Dola Seed 2.0 Lite vs Gemini 3.7 Flash
何時該用哪一個 — 綜合評斷，而非基準測試數據表
兩者都接受文字、影像、音訊和視訊並輸出文字，所以差別其實在規模和價格：Dola-Seed-2.0-lite 每百萬輸入 $0.25、輸出 $2，脈絡 256000、最多輸出 131072 token；gemini-3.7-flash 輸入貴 3 倍、輸出貴 1.875 倍（$0.75 和 $3.75），換來 4 倍大的 1048576 token 脈絡。低成本大批量對話、程式碼和工具呼叫、超長生成，或想關閉思考時選 Dola-Seed-2.0-lite；單一提示詞必須裝下超大語料，或需要它的推理與視覺標誌時選 gemini-3.7-flash。
定價
| Dola Seed 2.0 Lite | Gemini 3.7 Flash | Δ | |
|---|---|---|---|
| 輸入 / 1M tokens | $0.25 | $0.75 | 0.33× |
| 輸出 / 1M tokens | $2 | $3.75 | 0.53× |
| 快取讀取 / 1M tokens | $0.05 | $0.075 | 0.67× |
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）
能力
| Dola Seed 2.0 Lite | Gemini 3.7 Flash | |
|---|---|---|
| 工具使用 | 是 | 是 |
| 思考控制 | 可配置 | 是 —— 廠商未公布調節參數 |
| 結構化輸出 | 是 | 是 |
| 提示快取 | 隱式 + 顯式 | 隱式 + 顯式 |
| 快取生命週期 | 未公開 | 未公開 |
| 最小快取前綴 | 1024 個 token | 4096 個 token |
規格
| Dola Seed 2.0 Lite | Gemini 3.7 Flash | |
|---|---|---|
| 輸入模態 | 文字 影像 音訊 影片 | 文字 影像 音訊 影片 |
| 輸出模態 | 文字 | 文字 |
| 發布日期 | 2026-02-14 | 2026-08-13 |
| 上下文視窗 | 256K | 1M |
| 最大輸出 | 131K | 66K |
| 思考參數 |
| — |
| 可接受的值 | thinking.type
reasoning_effort
| — |
| 預設值 | enabled, with reasoning_effort medium | — |
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 · Gemini 3.7 Flash
單一提示詞，兩款模型 — 經由閘道測量
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
Gemini 3.7 Flash 通過 · 3 sentences
Enabling reasoning causes the model to generate a high volume of invisible "thinking tokens" behind the scenes to deliberate and solve complex logic before delivering a final response. Because our vendor bills strictly on token volume, this additional internal computation effectively doubled our billable output per query. In short, our costs increased because we are now paying for the extra compute capacity required to produce significantly higher-accuracy answers.
輸出 878 tok (+799 思考) 延遲 6.9 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。
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
Gemini 3.7 Flash 通過 · 8/8 cases
Here is the corrected function: ```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 best = max(best, cur) return best ``` ### Explanation of the Bug The original function tracked equal adjacent transitions instead of the actual number of elements in a run, causing all non-empty run lengths to be undercounted by one (e.g., returning `0` instead of `1` for single-element lists because the loop never executes, and `1` instead of `2` for pairs). Initializing both `best` and `cur` to `1` (after handling empty inputs) and resetting `cur` to `1` on a mismatch ensures every individual element is counted as a valid run of at least length one.
輸出 1050 tok (+827 思考) 延遲 6.4 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。
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
Gemini 3.7 Flash 通過 · 5/5 fields, guidance "withheld"
```json { "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": null, "guidance": "withheld" } ```
輸出 918 tok (+858 思考) 延遲 6.2 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。
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
Gemini 3.7 Flash 通過 · 120 words, 0 banned, 1 question
Why pay twice for identical AI queries simply because you routed them to different model vendors? Today, we introduce Universal Prompt Caching directly within our unified API gateway architecture. This capability stores repeated prompt contexts across OpenAI, Anthropic, and local models, instantly returning stored results to eliminate redundant computation fees. When your application sends an LLM request, the gateway inspects the payload, identifies semantic matches, and returns accurate cached responses in under ten milliseconds. Engineering teams can now slash inference latency by eighty percent while dramatically reducing monthly token expenditures across diverse production deployments. You retain complete privacy control, flexible cache eviction policies, and granular metrics through a single dashboard. Update your routing settings today to accelerate overall system performance.
輸出 2858 tok (+2718 思考) 延遲 14.1 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="gemini-3.7-flash", # 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: "gemini-3.7-flash", // 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": "gemini-3.7-flash", # 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: "gemini-3.7-flash", // 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("gemini-3.7-flash") // 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(""));常見問題
Dola Seed 2.0 Lite 和 Gemini 3.7 Flash 哪個比較便宜？
Dola Seed 2.0 Lite 在 輸入 / 1m tokens 上較便宜（$0.25 對比 $0.75，相差 3.0×）。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 Lite 和 Gemini 3.7 Flash 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 Gemini 3.7 Flash 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。