Dola Seed 2.0 Lite vs Gemini 3.6 Flash
什麼情況選哪一個
兩者都接受文字、影像、視訊和音訊輸入並輸出文字，所以差別在價格和脈絡：Dola-Seed-2.0-lite 每百萬輸入 $0.25、輸出 $2，比 gemini-3.6-flash 的 $1.5 和 $7.5 便宜 6 倍和 3.75 倍，並且可以關閉思考、最多輸出 131072 token。需要 1048576 token 脈絡或視覺標誌時選 gemini-3.6-flash；注意它的音訊按每百萬輸入 token $5 單獨計費（快取 $0.5），而 Dola-Seed-2.0-lite 完全沒有公布單獨的音訊費率。
Benchmark 成績
供應商公布： Alibaba (Qwen) Anthropic ByteDance DeepSeek Google MiniMax Moonshot OpenAI Tencent Z.ai
定價
| Dola Seed 2.0 Lite | Gemini 3.6 Flash | Δ | |
|---|---|---|---|
| 輸入 / 1M tokens | $0.25 | $1.5 | 0.17× |
| 輸出 / 1M tokens | $2 | $7.5 | 0.27× |
| 快取讀取 / 1M tokens | $0.05 | $0.15 | 0.33× |
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 | Gemini 3.6 Flash | |
|---|---|---|
| 工具使用 | 是 | 是 |
| 思考控制 | 可設定 | 一律開啟 |
| 結構化輸出 | 是 | 是 |
| 提示詞快取 | 隱式 + 顯式 | 隱式 + 顯式 |
| 快取存活時間 | 未公布 | 未公布 |
| 最小快取前綴 | 1024 個 token | 4096 個 token |
規格
| Dola Seed 2.0 Lite | Gemini 3.6 Flash | |
|---|---|---|
| 輸入模態 | 文字 圖像 音訊 影片 | 文字 圖像 音訊 影片 |
| 輸出模態 | 文字 | 文字 |
| 發布日期 | 2026-02-14 | 2026-07-21 |
| 知識截止日期 | - | 2026-03 |
| 上下文視窗 | 256K | 1M |
| 最大輸出 | 131K | 66K |
| 思考參數 |
| thinkingLevel |
| 可接受的值 | thinking.type
reasoning_effort
|
|
| 預設值 | enabled, with reasoning_effort medium | 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 Lite · Gemini 3.6 Flash
同一段提示詞，兩個模型，經閘道實測
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 3.6 Flash 通過 · 3 sentences
Enabling reasoning causes the model to generate thousands of internal "thinking" tokens behind the scenes to process logic before delivering the final response. Because our vendor bills us for every token generated—including these intermediate processing steps—our billable output volume per query surged significantly. Since output tokens carry a much higher unit cost than input tokens, this jump in processing volume directly doubled our overall spend.
輸出 893 tok (+816 思考) 延遲 4.7 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。
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 3.6 Flash 通過 · 8/8 cases
Here is the fixed function along with an explanation of the bugs: ```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 The original function initialized `cur` and `best` to `0` and incremented `cur` on matching adjacent items, which meant it was counting identical adjacent *pairs* rather than the total number of *elements* in a run. This caused single-element inputs to skip the loop and return `0` instead of `1`, and undercounted all run lengths by 1 (for example, a run of two items at the end of the list resulted in `cur = 1`). […]
輸出 2034 tok (+1768 思考) 延遲 10.0 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。
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 3.6 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 2843 tok (+2783 思考) 延遲 13.1 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。
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
Gemini 3.6 Flash 未通過 · 60 words, 0 banned, 0 questions
72: reducing 73: your 74: monthly 75: token 76: spend. S6 (21): 77: You 78: can 79: easily 80: set 81: custom 82: expiration 83: rules, 84: configure 85: TTL 86: settings, 87: and 88: manage 89: cache 90: invalidation 91: across 92: all 93: vendors 94: from 95: one 96: centralized 97: dashboard. S7 (23): 98: Start 99: optimizing […]
輸出 4092 tok (+3929 思考) 延遲 16.3 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="gemini-3.6-flash", # 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)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-3.6-flash", // 取消這一行的註解，並把上一行註解掉
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-3.6-flash", # 取消這一行的註解，並把上一行註解掉
"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-3.6-flash", // 取消這一行的註解，並把上一行註解掉
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-3.6-flash") // 取消這一行的註解，並把上一行註解掉
.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 3.6 Flash 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 $1.5，相差 6.0×）。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 Lite 和 Gemini 3.6 Flash 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 Gemini 3.6 Flash 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。