Dola Seed 2.0 Lite vs GLM-5.2

Q: Dola Seed 2.0 Lite 和 GLM-5.2 哪個比較便宜？

Dola Seed 2.0 Lite 在 輸入 / 1m tokens 上較便宜（$0.25 對比 $0.77，相差 3.1×）。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。

更換任一側 vs

何時該用哪一個 — 綜合評斷，而非基準測試數據表

Dola-Seed-2.0-lite 更便宜、輸入面更寬：它接受文字、影像、視訊和音訊，$0.25 輸入、$2 輸出，比 glm-5.2 的 $0.77 和 $2.42 在輸入上便宜 3.1 倍、輸出上便宜 1.2 倍，快取讀取 $0.05 對 $0.143。當任務只是文字但很長時選 glm-5.2，它的 1000000 token 脈絡約為 Dola-Seed-2.0-lite 的 256000 的 3.9 倍，並帶推理和長脈絡標誌。兩者最大輸出都是 131072 token、都可關閉思考，所以真正的決定是多模態輸入還是脈絡長度。

定價

	Dola Seed 2.0 Lite	GLM-5.2	Δ
輸入 / 1M tokens	$0.25	$0.77	0.32×
輸出 / 1M tokens	$2	$2.42	0.83×
快取讀取 / 1M tokens	$0.05	$0.143	0.35×

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）

Dola Seed 2.0 Lite · $0.25 GLM-5.2 · $0.77

$0.05 · Qwen3 VL Flash $30 · GPT-5.4 Pro

能力

	Dola Seed 2.0 Lite	GLM-5.2
工具使用	是	是
思考控制	可配置	可配置
結構化輸出	是	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 Lite	GLM-5.2
輸入模態	文字影像音訊影片	文字
輸出模態	文字	文字
發布日期	2026-02-14	2026-06-16
上下文視窗	256K	1M
最大輸出	131K	131K
思考參數	thinking.type reasoning_effort	thinking.type reasoning_effort
可接受的值	thinking.type enabled disabled (no auto) reasoning_effort minimal low medium high	thinking.type enabled disabled reasoning_effort none minimal low medium high xhigh max (none and minimal skip thinking, low and medium map to high, xhigh maps to max)
預設值	enabled, with reasoning_effort medium	enabled, with reasoning_effort at max: the only GLM with an effort dial, and it defaults to the top of it

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 Lite · GLM-5.2

單一提示詞，兩款模型 — 經由閘道測量

針對 CFO 的推論修復包含錯誤的函式擷取至 schema 依照限制條件撰寫

提示詞 Explain to a CFO, in exactly three sentences, why our LLM bill doubled after we enabled reasoning. 檢查恰好 3 句

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

GLM-5.2 通過 · 3 sentences

Enabling reasoning means the LLM now generates thousands of invisible "thinking" tokens to systematically work through complex problems before producing a final answer. Because our cloud providers bill for these internal processing steps at the same rate as standard output, our billable token volume per query has doubled. While this increases our direct API costs, it drastically reduces error rates and manual review labor, ultimately lowering our total cost per resolved transaction.

輸出 1223 tok (+1138 思考) 延遲 17.1 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。

提示詞 This function is supposed to return the longest run of consecutive equal items, but callers report it is off by one on single-element inputs and misses runs that end at the last index. Fix it and explain the bug in one paragraph. 檢查修復通過測試

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

GLM-5.2 未命中 · 1/8 cases (fails [1])

```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 […]

輸出 4097 tok (+4036 思考) 延遲 58.4 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。

提示詞 Extract a JSON object with fields {company, quarter, revenue_usd_m, growth_pct, guidance} from this text. Use null for anything not stated; add no other fields. 檢查合法 JSON，schema 精確

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

GLM-5.2 通過 · 5/5 fields, guidance "withheld"

```json { "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": 11, "guidance": "withheld" } ```

輸出 1947 tok (+1893 思考) 延遲 30.9 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。

提示詞 Write a 120-word product announcement for an API gateway feature that caches prompts across providers. Forbidden words: "seamless", "unlock", "game-changer", "revolutionize", "empower". Exactly one sentence must be a question. 檢查 120 詞，0 個禁用詞

Dola Seed 2.0 Lite 未命中 · 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

GLM-5.2 通過 · 120 words, 0 banned, 1 question

We are introducing Caching for our API Gateway, the smartest way to optimize your workflows. Why pay for the exact same response twice? Now, you can automatically store and reuse prompt results across multiple AI providers, drastically reducing latency and overall operational costs. If a user submits a duplicate query, the gateway serves the cached answer instantly, regardless of whether you route to OpenAI, Anthropic, or others. This directly translates to faster applications and significantly lower monthly API bills. You can easily configure your specific caching rules within the developer dashboard and watch your efficiency soar. Stop wasting your valuable tokens on completely redundant computations. Upgrade to the latest gateway version today and experience the future of intelligent prompt management.

輸出 11125 tok (+10984 思考) 延遲 114.8 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-lite",
    # model="glm-5.2",  # 取消註解此行，並註解上一行
    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: "glm-5.2",  // 取消註解此行，並註解上一行
  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": "glm-5.2",  # 取消註解此行，並註解上一行
    "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: "glm-5.2",  // 取消註解此行，並註解上一行
		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("glm-5.2")  // 取消註解此行，並註解上一行
        .addUserMessage("Summarize this diff")
        .build());
System.out.println(resp.choices().get(0).message().content().orElse(""));

取得 API 金鑰 →

常見問題

Dola Seed 2.0 Lite 和 GLM-5.2 哪個比較便宜？

Dola Seed 2.0 Lite 在輸入 / 1m tokens 上較便宜（$0.25 對比 $0.77，相差 3.1×）。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 Lite 和 GLM-5.2 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 Lite 與 GLM-5.2 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。

定價

能力

規格

單一提示詞，兩款模型 — 經由閘道測量

只需一行程式碼即可在兩者間切換

常見問題

相關比較