哪款機型在測試中勝出？

在這次相容性 API 測試中，GPT-6 Astra 獲得 43/50 分，而對照組則為 19/50 分。.

GPT-6 Astra 對決 Claude Fable 5.1：哪款模型更勝一籌？

Q: GPT-6 Astra 的價格是否比 Claude Fable 5.1 更便宜？

它們的基本輸入和輸出定價相同。快取定價與長上下文規則則有所不同。.

Q: 哪個模型的上下文視窗較大？

GPT-6 Astra 掛牌 105 萬枚代幣；Claude Fable 5.1 掛牌 100 萬枚。.

2026-09-08
07:58
奧莉維亞·卡特
最後更新日期：2026年9月8日

GPT-6 Astra 與 Claude Fable 5.1 的標示 API 價格起點相同，但在我們的相容性 API 對比測試中，兩者的表現卻不盡相同。. 在推理、寫作、編碼、長文脈絡檢索及策略制定等領域，Astra 產出了更多可用的可見輸出結果。Fable 在長文脈絡準確度測試中與 Astra 並列第一，並寫出了具說服力的宣傳文案，但在這條特定路徑中，其數次運行結果卻出現截斷或空白的情況。.

在 GlobalGPT 上試用 GPT 系列

簡評

整體而言表現最佳

GPT-6 Astra · 43/50

平手（準確度）

長上下文檢索 · 每題 10/10

主要注意事項

相容性路由可能是導致 Fable 輸出為空的原因。.

當您需要在兼具分析與技術性質的工作中獲得可靠的第一輪答案時，請選擇 GPT-6 Astra。若您的終端裝置已通過可見輸出處理的驗證，且您重視其長上下文解讀能力或精準的行文風格，請選擇 Claude Fable 5.1。此並非通用模型排名：這是 2026 年 9 月 8 日透過 Broly Anywhere 進行的一次受控測試。.

GPT-6 Astra 與 Claude Fable 5.1 規格一覽

領域	GPT-6 Astra	Claude Fable 5.1
API 識別碼	gpt-6-astra	克勞德寓言 5-1
上下文	1.05M 代幣	100萬枚代幣
最大輸出	128K 代幣	128K 代幣
基礎輸入	$10 / 1M	$10 / 1M
快取輸入／命中	$1 / 1M	$0.25 / 1M
輸出	$50 / 1M	$50 / 1M
批次	50% 標準規格	每 1M 包含 $5 輸入 / $25 輸出
推理	可配置的推理工作量	適應性思維，時刻保持；高預設值
實際測驗分數	43/50	19/50

Claude Fable 5.1 型號概覽：價格與定位 — Anthropic 將 Fable 5.1 列為適用於高要求推理及長時程代理任務的較慢模型。.

如需更深入了解模型背景，請參閱我們的 GPT-6 Astra 評測, Claude Fable 5.1 指南, 以及 Claude 模型比較.

定價、快取、上下文與限制

這兩款模型均標示每百萬個基礎輸入令牌的緩存命中率為 $10，每百萬個輸出令牌的緩存命中率為 $50。兩者的運作差異在於緩存機制。GPT-6 Astra 標示每百萬個緩存輸入令牌的緩存命中率為 $1，緩存寫入速率為 $12.50。 Fable 5.1 則標示每百萬個 token 的快取命中率為 $0.25，每五分鐘寫入率為 $12.50，每小時寫入率為 $20。 Anthropic 表示完整的 100 萬個上下文仍維持標準定價；當輸入超過 27.2 萬個標記時，Astra 將採用較高的定價。.

Claude Fable 5.1 API 定價表 — 2026年9月8日記錄的 Fable 5.1 官方輸入、輸出及快取定價。.

Claude Fable 5.1 的應用情境與最大輸出限制 — 官方概述顯示，其上下文視窗為 100 萬個代幣，最大輸出量為 12.8 萬，且可靠知識的截止日期為 2026 年 6 月。.

