Dola Seed 2.0 Lite vs DeepSeek V4 Pro

Q: Dola Seed 2.0 Lite 和 DeepSeek V4 Pro 哪個比較便宜？

Dola Seed 2.0 Lite 在 輸入 / 1m tokens 上較便宜（$0.25 對比 $1.608，相差 6.4×）。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。

更換任一側 vs

何時該用哪一個 — 綜合評斷，而非基準測試數據表

低成本多模態工作選 Dola-Seed-2.0-lite：它接受影像、視訊和音訊輸入，$0.25 輸入 / $2 輸出，每輸入 token 比 deepseek-v4-pro 的 $1.608 / $3.216 便宜約 6.4 倍，每輸出 token 便宜約 1.6 倍。deepseek-v4-pro 僅處理文字但帶推理標誌，當你需要它的 1000000 token 脈絡和 393216 token 輸出上限（對比 256000 和 131072），或需要每百萬 $0.0134 的重度提示詞快取（對比 $0.05）時選它。兩者都允許關閉思考。

定價

	Dola Seed 2.0 Lite	DeepSeek V4 Pro	Δ
輸入 / 1M tokens	$0.25	$1.608	0.16×
輸出 / 1M tokens	$2	$3.216	0.62×
快取讀取 / 1M tokens	$0.05	$0.0134	3.7×
快取寫入	—	不額外計費	—

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 63 個聊天模型的每 1M tokens 的輸入價格（對數尺度）

Dola Seed 2.0 Lite · $0.25 DeepSeek V4 Pro · $1.608

$0.05 · Qwen3 VL Flash $30 · GPT-5.4 Pro

能力

	Dola Seed 2.0 Lite	DeepSeek V4 Pro
工具使用	是	是
思考控制	可配置	可配置
結構化輸出	是	是
提示快取	隱式 + 顯式	隱式（自動）
快取生命週期	未公開	no fixed TTL (evicted when unused)
最小快取前綴	1024 個 token	未公開

規格

	Dola Seed 2.0 Lite	DeepSeek V4 Pro
輸入模態	文字影像音訊影片	文字
輸出模態	文字	文字
發布日期	2026-02-14	2026-04-24
上下文視窗	256K	1M
最大輸出	131K	393K
思考參數	thinking.type reasoning_effort	thinking.type reasoning_effort
可接受的值	thinking.type enabled disabled (no auto) reasoning_effort minimal low medium high	thinking.type enabled disabled reasoning_effort high max (low and medium map to high, xhigh maps to max)
預設值	enabled, with reasoning_effort medium	enabled, with reasoning_effort high some complex agent requests are automatically set to max

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 Lite · DeepSeek V4 Pro

單一提示詞，兩款模型 — 經由閘道測量

針對 CFO 的推論修復包含錯誤的函式擷取至 schema 依照限制條件撰寫

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

DeepSeek V4 Pro 通過 · 3 sentences

Enabling reasoning causes the model to generate a lengthy internal chain‑of‑thought before it delivers a visible answer, and we are billed for every one of those hidden reasoning tokens just as we are for the final output. In most interactions, the volume of these unseen tokens at least equals the visible response, effectively doubling the total tokens consumed per query. That doubled token consumption translates directly into a doubled per‑query cost, which is why our overall LLM bill doubled.

輸出 641 tok (+545 思考) 延遲 11.5 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 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

DeepSeek V4 Pro 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 bug stems from initializing `cur = 0` and only incrementing it when two adjacent elements match, which counts the number of *additional* equal elements rather than the actual run length (which is `cur + 1`). As a result, the returned `best` is always one less than the true longest run—most obviously returning 0 for a single-element list instead of 1. […]

輸出 2418 tok (+2131 思考) 延遲 35.6 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 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

DeepSeek V4 Pro 通過 · 5/5 fields, guidance null

{ "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": "eleven and change", "guidance": null }

輸出 1204 tok (+1153 思考) 延遲 19.9 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 Lite 未命中 · 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

DeepSeek V4 Pro 沒有可評分的回答 · no answer text within 16,384 tokens (all of it went to thinking)

模型未回傳任何回答文字——所有 token 額度皆耗費於隱藏思考。

輸出 8193 tok (+8192 思考) 延遲 106.2 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-lite",
    # model="deepseek-v4-pro",  # 取消註解此行，並註解上一行
    messages=[{"role": "user", "content": "Summarize this diff"}],
)
print(resp.choices[0].message.content)

import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://synthorai.io/v1",
  apiKey: "sk-syn-...",
});

const resp = await client.chat.completions.create({
  model: "Dola-Seed-2.0-lite",
  // model: "deepseek-v4-pro",  // 取消註解此行，並註解上一行
  messages: [{ role: "user", content: "Summarize this diff" }],
});
console.log(resp.choices[0].message.content);

curl https://synthorai.io/v1/chat/completions \
  -H "Authorization: Bearer sk-syn-..." \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Dola-Seed-2.0-lite",
    # "model": "deepseek-v4-pro",  # 取消註解此行，並註解上一行
    "messages": [{"role": "user", "content": "Hello"}]
  }'

package main

import (
	"context"
	"fmt"

	"github.com/openai/openai-go/v3"
	"github.com/openai/openai-go/v3/option"
)

func main() {
	client := openai.NewClient(
		option.WithBaseURL("https://synthorai.io/v1"),
		option.WithAPIKey("sk-syn-..."),
	)
	resp, _ := client.Chat.Completions.New(context.TODO(), openai.ChatCompletionNewParams{
		Model: "Dola-Seed-2.0-lite",
		// Model: "deepseek-v4-pro",  // 取消註解此行，並註解上一行
		Messages: []openai.ChatCompletionMessageParamUnion{
			openai.UserMessage("Summarize this diff"),
		},
	})
	fmt.Println(resp.Choices[0].Message.Content)
}

import com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;
import com.openai.models.chat.completions.*;

OpenAIClient client = OpenAIOkHttpClient.builder()
    .baseUrl("https://synthorai.io/v1")
    .apiKey("sk-syn-...")
    .build();

ChatCompletion resp = client.chat().completions().create(
    ChatCompletionCreateParams.builder()
        .model("Dola-Seed-2.0-lite")
        // .model("deepseek-v4-pro")  // 取消註解此行，並註解上一行
        .addUserMessage("Summarize this diff")
        .build());
System.out.println(resp.choices().get(0).message().content().orElse(""));

取得 API 金鑰 →

常見問題

Dola Seed 2.0 Lite 和 DeepSeek V4 Pro 哪個比較便宜？

Dola Seed 2.0 Lite 在輸入 / 1m tokens 上較便宜（$0.25 對比 $1.608，相差 6.4×）。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 Lite 和 DeepSeek V4 Pro 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 Lite 與 DeepSeek V4 Pro 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。

定價

能力

規格

單一提示詞，兩款模型 — 經由閘道測量

只需一行程式碼即可在兩者間切換

常見問題

相關比較