GLM-5.1 vs Kimi K2.7 Code
什么时候选哪个
需要超长单次回复或关闭思考的选项时选 glm-5.1：它允许最多 131072 输出 token，而 kimi-k2.7-code 是 32768，而且它的推理是可选的而非常开。需要图像和视频输入、256000（而非 200000）token 上下文，以及更便宜的价目表时选 kimi-k2.7-code——输入 $0.95 对 $1.4（低约 1.5 倍）、输出 $4 对 $4.4。两者都覆盖对话、代码、推理和工具，所以真正的决定是输出长度与可切换思考，对多模态输入与上下文的取舍。
Benchmark 成绩
GLM-5.1：供应商没有公布 benchmark 成绩。
供应商公布： Alibaba (Qwen) Moonshot OpenAI Z.ai
价格
| GLM-5.1 | Kimi K2.7 Code | Δ | |
|---|---|---|---|
| 输入 / 1M token | $1.4 | $0.95 | 1.5× |
| 输出 / 1M token | $4.4 | $4 | 1.1× |
| 缓存读取 / 1M token | $0.26 | $0.19 | 1.4× |
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
| GLM-5.1 | Kimi K2.7 Code | |
|---|---|---|
| 工具调用 | 是 | 是 |
| 思考控制 | 可配置 | 始终开启 |
| 结构化输出 | 是 | - |
| 提示词缓存 | 隐式（自动） | 隐式（自动） |
| 缓存有效期 | 未公布 | 未公布 |
| 最小缓存前缀 | 未公布 | 未公布 |
规格
| GLM-5.1 | Kimi K2.7 Code | |
|---|---|---|
| 输入模态 | 文本 | 文本 图像 视频 |
| 输出模态 | 文本 | 文本 |
| 发布日期 | 2026-04-07 | 2026-06 |
| 上下文窗口 | 200K | 256K |
| 最大输出 | 131K | 33K |
| 思考参数 | thinking.type |
|
| 可选值 | thinking.type
| type
keep
|
| 默认值 | enabled, and the model automatically determines whether to think | thinking on with Preserved Thinking on |
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 · Kimi K2.7 Code
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
Kimi K2.7 Code 通过 · 3 sentences
Enabling reasoning mode causes the model to generate an internal chain-of-thought before producing each answer, and that hidden reasoning text is billed like any other output. Those extra reasoning tokens typically add up to much more than the final response itself, so every API call now costs significantly more than before. Because our overall usage volume did not change, the doubled bill reflects that each query consumes roughly twice as many billable tokens.
输出 259 tok (+174 思考) 延迟 5.4 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
Kimi K2.7 Code 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 ``` The bug is that the function counts *transitions* between equal neighbors instead of counting the items themselves, so a single-element list never enters the loop and returns `0` instead of `1`. Likewise, resetting `cur` to `0` on a mismatch forgets the current item that starts a new run, which causes runs that end at the last index to be undercounted. Initializing both `best` and `cur` to `1` and resetting `cur` to `1` fixes both issues.
输出 410 tok (+222 思考) 延迟 9.4 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。
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
Kimi K2.7 Code 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 721 tok (+663 思考) 延迟 13.2 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
Kimi K2.7 Code 通过 · 120 words, 0 banned, 1 question
We're introducing Prompt Cache for our API Gateway, a new capability that stores prompt responses across multiple AI providers. By caching replies, you reduce redundant calls, lower latency, and cut costs without changing how you build. Teams using several model providers can now reuse identical prompt results instantly, keeping traffic consistent and budgets predictable. Responses are keyed by provider, model, and exact prompt, so you always get the right result. Configure TTL, hit thresholds, and eviction rules from a single dashboard. It fits into your existing routing and requires no code changes. Setup takes minutes and works with your current endpoints. Want to see how much latency and spend you can trim? Check the docs to enable Prompt Cache today.
输出 2375 tok (+2235 思考) 延迟 38.6 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="kimi-k2.7-code", # 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: "kimi-k2.7-code", // 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": "kimi-k2.7-code", # 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: "kimi-k2.7-code", // 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("kimi-k2.7-code") // 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 Kimi K2.7 Code 哪个更便宜？
按「输入 / 1M token」算，Kimi K2.7 Code 更便宜（$0.95 对 $1.4，相差 1.5×）。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 Kimi K2.7 Code 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 Kimi K2.7 Code 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。