閱讀全文 GPT-6 Astra 價格明細或與我們的 Claude Fable 定價指南. 服務供應商的定價並不自動等同於 GlobalGPT 積分或相容性層計費。.

五項匹配的 API 測試

我們透過同一個相容於 OpenAI 的端點，將相同的任務傳送至每個模型 ID。測試 1 至 4 允許最多 2,000 個輸出標記。測試 5 則將兩者皆設為 1,200，因為閘道器會在約 30 秒後中斷較長的非串流請求。每項評分均採用測試前定義的五項規則。若結果為空白，則其隱形工作將不獲計分。.

API 測試 1 · 公式板

推理：SaaS 成本決策

10/10 對 1/10

Astra 完成了財務模型；Fable 則在假設設定階段停滯不前。.

檢視確切的任務

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 · 28.98 秒 · 242 項輸入 · 1,427 項完成 · 得分 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 · 26.01 秒 · 357 個輸入 · 2,000 個完成 · 分數 1/10

API 測試 2 · 限制條件檢查清單

撰稿：產品發表稿編輯

10/10 對 8/10

兩者都產出了可用的稿件，但《Fable》補充了筆記中未提及的細節。.

檢視確切的任務

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 · 15.47 秒 · 165 個輸入 · 500 個完成 · 得分 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 · 26.01 秒 · 263 項輸入 · 1,881 項完成 · 得分 8/10

API 測試 3 · 錯誤與測試控制台

程式設計：TypeScript 並行處理

9/10 對 0/10

Astra 回傳了明確的診斷結果及部分測試結果；Fable 在此路徑中則未回傳任何可見的結果。.

檢視確切的任務

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 · 41.54 秒 · 181 項輸入 · 2,000 項完成 · 分數 9/10

Claude Fable 5.1

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

HTTP 200 · 29.44 秒 · 277 項輸入 · 2,000 項完成 · 分數 0/10

API 測試 4 · 上下文定位器

長篇背景：種植修正案

10/10 對 10/10

兩者均查閱並援引了具有約束力的第417條修正案。.

檢視確切的任務

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 · 14.96 秒 · 99,495 個輸入 · 244 個完成 · 得分 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 · 22.24 秒 · 295,912 個輸入 · 1,053 個完成 · 得分 10/10

API 測試 5 · 優先級矩陣

策略：90 天市場進入計畫

4/10 對 0/10

Astra 啟動了一項務實的計畫，但中途被中斷；Fable 則未給予任何明確的回應。.

檢視確切的任務

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 · 34.27 秒 · 210 項輸入 · 1,200 項完成 · 得分 4/10

Claude Fable 5.1

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

HTTP 200 · 21.09 秒 · 338 項輸入 · 1,200 項完成 · 分數 0/10

您應該選擇哪一款型號呢？

推理與運作決策： 在這次試跑中，Astra 的表現要完整得多。.
寫作： 兩者都可使用；Astra 更貼近原始樂譜，而 Fable 雖然聽起來更為淬鍊，但對介面細節的處理則略顯主觀。.
編碼： Astra 提供了更明確的視覺診斷結果。請比較我們 Claude 與 ChatGPT 編碼指南.
長篇文件： 兩者都精確地發現了這項矛盾。代幣核算方式存在顯著差異，因此請使用您自己的文件進行測試。.
代理策略： 兩者皆未能在最終期限內完成受限任務；但 Astra 仍回傳了可用的部分成果。.

OpenAI GPT-6 Astra 介紹影片封面 — OpenAI 在其官方發布影片中展示了 GPT-6 Astra；此為供應商所提供的敘述，並非獨立的基準測試證據。. 在 YouTube 上觀看.

官方發布 Claude Fable 5.1 影片封面 — Fable 5.1 的官方發表示範旨在說明產品背景，而非提供經控管的比較性證明。. 在 YouTube 上觀看.

