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Dola Seed 2.0 Pro vs Claude Sonnet 5.5

vs

何時用哪一個

Dola-Seed-2.0-pro 是較便宜的選項,每百萬輸入為 $0.5、輸出為 $3,而 claude-sonnet-5-5 為 $2 與 $10;即輸入便宜 4x,輸出便宜約 3.3x,快取讀取為 $0.1(相對於 $0.2),且它是兩者中唯一在文字與圖片之外接受影片的選項,此外其思考功能可以關閉。當您需要 1,000,000-token 的脈絡長度而非 256,000 時,請選擇 claude-sonnet-5-5;其最大輸出為 128,000 token(相對於 131,072)。兩者皆涵蓋聊天、程式碼、推理與工具,且僅回傳文字。

定價

Dola Seed 2.0 Pro Claude Sonnet 5.5 Δ
輸入 / 1M tokens $0.5 $2 0.25×
輸出 / 1M tokens $3 $10 0.3×
快取讀取 / 1M tokens $0.1 $0.2 0.5×
快取寫入 - 1.25x (5m) / 2x (1h) -

費率取自建置時的即時目錄;各模型頁面皆附有目前的費率卡。

它們的相對位置 — 在此計費單位下,所有 76 個 聊天 模型的 每 1M tokens 的輸入價格(對數尺度)

能力

Dola Seed 2.0 Pro Claude Sonnet 5.5
工具使用 是 是
思考控制 可配置 常駐開啟
結構化輸出 - 是
提示快取 隱式 + 顯式 顯式(由您標記前綴)
快取生命週期 未公開 5m default, 1h option
最小快取前綴 1024 個 token 1024 個 token

規格

Dola Seed 2.0 Pro Claude Sonnet 5.5
輸入模態 文字 影像 影片 文字 影像
輸出模態 文字 文字
發布日期 2026-02-14 2026-09-28
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
thinking.type
可接受的值
thinking.type
  • enabled
  • disabled (no auto)
reasoning_effort
  • minimal
  • low
  • medium
  • high
thinking.type
  • adaptive (default)
  • between_tools
預設值 enabled, with reasoning_effort medium and thinking summary on adaptive, effort high

規格摘錄自各供應商的文件;供應商未發布的資料列會直接省略,而非自行推測。 完整來源: Dola Seed 2.0 Pro · Claude Sonnet 5.5

單一提示詞,兩款模型 — 經由閘道測量

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 Sonnet 5.5 通過 · 3 sentences

Enabling reasoning makes the model "think" before it answers, and that internal thinking is generated as extra text (reasoning tokens) that we are billed for at the same rate as output tokens, even though users never see it. Output tokens also cost several times more than input tokens, so a single request that used to produce a short answer can now generate many times more billable text behind the scenes. Because our request volume and prompts didn't change, the doubling comes almost entirely from this hidden thinking, and we can bring it down by limiting reasoning effort, turning it off for simple tasks, and reserving it for the workloads where the accuracy gain justifies the cost.

輸出 205 tok 延遲 3.6 s

指令遵循(恰好三句,可數)、受眾適配(面向 CFO 的語氣),以及下方 token 計量所暴露的隱藏思考計費缺口。

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 修復通過測試

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 Sonnet 5.5 通過 · 8/8 cases

**The bug:** `cur` counts the number of *adjacent equal pairs* rather than the number of items in the run. A run of length k contains only k−1 such pairs, so every result is one too small. The counter also starts at 0 and the loop starts at index 1, so a single-element list never updates `best` and returns 0 instead of 1. (An empty list should return 0.) In addition, `cur` resets to 0 on a mismatch, when it should reset to 1, because the item at `xs[i]` begins a new run of length 1. The claim that it "misses runs ending at the last index" is really the same off-by-one: the final run is counted, but as one less than its true length. Because `best` is updated inside the loop on every extension, the last run isn't skipped. […]

輸出 444 tok 延遲 6.7 s

修復是否真的正確(可執行)、解釋的資訊密度,以及在一個邊界明確的任務上的 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 合法 JSON,schema 精確

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 Sonnet 5.5 通過 · 5/5 fields, guidance null

Here is the extracted JSON: ```json { "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": 11, "guidance": null } ``` Notes on the choices: - **quarter**: The text says "September quarter" without specifying a fiscal year or Q-number, so I kept the wording as stated. - **revenue_usd_m**: The text gives "412" for the topline, with no currency or unit stated explicitly. I've taken it as 412 million USD per the field definition, but the text does not confirm that, and the figure is constant-currency. - **growth_pct**: "Eleven and change" means slightly above 11, so 11 is an approximation. The exact figure isn't given. - **guidance**: Guidance is withheld pending the Q4 close, so no guidance is stated, hence null.

輸出 300 tok 延遲 3.5 s

schema 服從度(不臆造欄位)、幻覺壓力(guidance 明確被暫緩給出),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 Sonnet 5.5 通過 · 122 words, 0 banned, 1 question

**Introducing Cross-Provider Prompt Caching** Your API gateway now caches prompts across every connected LLM provider, whether you route traffic to OpenAI, Anthropic, Google, or a self-hosted model. Here's how it works: the gateway fingerprints each prompt prefix, stores it centrally, and applies the right provider-specific caching mechanism automatically. When you switch providers or fail over during an outage, your cached context follows you. Early testing shows input token costs dropping by up to 70% on repeated system prompts and long documents, with latency improvements of 40% or more on cache hits. Tired of paying to send the same instructions thousands of times a day? Enable it with one configuration flag. Cache rules, TTLs, and per-team isolation are available in the dashboard today.

輸出 266 tok 延遲 3.8 s

約束服從度(字數預算、禁用詞表、唯一的那句問句)、文風指紋,以及長度控制。

只需一行程式碼即可在兩者間切換

以下每個頁籤中都有這兩個 ID — 醒目提示的這兩行是唯一的修改處。相同的端點,相同的金鑰,相同的請求結構。

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-sonnet-5-5",  # 取消註解此行,並註解上一行
    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 Sonnet 5.5 哪個比較便宜?

Dola Seed 2.0 Pro 在 輸入 / 1m tokens 上較便宜($0.5 對比 $2,相差 4.0×)。其他項目可能呈現相反結果 — 上表提供完整資訊,實際成本取決於您的使用組合。

我可以在不進行兩次整合的情況下,對 Dola Seed 2.0 Pro 和 Claude Sonnet 5.5 進行 A/B 測試嗎?

可以。兩者皆透過同一個相容 OpenAI 的端點提供服務,並使用同一把 API 金鑰 — 切換只需更改一行的模型字串,因此您可以將部分流量分別導向兩者並直接比較帳單。

Dola Seed 2.0 Pro 與 Claude Sonnet 5.5 支援提示快取嗎?

是的 — 兩者的快取讀取費率皆低於其輸入費率,因此具有暖前綴的工作負載成本會低於牌價所示。確切的快取讀取列請見上方的定價表。

相關比較