GLM-5.3 是 Z.AI 面向複雜軟體工程與長程智慧體任務的大規模推理模型。
- 輸入
- 文字 $1.4/M
- 輸出
- 文字 $4.4/M
- 快取讀取
- $0.26/M
- 上下文
- 1M
- 相較 GPT-4o
- 便宜約 72%
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) | 131,072 |
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 | No documented switch turns thinking off, unlike the earlier GLM-5 releases, so the output budget must allow for a reasoning trace on every call. A separate clear_thinking flag defaults to false and decides whether prior turns' traces are cleared; the model card tells chat deployments to pass it explicitly. |
| 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 → 文字 |
|---|---|
| 參數量 | 總參數 753B MoE |
- Large-scale reasoning model for complex software engineering and long-horizon agent tasks
- keeps the 1M-token context window of GLM-5.2 and improves on it in coding and in the balance between performance and token efficiency
依據 Z.ai 官方文件 ↗
單一提示詞 — 經由閘道測量
GLM-5.3 通過 · 3 sentences
When we enabled reasoning, the model began generating an extensive internal chain-of-thought—thousands of hidden "thinking" tokens the user never sees—before writing any part of the actual answer. Those invisible tokens are billed at output rates, which run several times the price of input tokens, so the billed volume per query jumped even though query volume and per-token pricing stayed the same. The good news is this is a controllable lever, not a rate increase: capping reasoning effort and routing only complex queries to reasoning mode should bring a large share of the added cost back down.
輸出 681 tok (+562 思考) 延遲 18.4 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 通過 · 8/8 cases
**Fixed:** ```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 bug:** The function suffers from a unit error — it counts adjacent equal *pairs* rather than *items* in a run. `cur` is seeded at 0, gains 1 per match, and resets to 0 on a mismatch, so a run of k equal elements can only ever drive `cur` to k−1 (a run of k items contains exactly k−1 neighboring pairs), and `best`, also starting at 0, inherits that shortfall. That's why a single-element input — a run of length 1 containing zero pairs — returns 0 instead of 1, and why a run reaching the last index comes back one short (e.g. […]
輸出 9934 tok (+9438 思考) 延遲 150.7 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 通過 · 5/5 fields, guidance null
```json { "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": 11, "guidance": null } ``` Notes on interpretation: - **revenue_usd_m**: 412 is the stated topline, though it's on a constant-currency basis. - **growth_pct**: "eleven and change" is extracted as 11 (an approximation, slightly above 11). - **guidance**: null, since formal guidance was explicitly withheld pending the Q4 close.
輸出 2173 tok (+2045 思考) 延遲 35.8 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 通過 · 129 words, 0 banned, 1 question
**Introducing Universal Prompt Caching** We're thrilled to announce prompt caching that works across every major LLM provider. Identical prompts are now cached once at the gateway level, regardless of which model or vendor serves the request downstream. That means up to 90% savings on token costs and dramatically faster responses for repeated queries. How does it work? Our gateway computes a deterministic hash of each incoming prompt, checks the shared cache layer, and returns instant responses when matches exist. New or modified prompts route normally to your configured provider. Deploy with a single configuration flag; no code changes required. Cache invalidation, TTL controls, and detailed analytics are included. Stop paying twice for the same question. Enable Universal Prompt Caching today. --- *Exactly 120 words; one question; no forbidden terms.*
輸出 5418 tok (+5255 思考) 延遲 52.4 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
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",
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",
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",
"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",
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")
.addUserMessage("Summarize this diff")
.reasoningEffort(ReasoningEffort.MEDIUM)
.build());
System.out.println(resp.choices().get(0).message().content().orElse(""));關於 GLM-5.3
- 它保留 GLM-5.2 引入的 1M token 上下文視窗，官方稱其在編碼能力以及效能與 token 效率的平衡上有所改進，因此升級的理由是吞吐與程式碼品質，而不是更大的視窗。
- 結構上它是總參數 753B 的混合專家模型，文字輸入、文字輸出。
- 思考由 reasoning_effort 控制，可取 low、high、max，預設為 max，因此一個什麼都不設的請求就是一個深度思考的請求。
- 相對產品線中更早版本，真正有後果的變化是：沒有公開的開關可以關閉思考——GLM-5、GLM-5.1 和 GLM-5.2 都可以關，而這一條不行，輸出預算必須為每次呼叫的推理軌跡留出空間。
- 另一個 clear_thinking 旗標預設為 false，決定是否清除先前輪次的軌跡，模型卡要求聊天類部署顯式傳入它。
- 工具呼叫、JSON 與結構化輸出、串流回傳和快取輸入都沿用產品線既有能力。
- 在 Synthorai 上它透過 OpenAI 相容的 chat completions 端點提供，因此從 GLM-5.2 遷移只是改一個模型名，而不是改一次整合。
常見問題
GLM-5.3 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 $1.4/M 計算,光是這筆額度就足以對 GLM-5.3 發出約 89 次 ~8K token 的請求。
GLM-5.3 最擅長什麼?
相比 GLM-5.2 改進編碼與 token 效率、1M token 上下文支撐長程智慧體任務、推理力度檔位預設取最大值。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 的價格是多少?
在 Synthorai 上,GLM-5.3 輸入 $1.4/百萬 token、輸出 $4.4/百萬 token,即廠商牌價,無平台加價。快取命中的輸入 token 以 $0.26/M 計費。
GLM-5.3 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 $0.26/M 計費(未命中 $1.4/M)。 提示詞快取指南 →
如何開通 GLM-5.3?
把現有 OpenAI SDK 的 base_url 指向 "https://synthorai.io/v1",model 設為 "glm-5.3" 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。