新人 免费注册,送 10 次调用,最高 $1,免绑卡。

Claude Fable 5.1 vs Gemini 3.8 Flash

Claude Fable 5.1 采用邀请制提供。下方数据为其实时费率,但调用前需先获得工作区授权;在基于此对比进行开发前,请向我们申请访问权限。

vs

何时用哪一个

两者发布时间相差一天,都支持在约一百万 token 的上下文内接收文本和图像输入,但费率表分歧严重:claude-fable-5-1 的每个 token 在输入($10 对比 $0.75)和输出($50 对比 $3.75)上都要贵约 13x,在缓存读取上要贵约 3x($0.25 对比 $0.075)。如果需要其无法关闭的常驻思考功能,或在单次响应中需要高达 128000 的输出 token,请选择 claude-fable-5-1。对于高容量或成本敏感的工作,或需要音频和视频输入时,请选择 gemini-3.8-flash(需接受其 65536 的最大输出)。

Benchmark 成绩

领先高于同侪均值无人分数更高Claude Fable 5.1416 / 185 / 18Gemini 3.8 Flash212 / 165 / 16

双方都被测过的 6 项。

Claude Fable 5.1 Gemini 3.8 Flash 其他被测模型 同侪均值 无人分数更高
DeepSWE 1.1
67.4%
73.7%
BioMysteryBench hard
N/A
无人分数更高 56.5%
OSWorld 2.0 Partial score, batch tool enabled
N/A
59%
HealthBench Professional
58.1%
52.1%
Finance Agent v2
N/A
无人分数更高 61.4%
Legal Agent Benchmark
N/A
10%
GPQA Diamond
93.7%
95.3%
AutomationBench
31.4%
N/A
CharXiv (RQ) no tools
N/A
无人分数更高 86.2%

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

定价

Claude Fable 5.1 Gemini 3.8 Flash Δ
输入 / 1M tokens $10 $0.75 13×
输出 / 1M tokens $50 $3.75 13×
缓存读取 / 1M tokens $0.25 $0.075 3.3×
缓存写入 1.25x (5m) / 2x (1h) - -

费率取自构建时的实时目录;每个模型页面均附有当前的费率卡。

它们的位置 — 以该计费单位计费的所有 71 个 聊天 模型的 每 1M token 的输入价格(对数刻度)

能力

Claude Fable 5.1 Gemini 3.8 Flash
工具使用
思考控制 始终开启 是 —— 厂商未公布调节参数
结构化输出
提示词缓存 显式(由您标记前缀) 隐式 + 显式
缓存生存时间 5m default, 1h option 未公开
最小缓存前缀 1024 个 token 4096 个 token

规格

Claude Fable 5.1 Gemini 3.8 Flash
输入模态 文本 图像 文本 图像 音频 视频
输出模态 文本 文本
发布日期 2026-09-01 2026-09-02
知识截止日期 2026-06 2026-03
上下文窗口 1M 1M
最大输出 128K 66K
思考参数 output_config.effort (thinking is adaptive-only and always on) -
允许的值
effort
  • low
  • medium
  • high
  • xhigh
  • max
-
默认值

thinking always on (adaptive)

effort
  • high
-

规格转录自各供应商的文档;若供应商未发布某项数据,则直接省略该行,而非进行推断。 完整来源: Claude Fable 5.1 · Gemini 3.8 Flash

单个 Prompt,两个模型 —— 通过网关实测

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 5.1 通过 · 3 sentences

When we enabled reasoning, the model started generating a long chain of internal "thinking" tokens before producing each answer, and we're billed for those tokens at the same output rate even though users never see them. In practice, that hidden reasoning often runs several times longer than the visible response, so the total tokens per request roughly doubled while the number of requests stayed flat. The upside is measurably better accuracy on complex tasks, so the right conversation is whether that quality gain justifies the cost—and if not, we can restrict reasoning to the workflows that actually need it rather than turning it on globally.

输出 186 tok 延迟 3.2 s

Gemini 3.8 Flash 通过 · 3 sentences

When we enabled reasoning, the model began generating hidden "thinking tokens" to work through logic step-by-step before producing a final answer. Because AI vendors bill for every single token processed—visible or not—this internal deliberation dramatically inflated our billable volume per query. In short, while our total number of user requests remained flat, the cost per transaction doubled to buy higher accuracy on complex tasks.

