Dola Seed 2.0 Pro vs GLM-5.1
何時該用哪一個 — 綜合評斷，而非基準測試數據表
Dola-Seed-2.0-pro 更便宜也更寬：每百萬輸入 $0.5 對 $1.4（2.8 倍）、輸出 $3 對 $4.4，256000 token 脈絡，文字之外還接受影像和視訊輸入，因此適合多模態工作以及每百萬 $0.1 快取讀取有意義的大批量文字任務。glm-5.1 只處理文字、視窗 200000 token，但帶明確的長脈絡能力標誌，當你希望長文件流程裡宣告該行為時選它。兩者都是當代模型，暴露對話、程式碼、推理和工具，最大輸出 131072 token，思考都可關閉。
定價
| Dola Seed 2.0 Pro | GLM-5.1 | Δ | |
|---|---|---|---|
| 輸入 / 1M tokens | $0.5 | $1.4 | 0.36× |
| 輸出 / 1M tokens | $3 | $4.4 | 0.68× |
| 快取讀取 / 1M tokens | $0.1 | $0.26 | 0.38× |
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 | GLM-5.1 | |
|---|---|---|
| 工具使用 | 是 | 是 |
| 思考控制 | 可配置 | 可配置 |
| 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 | 未公開 |
| 最小快取前綴 | 1024 個 token | 未公開 |
規格
| Dola Seed 2.0 Pro | GLM-5.1 | |
|---|---|---|
| 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 | 2026-04-07 |
| 上下文視窗 | 256K | 200K |
| 最大輸出 | 131K | 131K |
| 思考參數 |
| thinking.type |
| 可接受的值 | thinking.type
reasoning_effort
| thinking.type
|
| 預設值 | enabled, with reasoning_effort medium and thinking summary on | enabled, and the model automatically determines whether to think |
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 · GLM-5.1
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
GLM-5.1 通過 · 3 sentences
Enabling reasoning requires the LLM to generate thousands of invisible "thinking" tokens to work through complex logic before producing the final answer. Because we are billed on total token volume, these intermediate steps are charged at the premium output rate, drastically increasing the variable cost per query. The invoice doubled simply because the model is now doing roughly twice the computational work per request, trading higher variable compute costs for significantly improved analytical accuracy.
輸出 913 tok (+827 思考) 延遲 10.9 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 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
GLM-5.1 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 best = max(best, cur) else: cur = 1 return best ``` The original function tracks the number of adjacent equal pairs rather than the actual run length. By initializing `cur` and `best` to 0 and incrementing `cur` only when a match occurs, a run of length L produces a count of L−1. This causes single‑element inputs to return 0 (since the loop never runs) and generally returns a value one less than the true run length, effectively omitting the final element of any run—including those that end at the last index. […]
輸出 2642 tok (+2414 思考) 延遲 26.1 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
GLM-5.1 通過 · 5/5 fields, guidance null
```json { "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": "eleven and change", "guidance": null } ```
輸出 1465 tok (+1410 思考) 延遲 16.1 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
GLM-5.1 通過 · 120 words, 0 banned, 1 question
We are thrilled to introduce prompt caching across providers in the API gateway. Why pay twice for the same prompt context? Now, when your application sends identical prompt prefixes to different LLM providers, our gateway automatically caches the input, reducing latency and cutting costs. This feature intelligently recognizes repeated prompt structures across OpenAI, Anthropic, and others, storing them efficiently at the network edge. Developers no longer need to manage separate caching logic for each individual provider. Instead, our unified system handles it directly, ensuring faster response times on all subsequent requests. Stop wasting valuable tokens on redundant processing workloads. Upgrade your integration today and experience immediate performance gains while keeping your infrastructure simple and your overall monthly billing incredibly low.
輸出 7589 tok (+7447 思考) 延遲 188.9 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="glm-5.1", # 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: "glm-5.1", // 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": "glm-5.1", # 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: "glm-5.1", // 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("glm-5.1") // 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 GLM-5.1 哪個比較便宜？
Dola Seed 2.0 Pro 在 輸入 / 1m tokens 上較便宜（$0.5 對比 $1.4，相差 2.8×）。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 GLM-5.1 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 GLM-5.1 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。