DeepSeek V4 Pro vs GLM-5.1
什麼情況選哪一個
選擇 deepseek-v4-pro 可最大化規模與預算，因為它具備 1000000 的上下文視窗、393216 的輸出限制，且每百萬 token 的費率較低，輸入為 $1.32，輸出為 $3.96。如果您的工作負載需要其明確的長上下文能力旗標，請考慮 glm-5.1，儘管其實際上下文視窗較小，為 200000，且成本較高，輸入為 $1.4，輸出為 $4.4。這兩款 2026 年 4 月的模型皆可處理文字，支援程式碼與工具使用，並允許您停用思考功能。
Benchmark 成績
GLM-5.1：供應商沒有公布 benchmark 成績。
供應商公布： Alibaba (Qwen) Anthropic DeepSeek Google MiniMax Moonshot OpenAI Tencent Z.ai
定價
| DeepSeek V4 Pro | GLM-5.1 | Δ | |
|---|---|---|---|
| 輸入 / 1M tokens | $1.32 | $1.4 | 0.94× |
| 輸出 / 1M tokens | $3.96 | $4.4 | 0.9× |
| 快取讀取 / 1M tokens | $0.132 | $0.26 | 0.51× |
| 快取寫入 | 不額外計費 | - | - |
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 76 個聊天模型（對數尺度）
功能
| DeepSeek V4 Pro | GLM-5.1 | |
|---|---|---|
| 工具使用 | 是 | 是 |
| 思考控制 | 可設定 | 可設定 |
| 結構化輸出 | 是 | 是 |
| 提示詞快取 | 隱式（自動） | 隱式（自動） |
| 快取存活時間 | no fixed TTL (evicted when unused) | 未公布 |
| 最小快取前綴 | 未公布 | 未公布 |
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；供應商沒有公布的項目就直接略過，不自行推測。 完整來源： DeepSeek V4 Pro · GLM-5.1
同一段提示詞，兩個模型，經閘道實測
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 shifts the LLM from generating quick answers to producing hidden "chain-of-thought" tokens that systematically explore multiple solution paths before responding. Because billing is based on total tokens processed, these invisible reasoning tokens drastically increase input and output volume per query, often adding thousands of compute-intensive tokens to a single prompt. Consequently, our bill doubled not from higher per-token rates or increased user volume, but from the exponentially deeper computational work required to deliver higher-quality, more reliable outputs.
輸出 98 tok 延遲 7.8 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。
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 ``` **Explanation:** The original function initialized `cur` and `best` to `0` and only incremented `cur` when a duplicate was found. This counts the number of *extra* equal items in a run rather than the total run length, so every run is reported as one shorter than it actually is—returning `0` for single-element lists and `L-1` for a run of length `L` (including those ending at the last index). The fix initializes `cur` and `best` to `1` (when the list is non‑empty) and resets `cur` to `1` on a mismatch, so `cur` tracks the full length of the current run and `best` records the true maximum.
輸出 3823 tok (+3585 思考) 延遲 49.7 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。
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 "withheld pending the Q4 close"
```json { "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": "eleven and change", "guidance": "withheld pending the Q4 close" } ```
輸出 1091 tok (+1028 思考) 延遲 15.2 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。
DeepSeek V4 Pro 沒有可評分的回答 · no answer text within 16,384 tokens (all of it went to thinking)
模型沒有回傳任何回答文字，token 額度全都用在隱藏的思考上了。
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 excited to introduce Prompt Cache for our API Gateway. This new feature stores responses for identical prompts, routing subsequent requests directly to the cache instead of calling the underlying AI provider. Are you tired of paying multiple times for the exact same query? Prompt Cache solves this by recognizing duplicate inputs across all supported providers, drastically reducing latency and operational costs. When a user submits a request that matches a previously cached prompt, the gateway returns the stored answer instantly. You can configure cache expiration and scope rules via your dashboard to maintain data freshness. Stop wasting budget on redundant computational work. Upgrade to the latest gateway tier today to start saving time and money on every call.
輸出 3935 tok (+3802 思考) 延遲 41.1 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="deepseek-v4-pro",
# model="glm-5.1", # 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: "deepseek-v4-pro",
// model: "glm-5.1", // 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": "deepseek-v4-pro",
# "model": "glm-5.1", # 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: "deepseek-v4-pro",
// Model: "glm-5.1", // 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("deepseek-v4-pro")
// .model("glm-5.1") // 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
DeepSeek V4 Pro 和 GLM-5.1 哪個比較便宜？
以「輸入 / 1M tokens」來看，DeepSeek V4 Pro 比較便宜（$1.32 對 $1.4，相差 1.1×）。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 DeepSeek V4 Pro 和 GLM-5.1 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。
DeepSeek V4 Pro 與 GLM-5.1 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。