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Gemini 3.6 Flash vs GLM-5.2

vs

何時該用哪一個 — 綜合評斷,而非基準測試數據表

輸入本身不只是純文字時選 gemini-3.6-flash:它在文字之外接受影像、視訊和音訊(音訊輸入按每百萬音訊 token $5 計費),帶 1048576 token 脈絡。純文字工作選 glm-5.2——兩條 token 費率它都更便宜,輸入 $0.77 對 $1.5(約 1.9 倍)、輸出 $2.42 對 $7.5(約 3.1 倍)——最大輸出 131072 token 對 65536,思考也可以關閉,而 gemini-3.6-flash 始終思考。快取讀取基本打平,$0.143 對 $0.15。

定價

Gemini 3.6 Flash GLM-5.2 Δ
輸入 / 1M tokens $1.5 $0.77 1.9×
輸出 / 1M tokens $7.5 $2.42 3.1×
快取讀取 / 1M tokens $0.15 $0.143

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)

Gemini 3.6 Flash · $1.5 GLM-5.2 · $0.77
$0.05 · Qwen3 VL Flash $30 · GPT-5.4 Pro

能力

Gemini 3.6 Flash GLM-5.2
工具使用
思考控制 常駐開啟 可配置
結構化輸出
提示快取 隱式 + 顯式 隱式(自動)
快取生命週期 未公開 未公開
最小快取前綴 4096 個 token 未公開

規格

Gemini 3.6 Flash GLM-5.2
輸入模態 文字 影像 音訊 影片 文字
輸出模態 文字 文字
發布日期 2026-06-16
知識截止日期 2026-03
上下文視窗 1M 1M
最大輸出 66K 131K
思考參數 thinkingLevel
  • thinking.type
  • reasoning_effort
可接受的值
  • minimal
  • low
  • medium
  • high
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 medium 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: Gemini 3.6 Flash · GLM-5.2

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

Gemini 3.6 Flash 通過 · 3 sentences

Enabling reasoning causes the model to generate thousands of internal "thinking" tokens behind the scenes to process logic before delivering the final response. Because our vendor bills us for every token generated—including these intermediate processing steps—our billable output volume per query surged significantly. Since output tokens carry a much higher unit cost than input tokens, this jump in processing volume directly doubled our overall spend.

輸出 893 tok (+816 思考) 延遲 4.7 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

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

Gemini 3.6 Flash 通過 · 8/8 cases

Here is the fixed function along with an explanation of the bugs: ```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 else: cur = 1 best = max(best, cur) return best ``` ### Explanation The original function initialized `cur` and `best` to `0` and incremented `cur` on matching adjacent items, which meant it was counting identical adjacent *pairs* rather than the total number of *elements* in a run. This caused single-element inputs to skip the loop and return `0` instead of `1`, and undercounted all run lengths by 1 (for example, a run of two items at the end of the list resulted in `cur = 1`). […]

輸出 2034 tok (+1768 思考) 延遲 10.0 s

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

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

Gemini 3.6 Flash 通過 · 5/5 fields, guidance "withheld"

```json { "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": null, "guidance": "withheld" } ```

輸出 2843 tok (+2783 思考) 延遲 13.1 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

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

Gemini 3.6 Flash 未命中 · 60 words, 0 banned, 0 questions

72: reducing 73: your 74: monthly 75: token 76: spend. S6 (21): 77: You 78: can 79: easily 80: set 81: custom 82: expiration 83: rules, 84: configure 85: TTL 86: settings, 87: and 88: manage 89: cache 90: invalidation 91: across 92: all 93: vendors 94: from 95: one 96: centralized 97: dashboard. S7 (23): 98: Start 99: optimizing […]

輸出 4092 tok (+3929 思考) 延遲 16.3 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

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="gemini-3.6-flash",
    # model="glm-5.2",  # 取消註解此行,並註解上一行
    messages=[{"role": "user", "content": "Summarize this diff"}],
)
print(resp.choices[0].message.content)

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常見問題

Gemini 3.6 Flash 和 GLM-5.2 哪個比較便宜?

GLM-5.2 在 輸入 / 1m tokens 上較便宜($0.77 對比 $1.5,相差 1.9×)。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 Gemini 3.6 Flash 和 GLM-5.2 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。

Gemini 3.6 Flash 與 GLM-5.2 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。

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