Dola Seed 2.0 Pro vs DeepSeek V4.1 Flash (1017)
什麼情況選哪一個
若重視影片輸入，或想在對延遲敏感的呼叫中關閉思考功能，請選擇 Dola-Seed-2.0-pro；兩者之中，只有它接受影片輸入。其他情況下，deepseek-v4.1-flash-1017 的價格較低，容量也較大：輸入與輸出費用分別為 $0.3 和 $1.2，相較之下，Dola-Seed-2.0-pro 為 $0.5 和 $3，分別約為前者的 1.7 倍和 2.5 倍；上下文視窗為 1000000 個 token，對方為 256000；輸出上限為 393216 個 token，對方為 131072；每百萬個快取輸入 token 的讀取費用為 $0.006，而非 $0.1，對方約為其 17 倍。兩者皆支援聊天、程式碼、推論與工具，並以文字輸出，因此選擇取決於您較重視影片輸入，還是容量與價格。
定價
| Dola Seed 2.0 Pro | DeepSeek V4.1 Flash (1017) | Δ | |
|---|---|---|---|
| 輸入 / 1M tokens | $0.5 | $0.3 | 1.7× |
| 輸出 / 1M tokens | $3 | $1.2 | 2.5× |
| 快取讀取 / 1M tokens | $0.1 | $0.006 | 17× |
| 快取寫入 | - | 不額外計費 | - |
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 79 個聊天模型（對數尺度）
功能
| Dola Seed 2.0 Pro | DeepSeek V4.1 Flash (1017) | |
|---|---|---|
| 工具使用 | 是 | 是 |
| 思考控制 | 可設定 | 是，但供應商未公布調整參數 |
| 提示詞快取 | 隱式 + 顯式 | 隱式（自動） |
| 快取存活時間 | 未公布 | no fixed TTL (evicted when unused) |
| 最小快取前綴 | 1024 個 token | 未公布 |
規格
| Dola Seed 2.0 Pro | DeepSeek V4.1 Flash (1017) | |
|---|---|---|
| 輸入模態 | 文字 圖像 影片 | 文字 圖像 |
| 輸出模態 | 文字 | 文字 |
| 發布日期 | 2026-02-14 | 2026-10-17 |
| 上下文視窗 | 256K | 1M |
| 最大輸出 | 131K | 393K |
| 思考參數 |
| - |
| 可接受的值 | thinking.type
reasoning_effort
| - |
| 預設值 | enabled, with reasoning_effort medium and thinking summary on | - |
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 Pro · DeepSeek V4.1 Flash (1017)
同一段提示詞，兩個模型，經閘道實測
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
DeepSeek V4.1 Flash (1017) 通過 · 3 sentences
Enabling reasoning caused each request to generate additional internal reasoning tokens, which are billed like output tokens and can be several times longer than the final answer. Those extra tokens raise cost per call, and longer responses plus occasional retries pushed total usage to roughly double. We can control this with reasoning-effort limits, token budgets, caching, and spend monitoring by use case.
輸出 253 tok (+178 思考) 延遲 5.4 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 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
DeepSeek V4.1 Flash (1017) 通過 · 8/8 cases
```python def longest_run(xs): if not xs: return 0 best = 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 ``` The bug is that `cur` starts at `0` and is incremented only when two adjacent items match, so it counts equal adjacent pairs rather than run length; a run of length `k` is recorded as `k - 1`. Consequently, a single-element input returns `0`, and a run ending at the last index—especially a one-element final run—is undercounted or never counted. The fix initializes every run to length `1`, increments on a match, resets to `1` on a mismatch, updates `best` each iteration, and handles the empty input separately.
輸出 1300 tok (+1095 思考) 延遲 11.8 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 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
DeepSeek V4.1 Flash (1017) 通過 · 5/5 fields, guidance "withheld pending the Q4 close"
{"company":"Meridian Robotics","quarter":"September quarter","revenue_usd_m":412,"growth_pct":11,"guidance":"withheld pending the Q4 close"}
輸出 1375 tok (+1337 思考) 延遲 12.2 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 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
DeepSeek V4.1 Flash (1017) 通過 · 120 words, 0 banned, 1 question
Today we’re introducing Cross-Provider Prompt Cache in the API gateway. It stores identical prompt requests once and serves cached responses across supported model providers, reducing duplicate calls, latency, and spend. Teams can set cache keys by model, temperature, system prompt, and custom metadata, then choose TTLs per route or workspace. The gateway handles provider fallback while keeping cached results consistent with your policy. Is your team paying twice for the same prompt? Cross-Provider Prompt Cache helps you answer that. Configure it in the gateway dashboard, inspect hit rates by provider, and invalidate entries on demand. Existing gateway routes need no code changes; enable the feature and define your caching rules. Cross-Provider Prompt Cache is available for all API gateway customers.
輸出 2487 tok (+2335 思考) 延遲 17.1 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-pro",
# model="deepseek-v4.1-flash-1017", # 取消這一行的註解，並把上一行註解掉
messages=[{"role": "user", "content": "Summarize this diff"}],
reasoning_effort="medium",
)
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-pro",
// model: "deepseek-v4.1-flash-1017", // 取消這一行的註解，並把上一行註解掉
messages: [{ role: "user", content: "Summarize this diff" }],
reasoning_effort: "medium",
});
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-pro",
# "model": "deepseek-v4.1-flash-1017", # 取消這一行的註解，並把上一行註解掉
"messages": [{"role": "user", "content": "Hello"}],
"reasoning_effort": "medium"
}'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-pro",
// Model: "deepseek-v4.1-flash-1017", // 取消這一行的註解，並把上一行註解掉
Messages: []openai.ChatCompletionMessageParamUnion{
openai.UserMessage("Summarize this diff"),
},
ReasoningEffort: openai.ReasoningEffortMedium,
})
fmt.Println(resp.Choices[0].Message.Content)
}import com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;
import com.openai.models.chat.completions.*;
import com.openai.models.ReasoningEffort;
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-pro")
// .model("deepseek-v4.1-flash-1017") // 取消這一行的註解，並把上一行註解掉
.addUserMessage("Summarize this diff")
.reasoningEffort(ReasoningEffort.MEDIUM)
.build());
System.out.println(resp.choices().get(0).message().content().orElse(""));常見問題
Dola Seed 2.0 Pro 和 DeepSeek V4.1 Flash (1017) 哪個比較便宜？
以「輸入 / 1M tokens」來看，DeepSeek V4.1 Flash (1017) 比較便宜（$0.3 對 $0.5，相差 1.7×）。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 Pro 和 DeepSeek V4.1 Flash (1017) 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 Pro 與 DeepSeek V4.1 Flash (1017) 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。