GLM-5.2 vs GPT-5.6
何时使用哪一个 — 经过整理的结论，而非基准测试表格
两者的上下文都在百万 token 左右（glm-5.2 为 1,000,000，gpt-5.6 为 1,050,000），也都允许关闭思考，所以真正的差别是价格和模态：gpt-5.6 输入贵约 6.5 倍（$5 对 $0.77）、输出贵约 12 倍（$30 对 $2.42）。需要在文本之外接受图像输入，或需要它 2026-02 的知识截止时选 gpt-5.6；以低得多的费率做纯文本对话、代码、推理和工具工作时选 glm-5.2，它的缓存读取更便宜（$0.143 对 $0.5），最大输出也略大，为 131072 token。
定价
| GLM-5.2 | GPT-5.6 | Δ | |
|---|---|---|---|
| 输入 / 1M tokens | $0.77 | $5 | 0.15× |
| 输出 / 1M tokens | $2.42 | $30 | 0.081× |
| 缓存读取 / 1M tokens | $0.143 | $0.5 | 0.29× |
| 缓存写入 | — | 不单独收费 | — |
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）
能力
规格
| GLM-5.2 | GPT-5.6 | |
|---|---|---|
| 输入模态 | 文本 | 文本 图像 |
| 输出模态 | 文本 | 文本 |
| 发布日期 | 2026-06-16 | 2026-07-09 |
| 知识截止日期 | — | 2026-02 |
| 上下文窗口 | 1M | 1.1M |
| 最大输出 | 131K | 128K |
| 思考参数 |
| reasoning.effort |
| 允许的值 | thinking.type
reasoning_effort
| reasoning.effort
|
| 默认值 | enabled, with reasoning_effort at max: the only GLM with an effort dial, and it defaults to the top of it | medium |
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： GLM-5.2 · GPT-5.6
单个 Prompt，两个模型 —— 通过网关实测
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
GPT-5.6 通过 · 3 sentences
Enabling reasoning makes the model generate many additional internal tokens before producing an answer, so each request consumes substantially more compute. Those reasoning tokens are billed even though they are not visible to users, and longer processing can also increase tool calls, retries, and output length. The bill doubled because our cost per request rose, not necessarily because usage volume increased; we can control it by limiting reasoning effort and routing simpler tasks to cheaper models.
输出 126 tok (+30 思考) 延迟 3.0 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。
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
GPT-5.6 通过 · 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: best = max(best, cur) cur = 1 return max(best, cur) ``` The original code counted equal *transitions* rather than items, so a run of one had length `0`, a run of three had length `2`, and tracking from zero caused the off-by-one error. The corrected version starts each run at length `1`, resets to `1` when the value changes, and performs a final `max` after the loop so a run ending at the last element is included; an empty input still returns `0`.
输出 277 tok (+93 思考) 延迟 4.2 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。
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
GPT-5.6 通过 · 5/5 fields, guidance "withheld pending the Q4 close"
{ "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": null, "guidance": "withheld pending the Q4 close" }
输出 179 tok (+118 思考) 延迟 3.7 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。
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
GPT-5.6 通过 · 120 words, 0 banned, 1 question
Today we are introducing Prompt Cache, a new API gateway feature that stores reusable prompt prefixes and serves them across supported model providers. By recognizing identical system instructions, tools, examples, and context blocks, Prompt Cache reduces repeated input processing, lowers latency, and helps control token costs without changing application code. Teams can configure retention windows, cache keys, provider routing, encryption, and regional storage policies from one dashboard or API. What happens when a preferred provider is unavailable? The gateway can route requests to another provider while reusing eligible cached content, preserving performance and consistency. Built-in metrics report hit rates, savings, latency, and provider usage, while audit logs support governance. Prompt Cache is available today in public preview for all customers.
输出 628 tok (+473 思考) 延迟 7.3 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="glm-5.2",
# model="gpt-5.6", # 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: "glm-5.2",
// model: "gpt-5.6", // 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": "glm-5.2",
# "model": "gpt-5.6", # 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: "glm-5.2",
// Model: "gpt-5.6", // 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("glm-5.2")
// .model("gpt-5.6") // 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
GLM-5.2 和 GPT-5.6 哪个更便宜？
在 输入 / 1m tokens 方面，GLM-5.2 更便宜（$0.77 对比 $5，相差 6.5×）。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 GLM-5.2 和 GPT-5.6 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。
GLM-5.2 和 GPT-5.6 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。