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Dola Seed 2.0 Lite vs Claude Fable 5.1

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

Dola-Seed-2.0-lite 是更便宜、输入支持更广泛的选择:每百万 token 的输入和输出分别为 $0.25 和 $2,比 claude-fable-5-1 的 $10 和 $50 分别便宜 40 倍和 25 倍,并且它除了文本和图像之外还支持视频和音频,其思考功能可以关闭,且具有略大一些的 131072-token 输出上限。当你需要其 1000000-token 的上下文(大约是 Dola 的 256000 的四倍),外加在文本和图像输入上始终开启的思考和推理标志,并且能接受该价格表时,请选择 claude-fable-5-1。对于高吞吐量的多模态摄取任务,Dola 是务实的默认选择。

Benchmark 成绩

高于同类均值无更高分Dola Seed 2.0 Lite4 / 103 / 10Claude Fable 5.119 / 213 / 21
Dola Seed 2.0 Lite Claude Fable 5.1 其他参测模型 同类均值 ★ 没有模型得分更高
DeepSWE 1.1
N/A
67.4%
WenetSpeech test-net (CER)
没有模型得分更高 4.47%
N/A
OSWorld-Verified
64.4%
N/A
HealthBench Professional
N/A
58.1%
Terminal-Bench-Science 0.1
N/A
52.6%
GPQA Diamond
88.4%
93.7%
BrowseComp
64%
N/A
MMVU
76.7%
N/A

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

价格

Dola Seed 2.0 Lite Claude Fable 5.1 Δ
输入 / 1M token $0.25 $10 0.025×
输出 / 1M token $2 $50 0.04×
缓存读取 / 1M token $0.05 $0.25 0.2×
缓存写入 - 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 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

Dola Seed 2.0 Lite Claude Fable 5.1
工具调用 是 是
思考控制 可配置 始终开启
结构化输出 是 是
提示词缓存 隐式 + 显式 显式(由你标记前缀)
缓存有效期 未公布 5m default, 1h option
最小缓存前缀 1024 个 token 1024 个 token

规格

Dola Seed 2.0 Lite Claude Fable 5.1
输入模态 文本 图像 音频 视频 文本 图像
输出模态 文本 文本
发布日期 2026-02-14 2026-09-01
知识截止日期 - 2026-06
上下文窗口 256K 1M
最大输出 131K 128K
思考参数
  • thinking.type
  • reasoning_effort
output_config.effort (thinking is adaptive-only and always on)
可选值
thinking.type
  • enabled
  • disabled (no auto)
reasoning_effort
  • minimal
  • low
  • medium
  • high
effort
  • low
  • medium
  • high
  • xhigh
  • max
默认值 enabled, with reasoning_effort medium

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: Dola Seed 2.0 Lite · Claude Fable 5.1

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

Dola Seed 2.0 Lite 通过 · 3 sentences

Enabling reasoning on our production LLM requires the model to run resource-intensive multi-step chain-of-thought inference for every customer-facing query, rather than the single-pass inference that powered our original non-reasoning deployment, and GPU compute—which makes up 90% of our total LLM bill—scales directly with the number of compute cycles each query consumes. Our cloud provider’s LLM billing model charges per million processed tokens, and reasoning workflows generate an average of 2.1x more hidden processing tokens (the intermediate problem-solving steps the model never surfaces to end users) that are still billable, pushing our total monthly token consumption just over double what it was prior to enabling the feature. […]

输出 1306 tok (+1096 思考) 延迟 18.2 s

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

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

Dola Seed 2.0 Lite 通过 · 8/8 cases

### Corrected Function ```python def longest_run(xs): if not xs: # Handle empty input edge case return 0 best = cur = 1 # Base run length of 1 for any non-empty single element for i in range(1, len(xs)): if xs[i] == xs[i - 1]: cur += 1 best = max(best, cur) else: cur = 1 # Reset run length for the new, different element return best ``` ### Bug Explanation (single paragraph) The original function’s core flaw was that it counted the number of consecutive equal adjacent element pairs, rather than the number of elements in the run itself, leading to all results being off by one, which caused both reported issues: a run of k equal elements only has k-1 consecutive matching pairs, so the original function’s maximum `best` value was always one less than the true element count of the longest run. […]

输出 2620 tok (+2217 思考) 延迟 33.2 s

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

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

Dola Seed 2.0 Lite 通过 · 5/5 fields, guidance "Formal guidance withheld pending Q4 clos"

{ "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": 11, "guidance": "Formal guidance withheld pending Q4 close" }

输出 1502 tok (+1439 思考) 延迟 20.2 s

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

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

Dola Seed 2.0 Lite 未通过 · 141 words, 0 banned, 1 question

Today we launch Cross-Provider Prompt Cache, a new feature for our Core API Gateway built to cut redundant AI costs and latency for teams routing requests across multiple LLM providers. Many development teams rotate between OpenAI, Anthropic, and Google Gemini to balance performance, cost, and capability, but identical prompts get reprocessed from scratch with every provider switch, wasting tokens and slowing response times. This feature stores validated prompt responses at the gateway layer, so repeat requests pull from cache regardless of which provider they route to, with configurable TTLs and built-in compliance with all major provider data policies. […]

输出 1870 tok (+1695 思考) 延迟 23.1 s

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

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="Dola-Seed-2.0-lite",
    # model="claude-fable-5-1",  # 取消注释此行,注释上一行
    messages=[{"role": "user", "content": "Summarize this diff"}],
)
print(resp.choices[0].message.content)

获取 API key →

常见问题

Dola Seed 2.0 Lite 和 Claude Fable 5.1 哪个更便宜?

按「输入 / 1M token」算,Dola Seed 2.0 Lite 更便宜($0.25 对 $10,相差 40×)。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 Dola Seed 2.0 Lite 和 Claude Fable 5.1 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。

Dola Seed 2.0 Lite 和 Claude Fable 5.1 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