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Dola Seed 2.0 Lite vs Claude Opus 5

vs

什麼情況選哪一個

Dola-Seed-2.0-lite 是較便宜的接入路徑:每百萬輸入 $0.25、輸出 $2,分別比 claude-opus-5 的 $5 和 $25 低 20 倍和 12.5 倍,而且它是兩者中唯一在文字與影像之外還接受視訊與音訊的。需要 100 萬 token 脈絡(對比 256000)或它宣告的思考與推理能力時選 claude-opus-5,並為 $0.5 的快取讀取費率(對比 $0.05)預留預算。兩者都只輸出文字、都允許關閉思考、都按相同的每百萬 token 單位計費,所以真正的取捨是輸入廣度與脈絡加推理標誌之間。

Benchmark 成績

高於平均沒有模型更高Dola Seed 2.0 Lite4 / 103 / 10Claude Opus 538 / 468 / 46
Dola Seed 2.0 Lite Claude Opus 5 其他有成績的模型 其他模型平均 ★ 沒有模型分數更高
DeepSWE 1.1
N/A
68.8%
WenetSpeech test-net (CER)
沒有模型分數更高 4.47%
N/A
BioMysteryBench hard
N/A
49.4%
OSWorld-Verified
64.4%
N/A
ExploitBench (Cap%)
N/A
70%
HealthBench Professional
N/A
59.8%
Finance Agent v2
N/A
58.6%
Legal Agent Benchmark
N/A
6.7%
GPQA Diamond
88.4%
93.4%
BrowseComp
64%
90.8%
LVBench
N/A
75.4%

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

定價

Dola Seed 2.0 Lite Claude Opus 5 Δ
輸入 / 1M tokens $0.25 $5 0.05×
輸出 / 1M tokens $2 $25 0.08×
快取讀取 / 1M tokens $0.05 $0.5 0.1×
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 76 個聊天模型(對數尺度)

功能

Dola Seed 2.0 Lite Claude Opus 5
工具使用 是 是
思考控制 可設定 可設定
結構化輸出 是 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 Lite Claude Opus 5
輸入模態 文字 圖像 音訊 影片 文字 圖像
輸出模態 文字 文字
發布日期 2026-02-14 2026-07-24
知識截止日期 - 2026-05
上下文視窗 256K 1M
最大輸出 131K 128K
思考參數
  • thinking.type
  • reasoning_effort
  • thinking.type
  • output_config.effort
可接受的值
thinking.type
  • enabled
  • disabled (no auto)
reasoning_effort
  • minimal
  • low
  • medium
  • high
thinking.type
  • adaptive
  • disabled
effort
  • low
  • medium
  • high
  • xhigh
  • max
預設值 enabled, with reasoning_effort medium

thinking on

effort
  • high (Claude API and Claude Code)

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 Lite · Claude Opus 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 Lite 通過 · 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. […]

輸出 1306 tok (+1096 思考) 延遲 18.2 s

Claude Opus 5 通過 · 3 sentences

When reasoning is enabled, the model generates a hidden chain of "thinking" tokens before it writes the visible answer, and those internal tokens are billed at the output rate even though no one ever sees them — commonly 3–10x more tokens than the response itself. Because output tokens are priced several times higher than input tokens, that added volume hits us at our most expensive rate, which is why the bill roughly doubled while our request count stayed flat. The spend is controllable: we can cap the reasoning budget per call, route only genuinely hard requests to reasoning mode and cheaper models for the rest, and track cost per successfully completed task rather than cost per token, since a single correct answer usually beats three cheap retries plus human cleanup.

輸出 529 tok 延遲 9.0 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 Lite 通過 · 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. […]

輸出 2620 tok (+2217 思考) 延遲 33.2 s

Claude Opus 5 通過 · 8/8 cases

## Fixed version ```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 else: cur = 1 if cur > best: best = cur return best ``` ## The bug The counters were tracking the wrong quantity: `cur` counted *matching adjacent pairs* rather than *elements in the current run*, and both counters started at `0` instead of `1`. Since a run of length *n* contains only *n − 1* adjacent equal pairs, every result came back one too small — `[1, 1]` returned 1, `[3, 3, 3]` returned 2 — and a single-element list returned 0 because the loop body never executed and `best` kept its initial value of `0`. […]

輸出 1094 tok 延遲 16.1 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 Lite 通過 · 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" }

輸出 1502 tok (+1439 思考) 延遲 20.2 s

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

```json { "company": "Meridian Robotics", "quarter": "Q3", "revenue_usd_m": 412, "growth_pct": 11, "guidance": "Withheld pending Q4 close" } ``` Notes on judgment calls: - **quarter**: "September quarter" is labeled Q3 because the text refers to an upcoming "Q4 close," implying a calendar-aligned fiscal year. No fiscal year is given. - **revenue_usd_m**: 412 is stated as constant-currency and hedged ("call it"), so it's approximate; the currency is not explicitly stated as USD. - **growth_pct**: "up eleven and change" means slightly more than 11, so 11 is a floor rather than an exact figure.

輸出 539 tok 延遲 7.6 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 Lite 未通過 · 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. […]

輸出 1870 tok (+1695 思考) 延遲 23.1 s

Claude Opus 5 通過 · 123 words, 0 banned, 1 question

**Cross-Provider Prompt Caching** We're introducing Cross-Provider Prompt Caching in the API Gateway. Repeated system prompts, long documents, and few-shot examples are stored once at the gateway layer and reused across OpenAI, Anthropic, Google, and self-hosted models. Instead of paying full input token costs on every request, your application sends a cache reference, and the gateway rehydrates the context before forwarding it downstream. Why does that matter? Teams running high-volume agents and retrieval pipelines typically see input token spend fall 40 to 70 percent, with median latency dropping by several hundred milliseconds. Caches are scoped per project, encrypted at rest, and invalidated automatically when a prompt template changes. Enable it with a single header, and see the docs for TTL tuning and per-route controls.

輸出 1593 tok 延遲 19.1 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-lite",
    # model="claude-opus-5",  # 取消這一行的註解,並把上一行註解掉
    messages=[{"role": "user", "content": "Summarize this diff"}],
)
print(resp.choices[0].message.content)

取得 API 金鑰 →

常見問題

Dola Seed 2.0 Lite 和 Claude Opus 5 哪個比較便宜?

以「輸入 / 1M tokens」來看,Dola Seed 2.0 Lite 比較便宜($0.25 對 $5,相差 20×)。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 Lite 和 Claude Opus 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 Lite 與 Claude Opus 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