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Claude Fable 5 vs Claude Opus 5

Claude Fable 5 已从我们的目录下线。下方是它最后公布的价格;这个模型已无法调用,对比中的另一个模型仍可正常调用。

vs

什么时候选哪个

Anthropic 自己的分岔路口:Opus 5 为 $5/$25,具备可关闭的自适应思考功能,Fable 5 为 $10/$50,其思考是结构性且始终开启的。如果你的工作负载受益于在简单调用上禁用深思熟虑,Opus 5 只有一半价格且听从指令;Fable 5 则适用于你绝不希望模型跳过思考的工作。

Benchmark 成绩

领先高于同类均值无更高分Claude Fable 51386 / 9740 / 97Claude Opus 51738 / 468 / 46

30 项两边都有成绩。

Claude Fable 5 Claude Opus 5 其他参测模型 同类均值 ★ 没有模型得分更高
DeepSWE 1.1
69.7%
68.8%
BioMysteryBench hard
46.5%
49.4%
OSWorld 2.0
66.1%
没有模型得分更高 70.6%
ExploitBench (Cap%)
78%
70%
HealthBench Professional
60.9%
59.8%
Finance Agent v2
56.3%
58.6%
Legal Agent Benchmark
没有模型得分更高 13.3%
6.7%
GPQA Diamond
92.6%
93.4%
Blueprint-Bench 2
38.6%
N/A
BrowseComp
87.4%
90.8%
LVBench
N/A
75.4%

供应商公布: Alibaba (Qwen) Anthropic ByteDance DeepSeek Google MiniMax Moonshot OpenAI Tencent Z.ai

价格

Claude Fable 5 Claude Opus 5 Δ
输入 / 1M token $10 $5 2×
输出 / 1M token $50 $25 2×
缓存读取 / 1M token $1 $0.5 2×
缓存写入 1.25x (5m) / 2x (1h) 1.25x (5m) / 2x (1h) -

价格取自构建时的实时目录,最新价格见各模型页面。

两个模型所处的位置:全部 76 个按同一单位计费的聊天模型的每 1M token 输入价分布(对数刻度)

能力

Claude Fable 5 Claude Opus 5
工具调用 是 是
思考控制 始终开启 可配置
结构化输出 是 是
提示词缓存 显式(由你标记前缀) 显式(由你标记前缀)
缓存有效期 5m default, 1h option 5m default, 1h option
最小缓存前缀 1024 个 token 1024 个 token

规格

Claude Fable 5 Claude Opus 5
输入模态 文本 图像 文本 图像
输出模态 文本 文本
发布日期 2026-06-09 2026-07-24
知识截止日期 2026-01 2026-05
上下文窗口 1M 1M
最大输出 128K 128K
思考参数 output_config.effort (thinking.type is adaptive-only and needs no configuration)
  • thinking.type
  • output_config.effort
可选值
effort
  • low
  • medium
  • high
  • xhigh
  • max

both "enabled" and "disabled" return 400

thinking.type
  • adaptive
  • disabled
effort
  • low
  • medium
  • high
  • xhigh
  • max
默认值

thinking always on (adaptive)

effort
  • high

thinking on

effort
  • high (Claude API and Claude Code)

规格照录自各供应商的文档;供应商没有公布的项目,对应的行直接省略,不做推断。 完整来源: Claude Fable 5 · Claude Opus 5

同一条提示词,两个模型,经网关实测

提示词 Explain to a CFO, in exactly three sentences, why our LLM bill doubled after we enabled reasoning. 检查 恰好 3 句

Claude Fable 5 通过 · 3 sentences

When we enabled reasoning, the model began generating extensive internal "thinking" tokens before each answer, and those tokens are billed as output even though users never see them. Output tokens typically cost several times more than input tokens, so adding thousands of hidden reasoning tokens per request effectively multiplied our per-query cost. In short, we're paying for a much longer response behind the scenes in exchange for higher answer quality, and we can manage this by tuning reasoning effort levels or reserving reasoning mode for complex tasks only.

