Dola Seed 2.0 Pro vs DeepSeek V4 Pro (0813)
何時該用哪一個 — 綜合評斷，而非基準測試數據表
兩者皆涵蓋聊天、程式碼、推理與工具，因此差異在於上下文對比價格與輸入：Dola-Seed-2.0-pro 支援文字、圖片與影片，輸入 $0.5 且輸出 $3，具備 256000-token 的視窗，並允許您停用思考（thinking）。deepseek-v4-pro-0813 僅支援文字，在輸入與輸出上分別貴了 2.64x 與 1.32x（$1.32 與 $3.96），但換來了 1000000-token 的視窗與 393216 最大輸出 tokens——正好是 Dola 的 131072 的 3x。若要以較低成本進行具備可切換推理功能的多模態工作，請選擇 ByteDance 模型；當整個儲存庫（whole-repo）或長文件提示與非常長的回覆相當重要時，請選擇 DeepSeek 模型。
定價
| Dola Seed 2.0 Pro | DeepSeek V4 Pro (0813) | Δ | |
|---|---|---|---|
| 輸入 / 1M tokens | $0.5 | $1.32 | 0.38× |
| 輸出 / 1M tokens | $3 | $3.96 | 0.76× |
| 快取讀取 / 1M tokens | $0.1 | $0.132 | 0.76× |
| 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）
能力
| Dola Seed 2.0 Pro | DeepSeek V4 Pro (0813) | |
|---|---|---|
| 工具使用 | 是 | 是 |
| 思考控制 | 可配置 | 常駐開啟 |
| 結構化輸出 | — | 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 Pro | DeepSeek V4 Pro (0813) | |
|---|---|---|
| 輸入模態 | 文字 影像 影片 | 文字 |
| 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 | 393K |
| 思考參數 |
| reasoning_effort |
| 可接受的值 | thinking.type
reasoning_effort
| 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 Pro · DeepSeek V4 Pro (0813)
單一提示詞，兩款模型 — 經由閘道測量
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 Pro (0813) 通過 · 3 sentences
Enabling reasoning causes the model to generate a hidden chain-of-thought with many additional tokens before producing the final answer, which sharply increases compute consumption per request. Those extra reasoning tokens are billed at the same or higher rates, so total usage doubled even though the volume of final outputs stayed similar. We can control this by adjusting reasoning effort, setting token limits, or routing only complex requests through reasoning mode.
輸出 308 tok (+226 思考) 延遲 5.1 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 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 Pro (0813) 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 else: cur = 1 best = max(best, cur) return best ``` The bug is that `cur` was initialized to `0` and only counted adjacent matching pairs, so a run of length `n` produced `n - 1` (or `0` for a single element) instead of `n`. The code also reset to `0` on a change, when the new run should start at length `1`. Initializing `cur` and `best` to `1` fixes both the single-element case and runs ending at the last index.
輸出 1315 tok (+1130 思考) 延遲 16.9 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 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 Pro (0813) 通過 · 5/5 fields, guidance null
{"company":"Meridian Robotics","quarter":"September quarter","revenue_usd_m":412,"growth_pct":11.0,"guidance":null}
輸出 2155 tok (+2121 思考) 延遲 26.0 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 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 Pro (0813) 通過 · 121 words, 0 banned, 1 question
Introducing cross-provider prompt caching in our API gateway. Send a prompt once, and the gateway stores its computed prefix across Anthropic, OpenAI, and other LLM providers. Subsequent requests with the same prompt hit the cache, cutting latency and token costs while keeping outputs consistent across routing decisions and provider failovers. Teams can route identical prompts between providers without reprocessing shared context or lengthy system instructions. How much could you save on repeated prompt prefixes? The cache respects provider-specific key formats, handles TTLs automatically, and works with streaming and batch requests. Enable it with one configuration flag—no changes to your application code. Available today on all plans. Monitor cache hit rates, token savings, and provider-specific performance metrics in the live dashboard.
輸出 2845 tok (+2694 思考) 延遲 25.5 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-pro",
# model="deepseek-v4-pro-0813", # 取消註解此行，並註解上一行
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-pro-0813", // 取消註解此行，並註解上一行
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-pro-0813", # 取消註解此行，並註解上一行
"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-pro-0813", // 取消註解此行，並註解上一行
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-pro-0813") // 取消註解此行，並註解上一行
.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 Pro (0813) 哪個比較便宜？
Dola Seed 2.0 Pro 在 輸入 / 1m tokens 上較便宜（$0.5 對比 $1.32，相差 2.6×）。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 Pro 和 DeepSeek V4 Pro (0813) 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 Pro 與 DeepSeek V4 Pro (0813) 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。