GLM-5.1 vs GPT-5.6
什么时候选哪个
glm-5.1 是更便宜的纯文本选项，每百万输入 $1.4、输出 $4.4，上下文 200000 token、最大输出 131072 token，因此适合每个 token 都要计较的大批量对话、代码和工具调用工作。gpt-5.6 输入贵约 3.6 倍、输出贵约 6.8 倍（每百万 $5 和 $30），但它在文本之外接受图像，并带 1050000 token 上下文，是前者的五倍多。需要视觉或超长输入时选 gpt-5.6；否则 glm-5.1 以更低价格覆盖同样的推理和工具标志。
Benchmark 成绩
GLM-5.1：供应商没有公布 benchmark 成绩。
供应商公布： Alibaba (Qwen) Anthropic ByteDance DeepSeek Google MiniMax Moonshot OpenAI Tencent Z.ai
价格
| GLM-5.1 | GPT-5.6 | Δ | |
|---|---|---|---|
| 输入 / 1M token | $1.4 | $5 | 0.28× |
| 输出 / 1M token | $4.4 | $30 | 0.15× |
| 缓存读取 / 1M token | $0.26 | $0.5 | 0.52× |
| 缓存写入 | - | 不单独收费 | - |
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）
能力
规格
| GLM-5.1 | 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 | 2026-04-07 | 2026-07-09 |
| 知识截止日期 | - | 2026-02 |
| 上下文窗口 | 200K | 1.1M |
| 最大输出 | 131K | 128K |
| 思考参数 | thinking.type | reasoning.effort |
| 可选值 | thinking.type
| reasoning.effort
|
| 默认值 | enabled, and the model automatically determines whether to think | medium |
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： GLM-5.1 · GPT-5.6
同一条提示词，两个模型，经网关实测
GLM-5.1 通过 · 3 sentences
Enabling reasoning shifts the LLM from generating quick answers to producing hidden "chain-of-thought" tokens that systematically explore multiple solution paths before responding. Because billing is based on total tokens processed, these invisible reasoning tokens drastically increase input and output volume per query, often adding thousands of compute-intensive tokens to a single prompt. Consequently, our bill doubled not from higher per-token rates or increased user volume, but from the exponentially deeper computational work required to deliver higher-quality, more reliable outputs.
输出 98 tok 延迟 7.8 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，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。
GLM-5.1 通过 · 8/8 cases
```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 original function initialized `cur` and `best` to `0` and only incremented `cur` when a duplicate was found. This counts the number of *extra* equal items in a run rather than the total run length, so every run is reported as one shorter than it actually is—returning `0` for single-element lists and `L-1` for a run of length `L` (including those ending at the last index). The fix initializes `cur` and `best` to `1` (when the list is non‑empty) and resets `cur` to `1` on a mismatch, so `cur` tracks the full length of the current run and `best` records the true maximum.
输出 3823 tok (+3585 思考) 延迟 49.7 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（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。
GLM-5.1 通过 · 5/5 fields, guidance "withheld pending the Q4 close"
```json { "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": "eleven and change", "guidance": "withheld pending the Q4 close" } ```
输出 1091 tok (+1028 思考) 延迟 15.2 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
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。
GLM-5.1 通过 · 120 words, 0 banned, 1 question
We are excited to introduce Prompt Cache for our API Gateway. This new feature stores responses for identical prompts, routing subsequent requests directly to the cache instead of calling the underlying AI provider. Are you tired of paying multiple times for the exact same query? Prompt Cache solves this by recognizing duplicate inputs across all supported providers, drastically reducing latency and operational costs. When a user submits a request that matches a previously cached prompt, the gateway returns the stored answer instantly. You can configure cache expiration and scope rules via your dashboard to maintain data freshness. Stop wasting budget on redundant computational work. Upgrade to the latest gateway tier today to start saving time and money on every call.
输出 3935 tok (+3802 思考) 延迟 41.1 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
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="glm-5.1",
# 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-5.1",
// 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-5.1",
# "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-5.1",
// 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-5.1")
// .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-5.1 和 GPT-5.6 哪个更便宜？
按「输入 / 1M token」算，GLM-5.1 更便宜（$1.4 对 $5，相差 3.6×）。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 GLM-5.1 和 GPT-5.6 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。
GLM-5.1 和 GPT-5.6 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。