Dola Seed 2.0 Pro vs GPT-6 Sol
什麼情況選哪一個
在費率表的每個項目上，Dola-Seed-2.0-pro 都是兩者中較便宜的，每百萬 token 輸入為 $0.5、輸出為 $3，而 gpt-6-sol 則為 $2 與 $10，這使其在輸入上便宜 4 倍，輸出上大約便宜 3.3 倍，且它是兩者中唯一能在文字與圖片之外接受影片輸入的模型。當你需要 1050000 token 脈絡來處理長文件或大型程式碼庫時，請選擇 gpt-6-sol，這大約是 Dola-Seed-2.0-pro 所允許之 256000 token 的四倍。兩者皆可處理工具、程式碼與推理，皆可讓你關閉思考功能，且它們的最大輸出上限很接近，分別為 131072 與 128000 token。
Benchmark 成績
Dola Seed 2.0 Pro：供應商沒有公布 benchmark 成績。
定價
| Dola Seed 2.0 Pro | GPT-6 Sol | Δ | |
|---|---|---|---|
| 輸入 / 1M tokens | $0.5 | $2 | 0.25× |
| 輸出 / 1M tokens | $3 | $10 | 0.3× |
| 快取讀取 / 1M tokens | $0.1 | $0.2 | 0.5× |
| 快取寫入 | - | 1.25x (30m) / 2.5x (12h) | - |
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 83 個聊天模型（對數尺度）
功能
| Dola Seed 2.0 Pro | GPT-6 Sol | |
|---|---|---|
| 工具使用 | 是 | 是 |
| 思考控制 | 可設定 | 可設定 |
| 結構化輸出 | - | 是 |
| 提示詞快取 | 隱式 + 顯式 | 隱式 + 顯式 |
| 快取存活時間 | 未公布 | at least 30m by default, 12h option on the Responses API |
| 最小快取前綴 | 1,024 個 token | 1,024 個 token |
規格
| Dola Seed 2.0 Pro | GPT-6 Sol | |
|---|---|---|
| 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 | 2026-09-22 |
| 知識截止日期 | - | 2026-04 |
| 上下文視窗 | 256K | 1.1M |
| 最大輸出 | 131K | 128K |
| 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 | 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 Pro · GPT-6 Sol
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
GPT-6 Sol 通過 · 3 sentences
Enabling reasoning made the model generate extra internal tokens to work through each request, and those tokens are billed even though users don’t see them. If request volume stayed roughly the same, more billed tokens per request could explain why the bill doubled. We should check the usage breakdown to confirm, then lower reasoning effort or limit it to tasks that need it.
輸出 188 tok (+111 思考) 延遲 5.0 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
GPT-6 Sol 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 original `cur` counted matching *adjacent pairs*, not items, so every run was one too short—including a single-element input, which returned 0. Starting each run at 1 counts its first item, and updating `best` on every iteration ensures a run that reaches the last index is included.
輸出 206 tok (+59 思考) 延遲 5.9 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
GPT-6 Sol 通過 · 5/5 fields, guidance "Withheld pending the Q4 close"
{"company":"Meridian Robotics","quarter":"September quarter","revenue_usd_m":null,"growth_pct":null,"guidance":"Withheld pending the Q4 close"}
輸出 227 tok (+185 思考) 延遲 5.2 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
GPT-6 Sol 通過 · 120 words, 0 banned, 1 question
Today, we’re introducing cross-provider prompt caching for our API gateway, helping teams reuse repeated prompt content when routing requests among supported AI providers. The gateway identifies eligible prompt prefixes, stores cache references, and applies them to subsequent requests where provider capabilities allow. Why send the same context again if it can be reused? With fewer duplicate input tokens, applications can reduce costs and improve latency without changing how developers call the gateway. Configure caching by route, monitor hit rates and savings in your dashboard, and keep existing provider fallback rules in place. Availability and cache behavior vary by provider, so the gateway reports eligibility and usage for each request. Get started today with our documentation and test your existing workloads.
輸出 586 tok (+443 思考) 延遲 7.7 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="gpt-6-sol", # 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: "gpt-6-sol", // 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": "gpt-6-sol", # 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: "gpt-6-sol", // 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("gpt-6-sol") // 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 GPT-6 Sol 哪個比較便宜？
以「輸入 / 1M tokens」來看，Dola Seed 2.0 Pro 比較便宜（$0.5 對 $2，相差 4.0×）。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 Pro 和 GPT-6 Sol 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 GPT-6 Sol 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。