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Dola Seed 2.0 Pro vs Claude Opus 5.5

vs

何时用哪一个

Dola-Seed-2.0-pro 每百万输入 token 的成本为 $0.5,输出为 $3,而 claude-opus-5-5 则分别为 $4 和 $20,因此其输入费率低 8 倍,输出费率低约 6.7 倍,而且它是两者中唯一接受视频输入并允许您禁用思考(thinking)的模型。当您需要 claude-opus-5-5 的 1,000,000 token 上下文(约为 Dola-Seed-2.0-pro 的 256,000 窗口的 3.9 倍),并且可以接受始终开启的推理(reasoning)以及高 2 倍的缓存读取费率($0.2 对 $0.1)时,请选择 claude-opus-5-5。两者均按 token 计费,没有折扣乘数,因此成本比较是直接的。

Benchmark 成绩

Dola Seed 2.0 Pro:厂商没有公布过 benchmark 成绩。

高于同侪均值无人分数更高Claude Opus 5.59 / 97 / 9
Dola Seed 2.0 Pro Claude Opus 5.5 其他被测模型 同侪均值 无人分数更高
Terminal-bench 4.0
N/A
无人分数更高 66.4%
OSWorld 2.0 partial
N/A
无人分数更高 81.8%
Terminal-Bench-Science 0.1
N/A
58.7%
Humanity's Last Exam with tools
N/A
无人分数更高 67.7%
AutomationBench
N/A
40%
Chartography with tools
N/A
无人分数更高 89%

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

定价

Dola Seed 2.0 Pro Claude Opus 5.5 Δ
输入 / 1M tokens $0.5 $4 0.13×
输出 / 1M tokens $3 $20 0.15×
缓存读取 / 1M tokens $0.1 $0.2 0.5×
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 74 个 聊天 模型的 每 1M token 的输入价格(对数刻度)

能力

Dola Seed 2.0 Pro Claude Opus 5.5
工具使用
思考控制 可配置 始终开启
结构化输出 -
提示词缓存 隐式 + 显式 显式(由您标记前缀)
缓存生存时间 未公开 5m default, 1h option
最小缓存前缀 1024 个 token 1024 个 token

规格

Dola Seed 2.0 Pro Claude Opus 5.5
输入模态 文本 图像 视频 文本 图像
输出模态 文本 文本
发布日期 2026-02-14 2026-09-22
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
允许的值
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 and thinking summary on medium

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: Dola Seed 2.0 Pro · Claude Opus 5.5

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

Dola Seed 2.0 Pro 通过 · 3 sentences

Reasoning-enabled LLMs do not generate final answers in one pass: they run hundreds of unshown internal trial, cross-check, and correction steps for every user request, and every one of these hidden steps consumes billable GPU time just like the visible output text. Unlike our prior standard chat workload which only processed exactly what users typed, reasoning mode automatically executes these validation loops by default, with no visible indicator to end users that extra compute is being consumed. This 2x increase is not a billing error or rogue usage spike: industry benchmarks consistently put reasoning workloads at 1.7-2.6x the compute cost of equivalent standard LLM requests, and this pricing dynamic is almost never clearly disclosed on vendor rate cards.

输出 563 tok (+416 思考) 延迟 9.4 s

Claude Opus 5.5 通过 · 3 sentences

When we turned on reasoning, the model began "thinking out loud" before each answer, generating thousands of internal working tokens that users never see but that we are billed for just like the final response. Those hidden tokens are charged at the output rate, typically the most expensive rate, so the same question with the same visible answer now costs roughly twice as much or more. We can bring the bill back down without losing the quality gains by capping the reasoning budget and reserving reasoning mode for the complex tasks that actually benefit from it.

