DeepSeek V4 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
兩者都是純文字推理模型，支援對話、程式碼、工具和可選思考，所以差別其實在脈絡和價目表。超大任務和重快取流程選 deepseek-v4-pro：它 1000000 token 的視窗是 GLM-5.1 的 200000 的 5 倍，允許 393216 輸出 token 而非 131072，輸出 $3.216 對 $4.4，快取讀取 $0.0134 比 $0.26 便宜約 19 倍。當提示詞能裝進 200000 token 且新增輸入占主導時選 glm-5.1，它每百萬輸入 $1.4 低於 $1.608。
定價
| DeepSeek V4 Pro | GLM-5.1 | Δ | |
|---|---|---|---|
| 輸入 / 1M tokens | $1.608 | $1.4 | 1.1× |
| 輸出 / 1M tokens | $3.216 | $4.4 | 0.73× |
| 快取讀取 / 1M tokens | $0.0134 | $0.26 | 0.052× |
| 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
| DeepSeek V4 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 | 是 |
| 提示快取 | 隱式（自動） | 隱式（自動） |
| 快取生命週期 | no fixed TTL (evicted when unused) | 未公開 |
| 最小快取前綴 | 未公開 | 未公開 |
規格
| DeepSeek V4 Pro | GLM-5.1 | |
|---|---|---|
| 輸入模態 | 文字 | 文字 |
| 輸出模態 | 文字 | 文字 |
| 發布日期 | 2026-04-24 | 2026-04-07 |
| 上下文視窗 | 1M | 200K |
| 最大輸出 | 393K | 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 high some complex agent requests are automatically set to max | 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： DeepSeek V4 Pro · GLM-5.1
單一提示詞，兩款模型 — 經由閘道測量
DeepSeek V4 Pro 通過 · 3 sentences
Enabling reasoning causes the model to generate a lengthy internal chain‑of‑thought before it delivers a visible answer, and we are billed for every one of those hidden reasoning tokens just as we are for the final output. In most interactions, the volume of these unseen tokens at least equals the visible response, effectively doubling the total tokens consumed per query. That doubled token consumption translates directly into a doubled per‑query cost, which is why our overall LLM bill doubled.
輸出 641 tok (+545 思考) 延遲 11.5 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。
DeepSeek V4 Pro 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 ``` **Explanation:** The bug stems from initializing `cur = 0` and only incrementing it when two adjacent elements match, which counts the number of *additional* equal elements rather than the actual run length (which is `cur + 1`). As a result, the returned `best` is always one less than the true longest run—most obviously returning 0 for a single-element list instead of 1. […]
輸出 2418 tok (+2131 思考) 延遲 35.6 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。
DeepSeek V4 Pro 通過 · 5/5 fields, guidance null
{ "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": "eleven and change", "guidance": null }
輸出 1204 tok (+1153 思考) 延遲 19.9 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。
DeepSeek V4 Pro 沒有可評分的回答 · no answer text within 16,384 tokens (all of it went to thinking)
模型未回傳任何回答文字——所有 token 額度皆耗費於隱藏思考。
輸出 8193 tok (+8192 思考) 延遲 106.2 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="deepseek-v4-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: "deepseek-v4-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": "deepseek-v4-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: "deepseek-v4-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("deepseek-v4-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
DeepSeek V4 Pro 和 GLM-5.1 哪個比較便宜？
GLM-5.1 在 輸入 / 1m tokens 上較便宜（$1.4 對比 $1.608，相差 1.1×）。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 DeepSeek V4 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。
DeepSeek V4 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。