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Dola Seed 2.0 Pro vs Claude Haiku 5.5

vs

什麼情況選哪一個

claude-haiku-5-5 比較便宜,容量也更大:輸入 $0.1、輸出 $0.5,對比 Dola-Seed-2.0-pro 的 $0.5 與 $3,輸入便宜 5 倍、輸出便宜 6 倍,上下文視窗為 1000000 個 token 對比 256000。兩者都可以關閉思考,長提示詞也都會加價:claude-haiku-5-5 的提示詞超過 100000 個 token 後按 5 倍計費,輸入價因此與 Dola-Seed-2.0-pro 持平,輸出價仍然較低;Dola-Seed-2.0-pro 則在超過 128000 後費率加倍。Dola-Seed-2.0-pro 接受影片輸入,claude-haiku-5-5 不接受。重視成本與長輸入時選 claude-haiku-5-5,輸入是影片時選 Dola-Seed-2.0-pro。

定價

Dola Seed 2.0 Pro Claude Haiku 5.5 Δ
輸入 / 1M tokens $0.5 $0.1 5×
輸出 / 1M tokens $3 $0.5 6×
快取讀取 / 1M tokens $0.1 $0.01 10×
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 78 個聊天模型(對數尺度)

功能

Dola Seed 2.0 Pro Claude Haiku 5.5
工具使用 是 是
思考控制 可設定 可設定
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 1024 個 token 1024 個 token

規格

Dola Seed 2.0 Pro Claude Haiku 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 2026-10-07
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 Haiku 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 Haiku 5.5 通過 · 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.

輸出 332 tok (+159 思考) 延遲 2.9 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 Haiku 5.5 通過 · 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 `[]`. […]

輸出 1012 tok (+591 思考) 延遲 6.0 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 Haiku 5.5 通過 · 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.

輸出 725 tok (+572 思考) 延遲 4.6 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 Haiku 5.5 通過 · 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.

輸出 2164 tok (+1929 思考) 延遲 9.9 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-haiku-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 Haiku 5.5 哪個比較便宜?

以「輸入 / 1M tokens」來看,Claude Haiku 5.5 比較便宜($0.1 對 $0.5,相差 5.0×)。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 Haiku 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 Haiku 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。

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