输出 228 tok 延迟 6.7 s

Claude Opus 5 通过 · 3 sentences

When reasoning is enabled, the model generates a hidden chain of "thinking" tokens before it writes the visible answer, and those internal tokens are billed at the output rate even though no one ever sees them — commonly 3–10x more tokens than the response itself. Because output tokens are priced several times higher than input tokens, that added volume hits us at our most expensive rate, which is why the bill roughly doubled while our request count stayed flat. The spend is controllable: we can cap the reasoning budget per call, route only genuinely hard requests to reasoning mode and cheaper models for the rest, and track cost per successfully completed task rather than cost per token, since a single correct answer usually beats three cheap retries plus human cleanup.

输出 529 tok 延迟 9.0 s

指令遵循(恰好三句,数得出来)、受众适配(对 CFO 说话的口吻),以及下方 token 计数暴露出的隐藏思考计费差额。

提示词 This function is supposed to return the longest run of consecutive equal items, but callers report it is off by one on single-element inputs and misses runs that end at the last index. Fix it and explain the bug in one paragraph. 检查 修复通过测试

Claude Fable 5 通过 · 8/8 cases

## Fixed code ```python def longest_run(xs): if not xs: return 0 best = cur = 1 for i in range(1, len(xs)): if xs[i] == xs[i - 1]: cur += 1 else: cur = 1 best = max(best, cur) return best ``` ## Explanation The original function counts **adjacent equal pairs** rather than **items in a run**: a run of length *n* contains only *n − 1* equal neighbor pairs, so every result is off by one — a single-element list yields 0 instead of 1, and `[1, 2, 2]` yields 1 instead of 2 (which callers perceive as "missing" the run at the end). The fix is to initialize both `cur` and `best` to 1 (any nonempty list has a run of at least length 1), reset `cur` to 1 rather than 0 when

输出 531 tok 延迟 12.5 s

Claude Opus 5 通过 · 8/8 cases

## Fixed version ```python def longest_run(xs): if not xs: return 0 best = 1 cur = 1 for i in range(1, len(xs)): if xs[i] == xs[i - 1]: cur += 1 else: cur = 1 if cur > best: best = cur return best ``` ## The bug The counters were tracking the wrong quantity: `cur` counted *matching adjacent pairs* rather than *elements in the current run*, and both counters started at `0` instead of `1`. Since a run of length *n* contains only *n − 1* adjacent equal pairs, every result came back one too small — `[1, 1]` returned 1, `[3, 3, 3]` returned 2 — and a single-element list returned 0 because the loop body never executed and `best` kept its initial value of `0`. […]

输出 1094 tok 延迟 16.1 s

修复是否真的正确(能运行)、解释的信息密度,以及在一个范围明确的任务上的 token 效率。

提示词 Extract a JSON object with fields {company, quarter, revenue_usd_m, growth_pct, guidance} from this text. Use null for anything not stated; add no other fields. 检查 合法 JSON,严格符合 schema

Claude Fable 5 通过 · 5/5 fields, guidance null

```json { "company": "Meridian Robotics", "quarter": "Q3 (September quarter)", "revenue_usd_m": 412, "growth_pct": 11, "guidance": null } ``` Notes on interpretation: "up eleven and change" is rendered as 11 (the precise figure isn't stated), and guidance is null because it was explicitly withheld.

输出 192 tok 延迟 6.5 s

Claude Opus 5 通过 · 5/5 fields, guidance "Withheld pending Q4 close"

```json { "company": "Meridian Robotics", "quarter": "Q3", "revenue_usd_m": 412, "growth_pct": 11, "guidance": "Withheld pending Q4 close" } ``` Notes on judgment calls: - **quarter**: "September quarter" is labeled Q3 because the text refers to an upcoming "Q4 close," implying a calendar-aligned fiscal year. No fiscal year is given. - **revenue_usd_m**: 412 is stated as constant-currency and hedged ("call it"), so it's approximate; the currency is not explicitly stated as USD. - **growth_pct**: "up eleven and change" means slightly more than 11, so 11 is a floor rather than an exact figure.

