning assumptions—not established facts**
– SDK retry middleware and a narrowly scoped cost dashboard each fit one medium engineering project.
– Existing telemetry can measure activation, first-day requests, and cohort retention. Confirm this in week 1.
– The community contains relevant prospects and permits promotional activity; membership is not equivalent to reachable demand.
– Marketing can create documentation-based templates and run webinars without another engineering project.
– All targets below are proposed operating thresholds. Marketing allocations cover campaign execution, content, research, and pilot enablement—not engineering or employee salaries.
– Week 13 covers days 85–90. Preserve the current activation definition and establish the retention measurement window before comparisons.

## Exactly four prioritized initiatives

| Initiative | Evidence | Owner | Week range | Marketing budget | Success metric | Dependency | Risk |
|—|—|—|—|—:|—|—|—|
| **1. Ship SDK retry middleware** — bounded retries, backoff, clear retryability rules, and safe examples in one priority SDK | Retry handling ranks first; seven interviewees use homegrown queues | Engineering lead | **W1–5** build and beta; **W6–13** adoption and fixes | **$4,000** — developer testing and launch content | By W6, **5 teams** use it in real workflows; by W13, **≥60%** of beta teams with a full two-week observation window still use it | Validate priority SDK; confirm API idempotency and error semantics; recruit beta teams | Retry amplification, duplicate operations, or added spend. Do not imply that retry middleware replaces an entire queue |
| **2. Ship a minimum cost-visibility dashboard** — usage, estimated cost, and top cost drivers; defer forecasting and complex alerts | Cost visibility ranks second; willingness to adopt or pay is unproven | Engineering lead | **W1–3** discovery; **W6–10** build; **W11–13** pilot | **$3,000** — research and onboarding materials | Before build, **5 of 8** target users confirm a recurring decision the dashboard would support; after launch, **≥50% of 10 pilot teams** use it in two consecutive weeks | Reliable usage/pricing data; retry project completes within its engineering slot | Billing mismatches undermine trust; a dashboard may attract curiosity without repeated value |
| **3. Run a community-to-activation program** — workflow templates, onboarding messages, and two hands-on webinars centered on retries | Community has 8,400 members; activation is 21%; first-day 100-request users have higher observed retention | Marketing lead / DevRel | **W1–2** instrumentation and content; **W3–13** weekly recruitment and iteration |

HTTP 200 · 34.27s · 210 input · 1,200 completion · score 4/10

Claude Fable 5.1

[No visible answer was returned by this compatibility route despite reported completion-token usage.]

HTTP 200 · 21.09s · 338 input · 1,200 completion · score 0/10

คุณควรเลือกรุ่นไหนดี?

Reasoning and operational decisions: Astra was much more complete in this run.
Writing: both were usable; Astra was tighter to source notes, while Fable sounded polished but invented interface specifics.
การเขียนโค้ด: Astra produced the stronger visible diagnosis. Compare broader patterns in our Claude vs ChatGPT coding guide.
Long documents: both found the contradiction exactly. Token accounting differed dramatically, so test cost with your own documents.
Agentic strategy: neither completed the constrained task within the final cap; Astra still returned usable partial work.

OpenAI Introducing GPT-6 Astra video cover — OpenAI presents GPT-6 Astra in its official launch video; this is provider framing, not independent benchmark evidence. Watch on YouTube.

Official Introducing Claude Fable 5.1 video cover — The official Fable 5.1 launch demonstration provides product context, not controlled comparative proof. Watch on YouTube.

Independent GPT-6 Astra and Claude Fable 5.1 comparison video cover — A third-party comparison with task chapters. Its conclusions remain attributed to the creator. Watch on YouTube.

For another benchmark frame, see GPT-6 Astra vs GPT-5.6 Sol หรือ Claude Fable 5 vs GPT-5.5.

How to compare both in GlobalGPT

GlobalGPT exposes separate landing routes for both models. Use the same prompt, output constraint, and source material, then judge visible correctness before style. Record latency and token usage when the interface or API returns them. For long documents, measure each route independently because identical text may tokenize differently.

GPT-6 Astra selected in the GlobalGPT interface — The GPT-6 Astra model workspace on GlobalGPT.

Claude Fable 5.1 selected in the GlobalGPT interface — Claude Fable 5.1 available in the same platform environment.

Try GPT-6 Astra

Try Claude Fable 5.1

คำถามที่มักถูกถาม

Is GPT-6 Astra cheaper than Claude Fable 5.1?

