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Claude Fable 5.1 vs DeepSeek V4 Flash (0731)

Claude Fable 5.1 目前为邀请制。下方是它的实时价格,但调用前需要先为你的工作空间开通权限;打算根据这份对比做开发的话,请先向我们申请。

vs

什么时候选哪个

两者共享 1,000,000-token 的上下文窗口,因此区别在于成本和模态:claude-fable-5-1 每百万输入 $10,每百万输出 $50,分别是 deepseek-v4-flash-0731 的 $0.44 和 $1.32 的约 23x 和 38x。当你需要在文本之外输入图像,或需要无法在其上禁用的始终开启的思考功能时,请选择 claude-fable-5-1。对于大规模纯文本对话、代码、工具和推理任务,或当单次回复需要很长时,请选择 deepseek-v4-flash-0731——它的 393216-token 最大输出大约是 claude-fable-5-1 允许的 128000 token 的 3x。

Benchmark 成绩

领先高于同类均值无更高分Claude Fable 5.1519 / 213 / 21DeepSeek V4 Flash (0731)010 / 280 / 28

5 项两边都有成绩。

Claude Fable 5.1 DeepSeek V4 Flash (0731) 其他参测模型 同类均值 ★ 没有模型得分更高
DeepSWE 1.1
67.4%
54.4%
OSWorld 2.0 partial
80.7%
N/A
Cybergym
N/A
76.7%
HealthBench Professional
58.1%
N/A
JobBench
N/A
41.3%
GPQA Diamond
93.7%
89.9%
Agents' Last Exam
N/A
25.2%
Chartography with tools
88.4%
N/A

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

价格

Claude Fable 5.1 DeepSeek V4 Flash (0731) Δ
输入 / 1M token $10 $0.44 23×
输出 / 1M token $50 $1.32 38×
缓存读取 / 1M token $0.25 $0.044 5.7×
缓存写入 1.25x (5m) / 2x (1h) 不单独收费 -

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 79 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 5.1 DeepSeek V4 Flash (0731)
工具调用 是 是
思考控制 始终开启 是,但供应商未公布调节参数
结构化输出 是 是
提示词缓存 显式(由你标记前缀) 隐式(自动)
缓存有效期 5m default, 1h option no fixed TTL (evicted when unused)
最小缓存前缀 1024 个 token 未公布

规格

Claude Fable 5.1 DeepSeek V4 Flash (0731)
输入模态 文本 图像 文本
输出模态 文本 文本
发布日期 2026-09-01 2026-07-31
知识截止日期 2026-06 -
上下文窗口 1M 1M
最大输出 128K 393K
思考参数 output_config.effort (thinking is adaptive-only and always on) -
可选值
effort
  • low
  • medium
  • high
  • xhigh
  • max
-
默认值

thinking always on (adaptive)

effort
  • high
-

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 5.1 · DeepSeek V4 Flash (0731)

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 5.1 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

DeepSeek V4 Flash (0731) 通过 · 3 sentences

Enabling reasoning mode prompts the model to generate extensive internal chain-of-thought tokens before answering, often multiplying total token output per query by several times. This directly inflates costs because we are billed per token, and these reasoning tokens are processed at a premium compute tier that carries a higher price per million tokens. However, this spend buys measurably higher accuracy and fewer failed calls, so we are tracking it against specific error-reduction KPIs to ensure the ROI justifies the increase.

输出 365 tok (+266 思考) 延迟 5.1 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 5.1 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

DeepSeek V4 Flash (0731) 通过 · 8/8 cases

```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 ``` The original bug stems from initializing `cur` to 0 and only incrementing it when adjacent elements are equal, which counts the number of *equal transitions* rather than the actual run length—so every run of length L is reported as L−1, and a single‑element run (with no transitions) yields 0. Moreover, because `best` is updated only inside the equality branch, it never accounts for a run that ends at the last element if that run is longer than any previously seen (though the real issue remains the off‑by‑one). […]

输出 1268 tok (+1016 思考) 延迟 14.0 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 5.1 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

DeepSeek V4 Flash (0731) 通过 · 5/5 fields, guidance null

{ "company": "Meridian Robotics", "quarter": "Q3", "revenue_usd_m": 412, "growth_pct": null, "guidance": null }

输出 1119 tok (+1072 思考) 延迟 12.3 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 5.1 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

DeepSeek V4 Flash (0731) 未通过 · 141 words, 0 banned, 1 question

**Introducing Prompt Cache for API Gateway** We are pleased to announce Prompt Cache, a new feature that stores and reuses identical prompt prefixes across all connected LLM providers. By automatically recognizing repeated requests, this cache reduces token consumption and lowers latency without altering your application logic. Whether you use OpenAI, Anthropic, or open-source models, the cache operates transparently, ensuring consistent behavior while cutting costs. Developers can configure cache TTL and scoping rules via a simple API call, giving you precise control over data freshness and privacy. This feature also minimizes redundant upstream calls, freeing provider rate limits for more critical workloads. Start caching today through the dashboard or CLI, and watch your operational expenses drop significantly. […]

输出 254 tok (+80 思考) 延迟 4.4 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-5-1",
    # model="deepseek-v4-flash-0731",  # 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 5.1 和 DeepSeek V4 Flash (0731) 哪个更便宜?

按「输入 / 1M token」算,DeepSeek V4 Flash (0731) 更便宜($0.44 对 $10,相差 23×)。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 5.1 和 DeepSeek V4 Flash (0731) 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 5.1 和 DeepSeek V4 Flash (0731) 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