输出 263 tok (+102 思考) 延迟 5.7 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

Dola Seed 2.0 Pro 通过 · 8/8 cases

### Corrected Implementation ```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 ``` --- ### Bug Explanation The original code has three critical linked flaws: 1. It initialized run counters `best` and `cur` to `0` instead of `1`, failing to account for the fact that *every individual element is already a valid run of length 1* -- this causes single-element inputs to incorrectly return `0`. 2. `cur` only counted how many extra matching elements followed the start of a run, so it always tracked one less than the actual run length, and it reset to `0` (instead of `1`) when a new run began, preserving this off-by-one error for all runs. 3. […]

输出 1278 tok (+951 思考) 延迟 16.7 s

Claude Opus 5.5 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 ``` **Explanation:** The original code counted *matching adjacent pairs* rather than *items in a run*. Both `cur` and `best` started at 0, and `cur` reset to 0 on a mismatch, so a run of length *n* was reported as *n − 1*. For a single-element list the loop never executes, so it returned 0 instead of 1. For `[5, 5]` it returned 1 instead of 2. The fix treats every element as the start of a run of length 1: `best` and `cur` start at 1, and `cur` resets to 1 (not 0) when the value changes. An explicit check returns 0 for an empty list. The "misses runs at the last index" reports are the same off-by-one bug rather than a separate one. […]

输出 723 tok (+303 思考) 延迟 10.4 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

Dola Seed 2.0 Pro 通过 · 5/5 fields, guidance null

{ "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": 11, "guidance": null }

输出 506 tok (+452 思考) 延迟 8.6 s

Claude Opus 5.5 通过 · 5/5 fields, guidance "Withheld pending Q4 close"

```json { "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": 11, "guidance": "Withheld pending Q4 close" } ``` **Judgment calls to check:** - **quarter**: Kept as "September quarter" rather than "Q3", since the fiscal calendar isn't stated. The text mentions an upcoming "Q4 close," so it may be fiscal Q3, but that's not certain. - **revenue_usd_m**: The text says only "412 for the topline." It doesn't state currency or units, and the figure is on a constant-currency basis, not reported USD. USD millions is an assumption. Use `null` if you need strictly stated values. - **growth_pct**: The source says "eleven and change," so the true figure is slightly above 11. 11 is an approximation. - **guidance**: Guidance was explicitly withheld. I recorded that rather than `null`, since the text addresses it directly.

输出 829 tok (+488 思考) 延迟 10.9 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

Dola Seed 2.0 Pro 通过 · 123 words, 0 banned, 1 question

Today we’re rolling out cross-provider prompt caching for our API gateway. For engineering teams routing LLM requests across OpenAI, Anthropic, Mistral and open source models, this feature stores identical prompt payloads at the gateway layer, rather than relying on per-provider cache implementations limited to single endpoints. How much time and compute could your team save by avoiding redundant token processing for repeated system prompts, context windows, or common user queries? Cache hits return responses in under 10ms, with configurable TTL, granular purge controls, and per-application cache partitioning. Early access teams running support bots, batch inference and internal assistants recorded 42-67% lower LLM spend. This feature is live for all gateway users today, with no required code changes to existing routing workflows. (120 words)

输出 1041 tok (+872 思考) 延迟 11.4 s

Claude Opus 5.5 通过 · 120 words, 0 banned, 1 question

Today we're launching Cross-Provider Prompt Caching for our API gateway. Why pay twice for the same context? Our gateway now stores repeated prompt prefixes, system instructions, and document chunks, then reuses them whether your request goes to OpenAI, Anthropic, Google, or an open-source model. Teams running retrieval pipelines or long agent loops can cut token costs by up to 60 percent and trim latency on every cached call. Setup takes one configuration flag; no code changes are required. Cache rules respect tenant boundaries, expire on your schedule, and log every hit for audit review. Dashboards show savings by provider, route, and application in real time. Cross-Provider Prompt Caching is available on all Pro and Enterprise plans. Turn it on today.

输出 1263 tok (+1007 思考) 延迟 13.5 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="Dola-Seed-2.0-pro",
    # model="claude-opus-5-5",  # 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

Dola Seed 2.0 Pro 和 Claude Opus 5.5 哪个更便宜?

在 输入 / 1m tokens 方面,Dola Seed 2.0 Pro 更便宜($0.5 对比 $4,相差 8.0×)。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 Dola Seed 2.0 Pro 和 Claude Opus 5.5 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。

Dola Seed 2.0 Pro 和 Claude Opus 5.5 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