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Gemini 3.6 Flash vs GLM-5.2

vs

何时使用哪一个 — 经过整理的结论,而非基准测试表格

输入本身不只是纯文本时选 gemini-3.6-flash:它在文本之外接受图像、视频和音频(音频输入按每百万音频 token $5 计费),带 1048576 token 上下文。纯文本工作选 glm-5.2——两条 token 费率它都更便宜,输入 $0.77 对 $1.5(约 1.9 倍)、输出 $2.42 对 $7.5(约 3.1 倍)——最大输出 131072 token 对 65536,思考也可以关闭,而 gemini-3.6-flash 始终思考。缓存读取基本打平,$0.143 对 $0.15。

定价

Gemini 3.6 Flash GLM-5.2 Δ
输入 / 1M tokens $1.5 $0.77 1.9×
输出 / 1M tokens $7.5 $2.42 3.1×
缓存读取 / 1M tokens $0.15 $0.143

费率取自构建时的实时目录;每个模型页面均附有当前的费率卡。

它们的位置 — 以该计费单位计费的所有 63 个 聊天 模型的 每 1M token 的输入价格(对数刻度)

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 始终开启 可配置
结构化输出
提示词缓存 隐式 + 显式 隐式(自动)
缓存生存时间 未公开 未公开
最小缓存前缀 4096 个 token 未公开

规格

Gemini 3.6 Flash GLM-5.2
输入模态 文本 图像 音频 视频 文本
输出模态 文本 文本
发布日期 2026-06-16
知识截止日期 2026-03
上下文窗口 1M 1M
最大输出 66K 131K
思考参数 thinkingLevel
  • thinking.type
  • reasoning_effort
允许的值
  • 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)
默认值 medium enabled, with reasoning_effort at max: the only GLM with an effort dial, and it defaults to the top of it

规格转录自各供应商的文档;若供应商未发布某项数据,则直接省略该行,而非进行推断。 完整来源: Gemini 3.6 Flash · GLM-5.2

单个 Prompt,两个模型 —— 通过网关实测

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

Gemini 3.6 Flash 通过 · 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.

输出 893 tok (+816 思考) 延迟 4.7 s

GLM-5.2 通过 · 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.

输出 1223 tok (+1138 思考) 延迟 17.1 s

指令遵循(恰好三句,可数)、受众适配(面向 CFO 的语气),以及下方 token 计量所暴露的隐藏思考计费缺口。

提示词 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. 检查 修复通过测试

Gemini 3.6 Flash 通过 · 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`). […]

输出 2034 tok (+1768 思考) 延迟 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 […]

输出 4097 tok (+4036 思考) 延迟 58.4 s

修复是否真的正确(可运行)、解释的信息密度,以及在一个边界明确的任务上的 token 效率。

提示词 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. 检查 合法 JSON,schema 精确

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

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

输出 2843 tok (+2783 思考) 延迟 13.1 s

GLM-5.2 通过 · 5/5 fields, guidance "withheld"

```json { "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": 11, "guidance": "withheld" } ```

输出 1947 tok (+1893 思考) 延迟 30.9 s

schema 服从度(不臆造字段)、幻觉压力(guidance 明确被暂缓给出),以及结构化输出路径的差异。

提示词 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. 检查 120 词,0 个禁用词

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 […]

输出 4092 tok (+3929 思考) 延迟 16.3 s

GLM-5.2 通过 · 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.

输出 11125 tok (+10984 思考) 延迟 114.8 s

约束服从度(字数预算、禁用词表、唯一的那句问句)、文风指纹,以及长度控制。

只需一行代码即可在它们之间切换

两个 ID 都包含在下方的每个选项卡中 — 高亮显示的两行是唯一的修改。相同的端点,相同的密钥,相同的请求结构。

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",  # 取消注释此行,注释上一行
    messages=[{"role": "user", "content": "Summarize this diff"}],
)
print(resp.choices[0].message.content)

获取 API 密钥 →

常见问题

Gemini 3.6 Flash 和 GLM-5.2 哪个更便宜?

在 输入 / 1m tokens 方面,GLM-5.2 更便宜($0.77 对比 $1.5,相差 1.9×)。其他行可能得出相反的结论——上方表格提供了完整信息,实际成本取决于你的组合使用情况。

我可以在不进行两次集成的情况下,对 Gemini 3.6 Flash 和 GLM-5.2 进行 A/B 测试吗?

可以。两者均通过同一个兼容 OpenAI 的端点提供服务,并使用同一把 API 密钥——切换只需更改一行模型字符串,因此你可以将一部分流量路由到各个模型并直接比较账单。

Gemini 3.6 Flash 和 GLM-5.2 支持提示词缓存吗?

是的 —— 两者对缓存读取的计费均低于其输入费率,因此热前缀工作负载的成本低于标价。准确的缓存读取行位于上方的定价表中。

相关对比

来自我们的实测研究