Their base input and output list prices are the same. Cache-hit pricing and long-context rules differ, while GlobalGPT billing may use separate credits.

รุ่นใดมีหน้าต่างบริบทที่ใหญ่กว่า?

GPT-6 Astra lists 1.05M tokens and Claude Fable 5.1 lists 1M. Both list a 128K maximum output.

Which was better for coding?

GPT-6 Astra produced the stronger visible answer in our single TypeScript test. Fable returned no visible content through this compatibility route, so this is not a provider-native verdict.

Which was better for long-context retrieval?

They tied at 10/10, locating and quoting the same planted amendment. Fable reported nearly three times as many input tokens for the identical dossier.

Why did Fable score lower?

Several Fable calls consumed completion tokens but returned truncated or blank visible content. That may be a compatibility-layer or output-serialization issue.

Can I use both models in GlobalGPT?

Yes. Both dedicated GlobalGPT routes were live when checked on September 8, 2026.

คำตัดสินสุดท้าย

GPT-6 Astra is the safer default from this test set: it returned more complete, usable work and finished with 43/50 versus 19/50. Claude Fable 5.1 still matched Astra on the long-context accuracy task and produced solid marketing copy. Before rejecting Fable, repeat the tests through your production endpoint; two blank outputs are as much a warning about integration behavior as model behavior.

แชร์โพสต์:

GPT-6 Astra vs Claude Fable 5.1: โมเดลใดจะชนะ?

บทสรุปสั้นๆ

GPT-6 Astra vs Claude Fable 5.1 at a glance

Pricing, caching, context, and limits

Five matched API tests

Reasoning: SaaS cost decision

Writing: product launch edit

Coding: TypeScript concurrency

Long context: planted amendment

Strategy: 90-day GTM plan

คุณควรเลือกรุ่นไหนดี?

How to compare both in GlobalGPT

คำถามที่มักถูกถาม

Is GPT-6 Astra cheaper than Claude Fable 5.1?

รุ่นใดมีหน้าต่างบริบทที่ใหญ่กว่า?

Which was better for coding?

Which was better for long-context retrieval?

Why did Fable score lower?

Can I use both models in GlobalGPT?

คำตัดสินสุดท้าย

โพสต์ที่เกี่ยวข้อง

Grok 4 API ราคา: ค่าใช้จ่าย, ข้อจำกัด และวิธีเริ่มต้น

อธิบายเกี่ยวกับเครดิตวิดีโอ AI: งบประมาณสำหรับคลิปที่เสร็จสมบูรณ์

การออกแบบเคลื่อนไหวด้วย AI: การรวมวิดีโอที่สร้างขึ้นกับโค้ดที่สามารถแก้ไขได้

วิธีที่ GlobalGPT ทดสอบโมเดล AI: คุณภาพ ความเร็ว และค่าใช้จ่าย

GPT-6 Astra vs Claude Fable 5.1: โมเดลใดจะชนะ?

บทสรุปสั้นๆ

GPT-6 Astra vs Claude Fable 5.1 at a glance

Pricing, caching, context, and limits

Five matched API tests

Reasoning: SaaS cost decision

Writing: product launch edit

Coding: TypeScript concurrency

Long context: planted amendment

Strategy: 90-day GTM plan

คุณควรเลือกรุ่นไหนดี?

How to compare both in GlobalGPT

คำถามที่มักถูกถาม

Is GPT-6 Astra cheaper than Claude Fable 5.1?

รุ่นใดมีหน้าต่างบริบทที่ใหญ่กว่า?

Which was better for coding?

Which was better for long-context retrieval?

Why did Fable score lower?

Can I use both models in GlobalGPT?

คำตัดสินสุดท้าย

โพสต์ที่เกี่ยวข้อง

Grok 4 API ราคา: ค่าใช้จ่าย, ข้อจำกัด และวิธีเริ่มต้น

อธิบายเกี่ยวกับเครดิตวิดีโอ AI: งบประมาณสำหรับคลิปที่เสร็จสมบูรณ์

การออกแบบเคลื่อนไหวด้วย AI: การรวมวิดีโอที่สร้างขึ้นกับโค้ดที่สามารถแก้ไขได้

วิธีที่ GlobalGPT ทดสอบโมเดล AI: คุณภาพ ความเร็ว และค่าใช้จ่าย

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