Gemini 3.7 Flash vs GLM-5
什么时候选哪个
gemini-3.7-flash 面更宽：它接受图像、音频和视频加文本，上下文 1048576 token 对 200000，输入 $0.75 对 glm-5 的 $1，低约 1.3 倍，缓存读取 $0.075 对 $0.2，低约 2.7 倍。glm-5 在输出上扳回：$3.2 对 $3.75，低约 1.2 倍，最大输出 131072 token 是 gemini-3.7-flash 的 65536 的两倍，而且推理可以关闭，Gemini 模型不允许。多模态或长提示工作选 gemini-3.7-flash；需要长单次回复的纯文本任务选 glm-5。
Benchmark 成绩
供应商公布： Alibaba (Qwen) Anthropic ByteDance DeepSeek Google MiniMax Moonshot OpenAI Tencent Z.ai
价格
| Gemini 3.7 Flash | GLM-5 | Δ | |
|---|---|---|---|
| 输入 / 1M token | $0.75 | $1 | 0.75× |
| 输出 / 1M token | $3.75 | $3.2 | 1.2× |
| 缓存读取 / 1M token | $0.075 | $0.2 | 0.37× |
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 78 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）
能力
| Gemini 3.7 Flash | GLM-5 | |
|---|---|---|
| 工具调用 | 是 | 是 |
| 思考控制 | 是，但供应商未公布调节参数 | 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 3.7 Flash | GLM-5 | |
|---|---|---|
| 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 | 2026-08-13 | 2026-02-12 |
| 知识截止日期 | 2026-03 | - |
| 上下文窗口 | 1M | 200K |
| 最大输出 | 66K | 131K |
| 思考参数 | - | thinking.type |
| 可选值 | - | thinking.type
|
| 默认值 | - | enabled, and on GLM-5 the model automatically determines whether to think |
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 3.7 Flash · GLM-5
同一条提示词，两个模型，经网关实测
Gemini 3.7 Flash 通过 · 3 sentences
Enabling reasoning causes the model to generate a high volume of invisible "thinking tokens" behind the scenes to deliberate and solve complex logic before delivering a final response. Because our vendor bills strictly on token volume, this additional internal computation effectively doubled our billable output per query. In short, our costs increased because we are now paying for the extra compute capacity required to produce significantly higher-accuracy answers.
输出 878 tok (+799 思考) 延迟 6.9 s
GLM-5 通过 · 3 sentences
Enabling reasoning forces the model to perform multiple hidden steps to "think" through a problem, drastically increasing the volume of data processed per query compared to a standard response. Because our billing is directly tied to this processing volume, the additional computational work required for higher accuracy has proportionally doubled our consumption. Essentially, the spike reflects a trade-off where we are paying a higher variable cost to secure significantly better analytical outcomes.
输出 1393 tok (+1308 思考) 延迟 36.0 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。
Gemini 3.7 Flash 通过 · 8/8 cases
Here is the corrected function: ```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 of the Bug The original function tracked equal adjacent transitions instead of the actual number of elements in a run, causing all non-empty run lengths to be undercounted by one (e.g., returning `0` instead of `1` for single-element lists because the loop never executes, and `1` instead of `2` for pairs). Initializing both `best` and `cur` to `1` (after handling empty inputs) and resetting `cur` to `1` on a mismatch ensures every individual element is counted as a valid run of at least length one.
输出 1050 tok (+827 思考) 延迟 6.4 s
GLM-5 通过 · 8/8 cases
Here is the fixed code: ```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 ``` **Explanation:** The bug stems from initializing `best` and `cur` to zero, which causes the function to count the number of *transitions* between adjacent elements rather than the total number of items in the run, resulting in a return value that is always one less than the actual length (reporting 0 for a single element and 1 for a pair). By initializing the counters to 1 and resetting `cur` to 1 when a streak breaks, the logic correctly counts the items themselves; adding an explicit check for an empty list ensures the function handles the zero-element edge case correctly.
输出 2907 tok (+2694 思考) 延迟 73.7 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。
Gemini 3.7 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 918 tok (+858 思考) 延迟 6.2 s
GLM-5 通过 · 5/5 fields, guidance "withheld pending the Q4 close"
```json { "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": 11, "guidance": "withheld pending the Q4 close" } ```
输出 3620 tok (+3561 思考) 延迟 91.8 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。
Gemini 3.7 Flash 通过 · 120 words, 0 banned, 1 question
Why pay twice for identical AI queries simply because you routed them to different model vendors? Today, we introduce Universal Prompt Caching directly within our unified API gateway architecture. This capability stores repeated prompt contexts across OpenAI, Anthropic, and local models, instantly returning stored results to eliminate redundant computation fees. When your application sends an LLM request, the gateway inspects the payload, identifies semantic matches, and returns accurate cached responses in under ten milliseconds. Engineering teams can now slash inference latency by eighty percent while dramatically reducing monthly token expenditures across diverse production deployments. You retain complete privacy control, flexible cache eviction policies, and granular metrics through a single dashboard. Update your routing settings today to accelerate overall system performance.
输出 2858 tok (+2718 思考) 延迟 14.1 s
GLM-5 通过 · 119 words, 0 banned, 1 question
We are thrilled to introduce Global Prompt Caching, a powerful new capability within our API gateway designed to optimize your AI operations. By intelligently storing prompt responses across every supported provider, this feature drastically reduces latency and cuts operational costs. Instead of processing identical requests repeatedly, our system serves cached results instantly, ensuring consistent performance even during high-traffic periods. Why pay full price for repeated inference on the same inputs? This update gives developers fine-grained control over cache lifetimes and hit rates, allowing for predictable budgeting and faster application response times. You can activate this functionality directly in your dashboard settings today. Start maximizing your efficiency now and deliver a snappier experience to your end-users without unnecessary API expenditure.
输出 715 tok (+571 思考) 延迟 18.9 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-3.7-flash",
# model="glm-5", # 取消注释此行，注释上一行
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: "gemini-3.7-flash",
// model: "glm-5", // 取消注释此行，注释上一行
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": "gemini-3.7-flash",
# "model": "glm-5", # 取消注释此行，注释上一行
"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: "gemini-3.7-flash",
// Model: "glm-5", // 取消注释此行，注释上一行
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("gemini-3.7-flash")
// .model("glm-5") // 取消注释此行，注释上一行
.addUserMessage("Summarize this diff")
.reasoningEffort(ReasoningEffort.MEDIUM)
.build());
System.out.println(resp.choices().get(0).message().content().orElse(""));常见问题
Gemini 3.7 Flash 和 GLM-5 哪个更便宜？
按「输入 / 1M token」算，Gemini 3.7 Flash 更便宜（$0.75 对 $1，相差 1.3×）。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 3.7 Flash 和 GLM-5 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 3.7 Flash 和 GLM-5 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。