GLM-5.3-Flash 是 GLM-5.3 產品線中原生多模態的一員，面向高效編碼與長程智慧體任務。
- 輸入
- 文字 影像 影片 $0.15/M
- 輸出
- 文字 $0.5/M
- 快取讀取
- $0.03/M
- 上下文
- 1M
- 相較 GPT-4o
- 便宜約 97%
Benchmark 成績
廠商公布: Alibaba (Qwen) Anthropic DeepSeek Google Moonshot OpenAI Tencent Z.ai
價格在同類中的位置
價格在 65 個同類模型中的位置
這條線顯示該模型的價格，在 Synthorai 上同類模型裡處於什麼位置。兩端標出了最便宜和最貴的那個。這裡是基礎價，批次、區域與快取寫入的折扣見價格頁。
規格與限制
Token
| 上下文視窗(廠商規格) | 1,000,000 |
|---|---|
| 最大輸出(廠商規格) | 163,840 |
提示詞快取
| 快取方式 | 自動 |
|---|
思考
| 廠商參數 | reasoning_effort |
|---|---|
| 可選值 | low · high · max |
| 預設值 | max 請求未指定時生效 |
| 可關閉 | 不支援 |
| 思考行為 | Same dial as GLM-5.3 with no documented way to switch thinking off; clear_thinking defaults to false. |
| 參數 | reasoning_effort |
| 取值 | minimal · low · medium · high 閘道側參數面——以上方廠商映射為準 |
模型
| 模態 | 文字 + 影像 + 影片 → 文字 |
|---|---|
| 參數量 | 總參數 320B · 啟用 18B MoE (hybrid sparse + linear attention, mHC) |
- Natively multimodal member of the GLM-5.3 line, taking image and video alongside text
- the hybrid sparse-plus-linear attention architecture is credited with holding accurate long-context behaviour while cutting compute overhead
依據 Z.ai 官方文件 ↗
單一提示詞 — 經由閘道測量
GLM-5.3-Flash 通過 · 3 sentences
Enabling reasoning means the model now generates thousands of invisible "thinking" tokens before each visible answer, and we're billed for those hidden tokens at full output rates—our most expensive tier. Since output tokens cost 3–5x more than input tokens, that hidden thinking volume compounds across every request, which is why the bill doubled rather than edged up. If the spend concerns you, the fix is straightforward: route only complex queries to reasoning models and keep simple tasks on standard ones, which can reclaim a meaningful share of that cost without losing accuracy where it matters.
輸出 742 tok (+626 思考) 延遲 23.2 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。
GLM-5.3-Flash 通過 · 8/8 cases
```python def longest_run(xs): if not xs: return 0 best = 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 bug:** `cur` is supposed to hold the length of the current run, but it's seeded with `0` and reset to `0` on a mismatch instead of `1` — and since the loop starts at index 1, the first element of every run never passes through the `==` branch and so never gets counted at all. That means `cur` actually holds *run length minus 1* for every run, so every run is underreported by one: a single-element input never enters the loop and returns 0 instead of 1, and a run of length n returns n − 1 (e.g., `[3, 3, 3]` returns 2). […]
輸出 2462 tok (+2138 思考) 延遲 29.1 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。
GLM-5.3-Flash 通過 · 5/5 fields, guidance "Withheld pending the Q4 close"
```json { "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": 11, "guidance": "Withheld pending the Q4 close" } ``` Notes on judgment calls: revenue of 412 is on a constant-currency basis as stated; growth of "eleven and change" is approximated as 11 since no precise figure is given.
輸出 717 tok (+616 思考) 延遲 9.1 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。
GLM-5.3-Flash 通過 · 120 words, 0 banned, 1 question
Introducing Cross-Provider Prompt Cache, the newest feature in our API gateway. Today, teams send identical prompts to multiple LLM providers and pay full price each time. Why duplicate that work and cost? With Cross-Provider Prompt Cache, your gateway stores prompt-response pairs and serves repeated requests from cache, regardless of which provider handles the call. The result: lower latency, reduced spend, and consistent outputs across OpenAI, Anthropic, Google, and self-hosted models. Configure cache policies per route, set TTLs, and invalidate entries instantly through the dashboard or API. Built-in analytics show hit rates and savings in real time. Enable the cache with a single flag, no code changes required. Available today on all paid plans. Contact sales for enterprise volume pricing details.
輸出 2095 tok (+1937 思考) 延遲 20.2 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。
30 秒用上 GLM-5.3-Flash
OpenAI 相容:換掉 base_url,SDK 不用改。POST /v1/chat/completions
from openai import OpenAI
client = OpenAI(
base_url="https://synthorai.io/v1",
api_key="sk-syn-...",
)
resp = client.chat.completions.create(
model="glm-5.3-flash",
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: "glm-5.3-flash",
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": "glm-5.3-flash",
"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: "glm-5.3-flash",
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("glm-5.3-flash")
.addUserMessage("Summarize this diff")
.reasoningEffort(ReasoningEffort.MEDIUM)
.build());
System.out.println(resp.choices().get(0).message().content().orElse(""));關於 GLM-5.3-Flash
- 它在文字之外還接受影像與影片輸入並回傳文字，其混合架構——稀疏注意力結合線性注意力，並引入流形約束超連接（mHC）——被描述為在削減算力開銷的同時保持準確的長上下文行為。
- 效率體現在模型形態上：總參數 320B、啟用僅 18B，而 GLM-5.3 為 753B。
- 這正是它成為低價檔的原因；同時值得說清楚它沒有被犧牲掉什麼——它同樣以 1M token 上下文視窗提供服務，而非更小的視窗。
- 思考與 GLM-5.3 一致：reasoning_effort 取 low、high 或 max，預設 max，且沒有公開的關閉方式，因此輸出預算要為每次呼叫的推理軌跡留出空間。
- 工具呼叫、JSON 與結構化輸出、串流回傳和快取輸入均受支援，權重公開發布。
- 在 Synthorai 上它透過 OpenAI 相容的 chat completions 端點提供。
常見問題
GLM-5.3-Flash API 可以免費試用嗎?
可以,新帳號可獲得 10 次試用呼叫和最高 $1 的免費額度,無需信用卡。以輸入 $0.15/M 計算,光是這筆額度就足以對 GLM-5.3-Flash 發出約 833 次 ~8K token 的請求。
GLM-5.3-Flash 最擅長什麼?
原生多模態，支援影像與影片輸入、總參數 320B，啟用僅 18B、低價檔同樣提供 1M token 視窗。完整能力請見「關於」一節,內容取自廠商官方發布說明。
GLM-5.3-Flash 的價格是多少?
在 Synthorai 上,GLM-5.3-Flash 輸入 $0.15/百萬 token、輸出 $0.5/百萬 token,即廠商牌價,無平台加價。快取命中的輸入 token 以 $0.03/M 計費。
GLM-5.3-Flash 支援提示詞快取(prompt caching)嗎?
支援,且全自動:由 Z.ai 供應的提示詞會自動快取,無需改程式碼。快取命中的輸入 token 以 $0.03/M 計費(未命中 $0.15/M)。 提示詞快取指南 →
如何開通 GLM-5.3-Flash?
把現有 OpenAI SDK 的 base_url 指向 "https://synthorai.io/v1",model 設為 "glm-5.3-flash" 即可。一組 API key 通用閘道上的所有模型。
相關模型
對比
本頁每個值都轉錄自廠商自己的文件(連結見上),並帶有核對日期。價格在全目錄範圍內比較;各廠商定義不同的規格值,只說明差異而不作圖表對比。此處沒有任何由我們測量的資料,也不做評分。