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Gemini 3.6 Flash vs GLM-5.2

vs

什么时候选哪个

当输入本身不是纯文本时选 gemini-3.6-flash:它接受图像、视频和音频加文本(音频输入按每百万音频 token $5 计费),带 1048576 token 上下文,且 $0.15 的缓存读取低于 glm-5.2 的 $0.26。需要长回复的纯文本工作选 glm-5.2:输入 $1.4 对 $1.5 基本持平,但输出 $4.4 对 $7.5 低约 1.7 倍,最大输出 131072 token 对 65536,而且可关闭思考,gemini-3.6-flash 则始终思考。

Benchmark 成绩

高于同类均值无更高分Gemini 3.6 Flash6 / 181 / 18GLM-5.225 / 801 / 80
Gemini 3.6 Flash GLM-5.2 其他参测模型 同类均值 ★ 没有模型得分更高
DeepSWE 1.1
48.6%
46.2%
BioMysteryBench hard
41.2%
N/A
OSWorld 2.0
33.8%
N/A
Cybergym
N/A
77.2%
Finance Agent v2
N/A
49.7%
Harvey Lab-AA
85.1%
91%
GPQA Diamond
N/A
91.2%
Agents' Last Exam
N/A
23.8%
LVBench
84.2%
N/A

供应商公布: Alibaba (Qwen) Anthropic ByteDance DeepSeek Google MiniMax Moonshot OpenAI Tencent Z.ai

价格

Gemini 3.6 Flash GLM-5.2 Δ
输入 / 1M token $1.5 $1.4 1.1×
输出 / 1M token $7.5 $4.4 1.7×
缓存读取 / 1M token $0.15 $0.26 0.58×

价格取自构建时的实时目录,最新价格见各模型页面。

两个模型所处的位置:全部 76 个按同一单位计费的聊天模型的每 1M token 输入价分布(对数刻度)

Gemini 3.6 Flash · $1.5 GLM-5.2 · $1.4
$0.05 · Qwen3 VL Flash $30 · GPT-5.4 Pro

能力

Gemini 3.6 Flash GLM-5.2
工具调用 是 是
思考控制 始终开启 可配置
结构化输出 是 是
提示词缓存 隐式 + 显式 隐式(自动)
缓存有效期 未公布 未公布
最小缓存前缀 4096 个 token 未公布

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 2026-07-21 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
可选值
  • 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

规格照录自各供应商的文档;供应商没有公布的项目,对应的行直接省略,不做推断。 完整来源: Gemini 3.6 Flash · GLM-5.2

同一条提示词,两个模型,经网关实测

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,数得出来)、受众适配(对 CFO 说话的口吻),以及下方 token 计数暴露出的隐藏思考计费差额。

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 未通过 · 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(能运行)、解释的信息密度,以及在一个范围明确的任务上的 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,严格符合 schema

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

是否严守 schema(不臆造字段)、能否顶住幻觉压力(原文明说暂不给出 guidance),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 未通过 · 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

是否守住约束(字数预算、禁用词表、只能有一个问句)、文风特征,以及长度控制。

改一行代码就能在两个模型之间切换

下方每个标签页里都有两个模型 ID,高亮的那两行是唯一要改的地方。端点不变,API key 不变,请求结构也不变。

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 key →

常见问题

Gemini 3.6 Flash 和 GLM-5.2 哪个更便宜?

按「输入 / 1M token」算,GLM-5.2 更便宜($1.4 对 $1.5,相差 1.1×)。其他计费项的结论可能相反,完整价格见上方表格,实际成本取决于你的用量构成。

不用分别集成两次,就能对 Gemini 3.6 Flash 和 GLM-5.2 做 A/B 测试吗?

可以。两个模型走同一个 OpenAI 兼容端点,用同一个 API key,切换时只要改一行里的模型名,所以可以给两个模型各分一部分流量,直接对比账单。

Gemini 3.6 Flash 和 GLM-5.2 支持提示词缓存吗?

支持。两个模型的缓存读取价都低于各自的输入价,所以前缀能反复命中缓存的负载,实际成本会比按官网价估算的低。具体的缓存读取价见上方价格表。

相关对比

我们的实测研究