DeepSeek V4 Flash (0731) vs GLM-5.2
什麼情況選哪一個
兩者都有 1,000,000 token 上下文和相同的文字進、文字出輪廓，都支援聊天、程式碼、推理和工具，所以分野是價格和輸出長度：deepseek-v4-flash-0731 輸入 $0.44、輸出 $1.32，對 glm-5.2 的 $1.4 和 $4.4，分別便宜約 3.2 倍和 3.3 倍，快取讀取 $0.044 對 $0.26。大批量工作或單次最長 393216 token 的回覆選 deepseek-v4-flash-0731，約為對方 131072 上限的 3 倍。想要長上下文標誌或按請求關閉思考時選 glm-5.2。
Benchmark 成績
兩邊都有成績的有 19 項。
供應商公布： Alibaba (Qwen) Anthropic ByteDance DeepSeek Google MiniMax Moonshot OpenAI Tencent Z.ai
定價
| DeepSeek V4 Flash (0731) | GLM-5.2 | Δ | |
|---|---|---|---|
| 輸入 / 1M tokens | $0.44 | $1.4 | 0.31× |
| 輸出 / 1M tokens | $1.32 | $4.4 | 0.3× |
| 快取讀取 / 1M tokens | $0.044 | $0.26 | 0.17× |
| 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 Flash (0731) | GLM-5.2 | |
|---|---|---|
| 工具使用 | 是 | 是 |
| 思考控制 | 是，但供應商未公布調整參數 | 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 Flash (0731) | GLM-5.2 | |
|---|---|---|
| 輸入模態 | 文字 | 文字 |
| 輸出模態 | 文字 | 文字 |
| 發布日期 | 2026-07-31 | 2026-06-16 |
| 上下文視窗 | 1M | 1M |
| 最大輸出 | 393K | 131K |
| 思考參數 | - |
|
| 可接受的值 | - | thinking.type
reasoning_effort
|
| 預設值 | - | 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；供應商沒有公布的項目就直接略過，不自行推測。 完整來源： DeepSeek V4 Flash (0731) · GLM-5.2
同一段提示詞，兩個模型，經閘道實測
DeepSeek V4 Flash (0731) 通過 · 3 sentences
Enabling reasoning mode prompts the model to generate extensive internal chain-of-thought tokens before answering, often multiplying total token output per query by several times. This directly inflates costs because we are billed per token, and these reasoning tokens are processed at a premium compute tier that carries a higher price per million tokens. However, this spend buys measurably higher accuracy and fewer failed calls, so we are tracking it against specific error-reduction KPIs to ensure the ROI justifies the increase.
輸出 365 tok (+266 思考) 延遲 5.1 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
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 Flash (0731) 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 bug stems from initializing `cur` to 0 and only incrementing it when adjacent elements are equal, which counts the number of *equal transitions* rather than the actual run length—so every run of length L is reported as L−1, and a single‑element run (with no transitions) yields 0. Moreover, because `best` is updated only inside the equality branch, it never accounts for a run that ends at the last element if that run is longer than any previously seen (though the real issue remains the off‑by‑one). […]
輸出 1268 tok (+1016 思考) 延遲 14.0 s
GLM-5.2 未通過 · 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 Flash (0731) 通過 · 5/5 fields, guidance null
{ "company": "Meridian Robotics", "quarter": "Q3", "revenue_usd_m": 412, "growth_pct": null, "guidance": null }
輸出 1119 tok (+1072 思考) 延遲 12.3 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
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 Flash (0731) 未通過 · 141 words, 0 banned, 1 question
**Introducing Prompt Cache for API Gateway** We are pleased to announce Prompt Cache, a new feature that stores and reuses identical prompt prefixes across all connected LLM providers. By automatically recognizing repeated requests, this cache reduces token consumption and lowers latency without altering your application logic. Whether you use OpenAI, Anthropic, or open-source models, the cache operates transparently, ensuring consistent behavior while cutting costs. Developers can configure cache TTL and scoping rules via a simple API call, giving you precise control over data freshness and privacy. This feature also minimizes redundant upstream calls, freeing provider rate limits for more critical workloads. Start caching today through the dashboard or CLI, and watch your operational expenses drop significantly. […]
輸出 254 tok (+80 思考) 延遲 4.4 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
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-flash-0731",
# model="glm-5.2", # 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-flash-0731",
// model: "glm-5.2", // 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-flash-0731",
# "model": "glm-5.2", # 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-flash-0731",
// Model: "glm-5.2", // 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-flash-0731")
// .model("glm-5.2") // 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 Flash (0731) 和 GLM-5.2 哪個比較便宜？
以「輸入 / 1M tokens」來看，DeepSeek V4 Flash (0731) 比較便宜（$0.44 對 $1.4，相差 3.2×）。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 Flash (0731) 和 GLM-5.2 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 Flash (0731) 與 GLM-5.2 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。