獨立製作的 GPT-6 Astra 與 Claude Fable 5.1 比較影片封面 — 一份針對「任務」章節的第三方比較分析。其結論仍歸功於原創者。. 在 YouTube 上觀看.

如需另一個基準幀，請參閱 GPT-6 Astra 對決 GPT-5.6 Sol 或克勞德·法布爾 5 對決 GPT-5.5.

如何在 GlobalGPT 中比較這兩者

GlobalGPT 針對兩種模型分別提供獨立的生成路徑。使用相同的提示詞、輸出限制及原始素材，並在評估風格之前先判斷可見正確性。當介面或 API 回傳延遲時間與標記使用量時，請一併記錄。對於長篇文件，應分別測量各路徑的結果，因為相同的文字在不同路徑下可能產生不同的標記結果。.

GPT-6 Astra 獲選用於 GlobalGPT 介面 — GlobalGPT 上的 GPT-6 Astra 模型工作區。.

在 GlobalGPT 介面中選取 Claude Fable 5.1 — Claude Fable 5.1 可在相同的平台環境中使用。.

試試 GPT-6 Astra

試試 Claude Fable 5.1

常見問題

GPT-6 Astra 的價格是否比 Claude Fable 5.1 更便宜？

兩者的基本輸入與輸出定價相同。快取命中定價與長上下文規則有所不同，而 GlobalGPT 計費方式可能會使用獨立的信用額度。.

哪個模型的上下文視窗較大？

GPT-6 Astra 列出 105 萬個代幣，而 Claude Fable 5.1 則列出 100 萬個。兩者均設定最大輸出量為 12.8 萬個。.

哪一種更適合寫程式？

在我們的單一 TypeScript 測試中，GPT-6 Astra 給出了更明確的可見答案。Fable 透過此相容性途徑未返回任何可見內容，因此這並非基於提供者原生功能的結論。.

在長上下文檢索方面，哪一種效果更好？

雙方以 10/10 打成平手，皆找到了同一項預先安排的修正案並予以引用。Fable 針對這份完全相同的檔案所報告的輸入代幣數量，幾乎是對方的三倍。.

為什麼《Fable》的評分較低？

幾個 Fable 呼叫雖然消耗了完成標記，卻回傳了內容被截斷或為空的可視內容。這可能是相容性層或輸出序列化方面的问题。.

我可以在 GlobalGPT 中同時使用這兩種模型嗎？

是的。根據 2026 年 9 月 8 日的查證結果，這兩條專用的 GlobalGPT 路線均已啟用。.

最終裁決

從這組測試來看，GPT-6 Astra 是更安全的預設選擇：它產出的結果更完整、更實用，最終得分為 43/50，相較之下 Claude Fable 5.1 僅得 19/50。不過在長上下文準確度任務中，Claude Fable 5.1 仍與 Astra 表現相當，並產出了穩健的行銷文案。在排除 Fable 之前，請透過您的生產端點重複執行測試；出現兩次空白輸出，這不僅是模型行為的問題，同時也是對整合行為的警示。.

分享文章：

GPT-6 Astra 對決 Claude Fable 5.1：哪款模型更勝一籌？

簡評

GPT-6 Astra 與 Claude Fable 5.1 規格一覽

定價、快取、上下文與限制

五項匹配的 API 測試

推理：SaaS 成本決策

撰稿：產品發表稿編輯

程式設計：TypeScript 並行處理

長篇背景：種植修正案

策略：90 天市場進入計畫

您應該選擇哪一款型號呢？

如何在 GlobalGPT 中比較這兩者

常見問題

GPT-6 Astra 的價格是否比 Claude Fable 5.1 更便宜？

哪個模型的上下文視窗較大？

哪一種更適合寫程式？

在長上下文檢索方面，哪一種效果更好？

為什麼《Fable》的評分較低？

我可以在 GlobalGPT 中同時使用這兩種模型嗎？

最終裁決

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