输出 539 tok 延迟 7.6 s

是否严守 schema(不臆造字段)、能否顶住幻觉压力(原文明说暂不给出 guidance),以及结构化输出路径的差异。

提示词 Write a 120-word product announcement for an API gateway feature that caches prompts across providers. Forbidden words: "seamless", "unlock", "game-changer", "revolutionize", "empower". Exactly one sentence must be a question. 检查 120 词,0 个禁用词

Claude Fable 5 通过 · 120 words, 0 banned, 1 question

**Introducing Cross-Provider Prompt Caching for our API Gateway** Starting today, your repeated prompts are cached once and reused across OpenAI, Anthropic, Google, and any other provider you route through the gateway. Instead of paying full token costs every time your application sends the same system prompt or context block, the gateway detects matching prefixes and serves cached results in milliseconds. Why keep spending money and latency on identical requests? Early adopters report cost reductions of up to 60 percent on high-volume workloads, along with faster median response times. Configuration is simple: enable caching in your dashboard, set a TTL, and choose which routes participate. Cache entries are encrypted at rest and never shared between accounts. Available now on all plans.

输出 1173 tok 延迟 18.2 s

Claude Opus 5 通过 · 123 words, 0 banned, 1 question

**Cross-Provider Prompt Caching** We're introducing Cross-Provider Prompt Caching in the API Gateway. Repeated system prompts, long documents, and few-shot examples are stored once at the gateway layer and reused across OpenAI, Anthropic, Google, and self-hosted models. Instead of paying full input token costs on every request, your application sends a cache reference, and the gateway rehydrates the context before forwarding it downstream. Why does that matter? Teams running high-volume agents and retrieval pipelines typically see input token spend fall 40 to 70 percent, with median latency dropping by several hundred milliseconds. Caches are scoped per project, encrypted at rest, and invalidated automatically when a prompt template changes. Enable it with a single header, and see the docs for TTL tuning and per-route controls.

输出 1593 tok 延迟 19.1 s

是否守住约束(字数预算、禁用词表、只能有一个问句)、文风特征,以及长度控制。

改一行代码就能在两个模型之间切换

下方每个标签页里都有两个模型 ID,高亮的那两行是唯一要改的地方。端点不变,API key 不变,请求结构也不变。

from openai import OpenAI

client = OpenAI(
    base_url="https://synthorai.io/v1",
    api_key="sk-syn-...",
)

resp = client.chat.completions.create(
    model="claude-fable-5",
    # model="claude-opus-5",  # 取消注释此行,注释上一行
    messages=[{"role": "user", "content": "Summarize this diff"}],
    reasoning_effort="medium",
)
print(resp.choices[0].message.content)

获取 API key →

常见问题

Claude Fable 5 和 Claude Opus 5 哪个更便宜?

按「输入 / 1M token」算,Claude Opus 5 更便宜($5 对 $10,相差 2.0×)。其他计费项的结论可能相反,完整价格见上方表格,实际成本取决于你的用量构成。

不用分别集成两次,就能对 Claude Fable 5 和 Claude Opus 5 做 A/B 测试吗?

可以。两个模型走同一个 OpenAI 兼容端点,用同一个 API key,切换时只要改一行里的模型名,所以可以给两个模型各分一部分流量,直接对比账单。

Claude Fable 5 和 Claude Opus 5 支持提示词缓存吗?

支持。两个模型的缓存读取价都低于各自的输入价,所以前缀能反复命中缓存的负载,实际成本会比按官网价估算的低。具体的缓存读取价见上方价格表。

相关对比

我们的实测研究