Dola Seed 2.0 Pro vs Claude Opus 5.5

vs

什麼情況選哪一個

Dola-Seed-2.0-pro 每百萬 token 輸入費用為 $0.5,輸出為 $3,而 claude-opus-5-5 則分別為 $4 與 $20,因此前者的輸入費率低了 8x,輸出費率約低 6.7x,且它是兩者中唯一支援影片輸入並允許您停用思考的模型。當您需要 claude-opus-5-5 的 1,000,000 token 脈絡長度(大約是 Dola-Seed-2.0-pro 256,000 視窗的 3.9x),且可接受推理功能始終開啟以及高出 2x 的快取讀取費率($0.2 對比 $0.1)時,請選擇 claude-opus-5-5。兩者皆按 token 計費且無折扣乘數,因此成本比較非常直接。

Benchmark 成績

Dola Seed 2.0 Pro:供應商沒有公布 benchmark 成績。

高於平均沒有模型更高Claude Opus 5.59 / 97 / 9
Dola Seed 2.0 Pro Claude Opus 5.5 其他有成績的模型 其他模型平均 ★ 沒有模型分數更高
Terminal-bench 4.0
N/A
沒有模型分數更高 66.4%
OSWorld 2.0 partial
N/A
沒有模型分數更高 81.8%
Terminal-Bench-Science 0.1
N/A
58.7%
Humanity's Last Exam with tools
N/A
沒有模型分數更高 67.7%
AutomationBench
N/A
40%
Chartography with tools
N/A
沒有模型分數更高 89%

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

定價

Dola Seed 2.0 Pro Claude Opus 5.5 Δ
輸入 / 1M tokens $0.5 $4 0.13×
輸出 / 1M tokens $3 $20 0.15×
快取讀取 / 1M tokens $0.1 $0.2 0.5×
快取寫入 - 1.25x (5m) / 2x (1h) -

費率取自網站建置時的即時目錄;各模型頁面都列有最新的價目。

兩者的相對位置:每 1M tokens 的輸入價格,涵蓋同一計費單位下全部 83 個聊天模型(對數尺度)

功能

Dola Seed 2.0 Pro Claude Opus 5.5
工具使用 是 是
思考控制 可設定 一律開啟
結構化輸出 - 是
提示詞快取 隱式 + 顯式 顯式(由你標記前綴)
快取存活時間 未公布 5m default, 1h option
最小快取前綴 1,024 個 token 512 個 token

規格

Dola Seed 2.0 Pro Claude Opus 5.5
輸入模態 文字 圖像 影片 文字 圖像
輸出模態 文字 文字
發布日期 2026-02-14 2026-09-22
知識截止日期 - 2026-06
上下文視窗 256K 1M
最大輸出 131K 128K
思考參數
  • thinking.type
  • reasoning_effort
output_config.effort
可接受的值
thinking.type
  • enabled
  • disabled (no auto)
reasoning_effort
  • minimal
  • low
  • medium
  • high
output_config.effort
  • low
  • medium
  • high
  • xhigh
  • max
預設值 enabled, with reasoning_effort medium and thinking summary on medium

規格摘錄自各供應商的文件;供應商沒有公布的項目就直接略過,不自行推測。 完整來源: Dola Seed 2.0 Pro · Claude Opus 5.5

同一段提示詞,兩個模型,經閘道實測

提示詞 Explain to a CFO, in exactly three sentences, why our LLM bill doubled after we enabled reasoning. 檢查 恰好 3 句

Dola Seed 2.0 Pro 通過 · 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.

輸出 563 tok (+416 思考) 延遲 9.4 s

Claude Opus 5.5 通過 · 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.

輸出 263 tok (+102 思考) 延遲 5.7 s

是否照指令做(剛好三句,數得出來)、語氣是否貼合對象(寫給 CFO 看),以及下方 token 計量顯示出的隱藏思考計費落差。

提示詞 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. 檢查 修正後通過測試

Dola Seed 2.0 Pro 通過 · 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. […]

輸出 1278 tok (+951 思考) 延遲 16.7 s

Claude Opus 5.5 通過 · 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. […]

輸出 723 tok (+303 思考) 延遲 10.4 s

修正是否真的正確(跑得起來)、說明的資訊密度,以及在範圍明確的任務上的 token 效率。

提示詞 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. 檢查 有效的 JSON,完全符合 schema

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

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

輸出 506 tok (+452 思考) 延遲 8.6 s

Claude Opus 5.5 通過 · 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.

輸出 829 tok (+488 思考) 延遲 10.9 s

是否遵守 schema(不自創欄位)、面對幻覺誘因的表現(題目明確不給 guidance),以及結構化輸出路徑的差異。

提示詞 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. 檢查 120 詞,0 個禁用詞

Dola Seed 2.0 Pro 通過 · 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)

輸出 1041 tok (+872 思考) 延遲 11.4 s

Claude Opus 5.5 通過 · 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.

輸出 1263 tok (+1007 思考) 延遲 13.5 s

是否遵守限制(字數上限、禁用詞清單、只能有一句問句)、文字風格的特徵,以及長度控制。

改一行程式碼就能在兩者之間切換

下面每個頁籤都列了這兩個模型 ID,要改的只有醒目標示的那兩行。端點、金鑰和請求格式都不變。

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",  # 取消這一行的註解,並把上一行註解掉
    messages=[{"role": "user", "content": "Summarize this diff"}],
    reasoning_effort="medium",
)
print(resp.choices[0].message.content)

取得 API 金鑰 →

常見問題

Dola Seed 2.0 Pro 和 Claude Opus 5.5 哪個比較便宜?

以「輸入 / 1M tokens」來看,Dola Seed 2.0 Pro 比較便宜($0.5 對 $4,相差 8.0×)。其他項目的結果可能相反,完整價目請看上表;實際成本要看你的用量組合。

可以只串接一次,就對 Dola Seed 2.0 Pro 和 Claude Opus 5.5 做 A/B 測試嗎?

可以。兩個模型都走同一個 OpenAI 相容端點,用的也是同一把 API 金鑰,切換時只要改一行裡的模型名稱字串。你可以把一部分流量分別導到兩邊,再直接比較帳單。

Dola Seed 2.0 Pro 與 Claude Opus 5.5 支援提示詞快取嗎?

支援。兩者的快取讀取費率都低於輸入費率,所以前綴已經進快取的工作負載,實際成本會比官網價算出來的低。確切的快取讀取價格請見上方定價表。

相關比較