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Claude Fable 5.1 vs DeepSeek V4 Pro (0813)

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

这两个模型共享 1,000,000-token 的上下文窗口、对话、代码、工具和推理功能,因此区别归结于输入和价格:claude-fable-5-1 接受文本和图像输入,并始终在开启思考功能的状态下运行,而 deepseek-v4-pro-0813 仅支持文本,但允许高达 393,216 的输出 token(对比 Claude 的 128,000)。在费率方面,deepseek-v4-pro-0813 便宜得多,输入为 $1.32,输出为 $3.96,对比 $10 和 $50,分别便宜约 7.6x 和 12.6x。当需要图像输入或内置的思考功能时,请选择 claude-fable-5-1;需要大规模生成长文本时,请选择 deepseek-v4-pro-0813。

Benchmark 成绩

高于同类均值无更高分Claude Fable 5.119 / 213 / 21DeepSeek V4 Pro (0813)13 / 201 / 20
Claude Fable 5.1 DeepSeek V4 Pro (0813) 其他参测模型 同类均值 ★ 没有模型得分更高
DeepSWE 1.1
67.4%
62.7%
OSWorld 2.0 partial
80.7%
N/A
Cybergym
N/A
83.3%
HealthBench Professional
58.1%
N/A
Terminal-Bench-Science 0.1
52.6%
N/A
GPQA Diamond
93.7%
92.4%
Agents' Last Exam
N/A
25.7%
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 Pro (0813) Δ
输入 / 1M token $10 $1.32 7.6×
输出 / 1M token $50 $3.96 13×
缓存读取 / 1M token $0.25 $0.132 1.9×
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 Pro (0813)
工具调用 是 是
思考控制 始终开启 始终开启
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 Pro (0813)
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 2026-08-13
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) reasoning_effort
可选值
effort
  • low
  • medium
  • high
  • xhigh
  • max
reasoning_effort
  • the model card documents low
  • high
  • 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 Pro (0813)

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 Pro (0813) 通过 · 3 sentences

Enabling reasoning causes the model to generate a hidden chain-of-thought with many additional tokens before producing the final answer, which sharply increases compute consumption per request. Those extra reasoning tokens are billed at the same or higher rates, so total usage doubled even though the volume of final outputs stayed similar. We can control this by adjusting reasoning effort, setting token limits, or routing only complex requests through reasoning mode.

输出 308 tok (+226 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 Pro (0813) 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 else: cur = 1 best = max(best, cur) return best ``` The bug is that `cur` was initialized to `0` and only counted adjacent matching pairs, so a run of length `n` produced `n - 1` (or `0` for a single element) instead of `n`. The code also reset to `0` on a change, when the new run should start at length `1`. Initializing `cur` and `best` to `1` fixes both the single-element case and runs ending at the last index.

输出 1315 tok (+1130 思考) 延迟 16.9 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 Pro (0813) 通过 · 5/5 fields, guidance null

{"company":"Meridian Robotics","quarter":"September quarter","revenue_usd_m":412,"growth_pct":11.0,"guidance":null}

输出 2155 tok (+2121 思考) 延迟 26.0 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 Pro (0813) 通过 · 121 words, 0 banned, 1 question

Introducing cross-provider prompt caching in our API gateway. Send a prompt once, and the gateway stores its computed prefix across Anthropic, OpenAI, and other LLM providers. Subsequent requests with the same prompt hit the cache, cutting latency and token costs while keeping outputs consistent across routing decisions and provider failovers. Teams can route identical prompts between providers without reprocessing shared context or lengthy system instructions. How much could you save on repeated prompt prefixes? The cache respects provider-specific key formats, handles TTLs automatically, and works with streaming and batch requests. Enable it with one configuration flag—no changes to your application code. Available today on all plans. Monitor cache hit rates, token savings, and provider-specific performance metrics in the live dashboard.

输出 2845 tok (+2694 思考) 延迟 25.5 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-pro-0813",  # 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 Pro (0813) 哪个更便宜?

按「输入 / 1M token」算,DeepSeek V4 Pro (0813) 更便宜($1.32 对 $10,相差 7.6×)。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 Pro (0813) 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 Pro (0813) 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