🎁 新用戶 免費註冊,送 10 次呼叫,最高 $1,免綁卡。

GLM-5.2 vs Qwen3.8 Max

vs

何時該用哪一個 — 綜合評斷,而非基準測試數據表

兩者都是文字輸出的長脈絡推理模型,最大輸出同為 131072 token,所以真正的差別是模態和價格:glm-5.2 只收文字,輸入 $0.77、輸出 $2.42;qwen3.8-max 還接受影像,輸入 $2、輸出 $6,分別貴約 2.6 倍和 2.5 倍,快取讀取 $0.25 對 $0.143。管線裡要餵截圖、圖表或掃描頁時選 qwen3.8-max;純文字的對話、程式碼和工具工作選 glm-5.2——按 token 更便宜,視窗略大(1000000 token),思考還能關閉。

定價

GLM-5.2 Qwen3.8 Max Δ
輸入 / 1M tokens $0.77 $2 0.39×
輸出 / 1M tokens $2.42 $6 0.4×
快取讀取 / 1M tokens $0.143 $0.25 0.57×
快取寫入 1.25x

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)

GLM-5.2 · $0.77 Qwen3.8 Max · $2
$0.05 · Qwen3 VL Flash $30 · GPT-5.4 Pro

能力

GLM-5.2 Qwen3.8 Max
工具使用
思考控制 可配置 是 —— 廠商未公布調節參數
結構化輸出
提示快取 隱式(自動) 隱式 + 顯式
快取生命週期 未公開 explicit: 5m, reset on hit
最小快取前綴 未公開 1024 個 token

規格

GLM-5.2 Qwen3.8 Max
輸入模態 文字 文字 影像
輸出模態 文字 文字
發布日期 2026-06-16 2026-08-03
上下文視窗 1M 984K
最大輸出 131K 131K
思考參數
  • thinking.type
  • reasoning_effort
可接受的值
thinking.type
  • enabled
  • disabled
reasoning_effort
  • none
  • minimal
  • low
  • medium
  • high
  • xhigh
  • max (none and minimal skip thinking, low and medium map to high, xhigh maps to max)
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;供應商未發布的資料列會直接省略,而非自行推測。 完整來源: GLM-5.2 · Qwen3.8 Max

單一提示詞,兩款模型 — 經由閘道測量

提示詞 Explain to a CFO, in exactly three sentences, why our LLM bill doubled after we enabled reasoning. 檢查 恰好 3 句

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

Qwen3.8 Max 通過 · 3 sentences

Enabling reasoning makes the model produce additional hidden steps before responding, and those tokens are billable. It also tends to lengthen each interaction because the model works through more possibilities before settling on an answer. Therefore, the bill doubled mainly due to higher compute and token usage per request, not necessarily because the number of requests doubled.

輸出 378 tok (+305 思考) 延遲 8.6 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。

提示詞 This function is supposed to return the longest run of consecutive equal items, but callers report it is off by one on single-element inputs and misses runs that end at the last index. Fix it and explain the bug in one paragraph. 檢查 修復通過測試

GLM-5.2 未命中 · 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

Qwen3.8 Max 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 else: cur = 1 best = max(best, cur) return best ``` The bug is that the original code starts `best` and `cur` at `0`, so it counts adjacent equal *transitions* rather than the number of items in the run. A run of length `n` has only `n - 1` equal-neighbor transitions, so single-element inputs return `0`, and runs that reach the end are undercounted by one. Initializing the current run to `1` for the first element, resetting it to `1` on a break, and updating `best` from that count fixes the off-by-one.

輸出 1616 tok (+1411 思考) 延遲 34.7 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。

提示詞 Extract a JSON object with fields {company, quarter, revenue_usd_m, growth_pct, guidance} from this text. Use null for anything not stated; add no other fields. 檢查 合法 JSON,schema 精確

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

Qwen3.8 Max 通過 · 5/5 fields, guidance null

{ "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": 11, "guidance": null }

輸出 1199 tok (+1141 思考) 延遲 24.4 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。

提示詞 Write a 120-word product announcement for an API gateway feature that caches prompts across providers. Forbidden words: "seamless", "unlock", "game-changer", "revolutionize", "empower". Exactly one sentence must be a question. 檢查 120 詞,0 個禁用詞

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

Qwen3.8 Max 通過 · 120 words, 0 banned, 1 question

Today, our API gateway adds prompt caching across major model providers. It stores prompts and responses in one fast cache layer. Teams can lower token spend, reduce latency, and repeat reliable answers. The feature supports OpenAI, Anthropic, Google, and Mistral through one configuration. You can set retention rules, scope access, and invalidate entries quickly. How does your team maintain consistent results during provider outages? Approved cached responses keep applications stable while fallback routes recover. The dashboard shows hit rates, savings, latency, and provider usage. Engineers receive audit trails for every cached prompt, enabling safer testing. Product managers can compare cost trends before and after cache adoption. Start with a small route, then safely expand caching to production traffic right now.

輸出 2744 tok (+2591 思考) 延遲 46.3 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="glm-5.2",
    # model="qwen3.8-max",  # 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)

取得 API 金鑰 →

常見問題

GLM-5.2 和 Qwen3.8 Max 哪個比較便宜?

GLM-5.2 在 輸入 / 1m tokens 上較便宜($0.77 對比 $2,相差 2.6×)。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 GLM-5.2 和 Qwen3.8 Max 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。

GLM-5.2 與 Qwen3.8 Max 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。

相關比較