输出 705 tok (+624 思考) 延迟 6.4 s

指令遵循(恰好三句,可数)、受众适配(面向 CFO 的语气),以及下方 token 计量所暴露的隐藏思考计费缺口。

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 5.1 通过 · 8/8 cases

```python def longest_run(xs): if not xs: return 0 best = cur = 1 for i in range(1, len(xs)): cur = cur + 1 if xs[i] == xs[i - 1] else 1 best = max(best, cur) return best ``` The bug is that `cur` counts *matches between adjacent elements* rather than *elements in the run*: it starts at 0 and only increments when `xs[i] == xs[i-1]`, so a run of length k produces a count of k−1. That's why a single element returns 0 instead of 1, and why every other run also comes out one short — the "misses runs at the end" reports are the same off-by-one seen on inputs like `[1, 2, 2]`, where the trailing run of length 2 is reported as 1 (the original code does update `best` inside the loop, so end-of-list runs aren't actually skipped, just undercounted). […]

输出 882 tok (+507 思考) 延迟 3.4 s

Gemini 3.8 Flash 通过 · 8/8 cases

Here is the corrected function: ```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 best = max(best, cur) else: cur = 1 return best ``` ### Explanation of the Bug The original function counted equal-neighbor transitions rather than the actual items making up each run. Because `cur` started at `0` (and was reset to `0` on mismatches), every run was undercounted by one: single-element lists returned `0` because the loop never executed, and any subsequent run—including those ending at the last index—recorded a count of $N - 1$ instead of $N$. The fix handles empty input upfront, initializes both `cur` and `best` to `1` (since any single element forms a run of length 1), and resets `cur` to `1` whenever a new run begins.

输出 1393 tok (+1149 思考) 延迟 10.1 s

修复是否真的正确(可运行)、解释的信息密度,以及在一个边界明确的任务上的 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,schema 精确

Claude Fable 5.1 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 judgment calls (not part of the object): - **revenue_usd_m**: The text says "412 for the topline" but never states the currency or that the figure is in millions; it's also on a constant-currency basis and prefaced with "call it," so it's approximate. If you want strict literalism, this should be `null`. - **growth_pct**: "Eleven and change" means slightly above 11%; the exact figure isn't given. - **quarter**: "September quarter" is Q3 on a calendar basis, consistent with the reference to a pending Q4 close. - **guidance**: Explicitly withheld, so `null`.

输出 633 tok (+349 思考) 延迟 3.8 s

Gemini 3.8 Flash 通过 · 5/5 fields, guidance null

```json { "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": null, "guidance": null } ```

输出 3989 tok (+3932 思考) 延迟 30.4 s

schema 服从度(不臆造字段)、幻觉压力(guidance 明确被暂缓给出),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 5.1 通过 · 121 words, 0 banned, 1 question

**Introducing Cross-Provider Prompt Caching for the Gateway.** Starting today, your API gateway can cache prompts once and reuse them across OpenAI, Anthropic, Google, and any other provider you route to. Long system instructions, retrieval context, and few-shot examples are stored at the gateway layer, so switching models no longer means paying to resend the same tokens. Why send the same 8,000-token preamble to three different vendors when you could send it once? Our cache normalizes prompt segments, fingerprints them, and serves matching prefixes from memory before the request ever reaches an upstream API. Early customers report token costs falling by 30 to 60 percent on high-volume workloads, with median latency dropping alongside. Available today on all Team and Enterprise plans.

输出 1722 tok (+1463 思考) 延迟 2.9 s

Gemini 3.8 Flash 通过 · 120 words, 0 banned, 1 question

Why pay multiple model providers for the exact same output? Introducing Universal Prompt Cache, our latest API gateway capability engineered to cut compute expenses and drop inference latency. When your application makes a call, the gateway inspects a central memory layer before routing traffic to external LLMs. If an identical query was previously processed by OpenAI, Anthropic, or Mistral, our gateway returns that response immediately. This shared cache eliminates duplicate token fees and insulates your production apps from vendor rate limits or unexpected downtime. Developers can easily customize expiration settings, enforce strict data privacy controls, and configure invalidation logic across every endpoint. Stop wasting your budget on repeated queries. Enable prompt caching in your dashboard to accelerate your pipeline today.

输出 3833 tok (+3688 思考) 延迟 21.8 s

约束服从度(字数预算、禁用词表、唯一的那句问句)、文风指纹,以及长度控制。

只需一行代码即可在它们之间切换

两个 ID 都包含在下方的每个选项卡中 — 高亮显示的两行是唯一的修改。相同的端点,相同的密钥,相同的请求结构。

from openai import OpenAI

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

resp = client.chat.completions.create(
    model="claude-fable-5-1",
    # model="gemini-3.8-flash",  # 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 密钥 →

常见问题

Claude Fable 5.1 和 Gemini 3.8 Flash 哪个更便宜?

在 输入 / 1m tokens 方面,Gemini 3.8 Flash 更便宜($0.75 对比 $10,相差 13×)。其他行可能得出相反的结论——上方表格提供了完整信息,实际成本取决于你的组合使用情况。

我可以在不进行两次集成的情况下,对 Claude Fable 5.1 和 Gemini 3.8 Flash 进行 A/B 测试吗?

可以。两者均通过同一个兼容 OpenAI 的端点提供服务,并使用同一把 API 密钥——切换只需更改一行模型字符串,因此你可以将一部分流量路由到各个模型并直接比较账单。

Claude Fable 5.1 和 Gemini 3.8 Flash 支持提示词缓存吗?

是的 —— 两者对缓存读取的计费均低于其输入费率,因此热前缀工作负载的成本低于标价。准确的缓存读取行位于上方的定价表中。

相关对比

来自我们的实测研究