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Dola Seed 2.0 Lite vs Claude Haiku 5.5

vs

什么时候选哪个

提示词不超过 100000 token 时 claude-haiku-5-5 更便宜:输入 $0.1、输出 $0.5,Dola-Seed-2.0-lite 为 $0.25 和 $2。超过之后 claude-haiku-5-5 按 5x 计费,价格反而高于 Dola-Seed-2.0-lite,直到后者自己的费率在超过 128000 token 后翻倍。claude-haiku-5-5 的上下文更大(1000000 对 256000),缓存读取也更便宜($0.01 对 $0.05);Dola-Seed-2.0-lite 接受音频和视频输入。短提示词和超长文档选 claude-haiku-5-5,音频或视频选 Dola-Seed-2.0-lite。

Benchmark 成绩

Claude Haiku 5.5:供应商没有公布 benchmark 成绩。

高于同类均值无更高分Dola Seed 2.0 Lite4 / 103 / 10
Dola Seed 2.0 Lite Claude Haiku 5.5 其他参测模型 同类均值 ★ 没有模型得分更高
SWE Multilingual GPT-5.4 High
66.6%
N/A
WenetSpeech test-net (CER)
没有模型得分更高 4.47%
N/A
OSWorld-Verified
64.4%
N/A
GPQA Diamond
88.4%
N/A
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 Haiku 5.5 Δ
输入 / 1M token $0.25 $0.1 2.5×
输出 / 1M token $2 $0.5 4×
缓存读取 / 1M token $0.05 $0.01 5×
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 78 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 Haiku 5.5
工具调用 是 是
思考控制 可配置 可配置
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 Haiku 5.5
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 2026-10-07
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
可选值
thinking.type
  • enabled
  • disabled (no auto)
reasoning_effort
  • minimal
  • low
  • medium
  • high
output_config.effort
  • low
  • medium
  • high
  • xhigh
  • max
默认值 enabled, with reasoning_effort medium medium

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 Haiku 5.5

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

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 Haiku 5.5 通过 · 3 sentences

Enabling reasoning makes the model generate a long internal chain of thought before its answer, and those thinking tokens are billed as output tokens, which cost several times more than input tokens. The model often produces many times more output per request than it did without reasoning, so the per-call cost rises even when the visible answer looks about the same length. Because that extra spend scales with every request, the bill roughly doubled, and we can control it by setting reasoning budgets, using reasoning only for tasks that need it, or routing simpler requests to a non-reasoning model.

输出 332 tok (+159 思考) 延迟 2.9 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 Haiku 5.5 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)): if xs[i] == xs[i - 1]: cur += 1 else: cur = 1 best = max(best, cur) return best ``` The bug is that `cur` counts adjacent equal *pairs*, not run *elements*. A run of k equal items produces k−1 matching pairs, so the function returns one less than the true length, and a single-element input produces no pairs at all, so it returns 0 instead of 1. Resetting `cur` to 0 on a mismatch compounds this by starting every run one step behind. The fix starts both `cur` and `best` at 1 (a non-empty list always has a run of at least one element), resets `cur` to 1 on a mismatch, and updates `best` after every element rather than only inside the equal-branch. The empty-list guard keeps the function from returning 1 for `[]`. […]

输出 1012 tok (+591 思考) 延迟 6.0 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

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 Haiku 5.5 通过 · 5/5 fields, guidance "Formal guidance withheld pending the Q4 "

```json { "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": null, "growth_pct": 11, "guidance": "Formal guidance withheld pending the Q4 close" } ``` Notes: The text gives revenue as 412 but doesn't state the currency, so I left `revenue_usd_m` as null. Growth is "eleven and change," so 11 is an approximation.

输出 725 tok (+572 思考) 延迟 4.6 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

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 Haiku 5.5 通过 · 120 words, 0 banned, 1 question

**Introducing Prompt Cache Sync, the newest feature in GatewayOne.** Teams routing requests across OpenAI, Anthropic, and Google have often paid to process the same long system prompts again and again. Now the gateway stores those prompts once and reuses them wherever they appear, regardless of provider. Repeated context is matched by content hash, so cached prefixes return faster and cost less, with no changes to your application code. How much could this save your team? Enable Prompt Cache Sync from the dashboard, and your next billing report will show the difference. Cache entries follow your existing retention settings and can be purged per project at any time. Availability begins this week for all Scale and Enterprise plans worldwide. Start today.

输出 2164 tok (+1929 思考) 延迟 9.9 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-haiku-5-5",  # 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 Haiku 5.5 哪个更便宜?

按「输入 / 1M token」算,Claude Haiku 5.5 更便宜($0.1 对 $0.25,相差 2.5×)。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 Haiku 5.5 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。

Dola Seed 2.0 Lite 和 Claude Haiku 5.5 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