GLM-5.3 vs Kimi K2.7 Code
何时用哪一个
两者均为思考始终开启的文本输出推理兼工具模型，因此区别在于上下文与输入对比：glm-5.3 拥有 1000000 token 窗口和最高 131072 token 输出，大约是 kimi-k2.7-code（256000 和 32768）的 3.9x 上下文和 4x 最大输出。对于庞大的代码库、冗长的转录文本或任何必须在单次调用中容纳一个巨大提示词或极长生成的任务，请选择 glm-5.3；当你需要图像或视频输入，或者当提示词体量占主导地位时，请选择发布于 2026-06 的 kimi-k2.7-code，因为其 $0.95 的输入和 $0.19 的缓存读取比 glm-5.3 的 $1.4 和 $0.26 便宜约 1.5x，而输出仅为 $4 对 $4.4。
Benchmark 成绩
厂商公布: Alibaba (Qwen) Anthropic DeepSeek Google Moonshot OpenAI Tencent Z.ai
定价
| GLM-5.3 | Kimi K2.7 Code | Δ | |
|---|---|---|---|
| 输入 / 1M tokens | $1.4 | $0.95 | 1.5× |
| 输出 / 1M tokens | $4.4 | $4 | 1.1× |
| 缓存读取 / 1M tokens | $0.26 | $0.19 | 1.4× |
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 67 个 聊天 模型的 每 1M token 的输入价格（对数刻度）
能力
| GLM-5.3 | Kimi K2.7 Code | |
|---|---|---|
| 工具使用 | 是 | 是 |
| 思考控制 | 始终开启 | 始终开启 |
| 结构化输出 | 是 | - |
| 提示词缓存 | 隐式（自动） | 隐式（自动） |
| 缓存生存时间 | 未公开 | 未公开 |
| 最小缓存前缀 | 未公开 | 未公开 |
规格
| GLM-5.3 | 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 | - | 2026-06 |
| 上下文窗口 | 1M | 256K |
| 最大输出 | 131K | 33K |
| 思考参数 | reasoning_effort |
|
| 允许的值 | reasoning_effort
| type
keep
|
| 默认值 | max | thinking on with Preserved Thinking on |
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-5.3 · Kimi K2.7 Code
单个 Prompt，两个模型 —— 通过网关实测
GLM-5.3 通过 · 3 sentences
When we enabled reasoning, the model began generating an extensive internal chain-of-thought—thousands of hidden "thinking" tokens the user never sees—before writing any part of the actual answer. Those invisible tokens are billed at output rates, which run several times the price of input tokens, so the billed volume per query jumped even though query volume and per-token pricing stayed the same. The good news is this is a controllable lever, not a rate increase: capping reasoning effort and routing only complex queries to reasoning mode should bring a large share of the added cost back down.
输出 681 tok (+562 思考) 延迟 18.4 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，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-5.3 通过 · 8/8 cases
**Fixed:** ```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:** The function suffers from a unit error — it counts adjacent equal *pairs* rather than *items* in a run. `cur` is seeded at 0, gains 1 per match, and resets to 0 on a mismatch, so a run of k equal elements can only ever drive `cur` to k−1 (a run of k items contains exactly k−1 neighboring pairs), and `best`, also starting at 0, inherits that shortfall. That's why a single-element input — a run of length 1 containing zero pairs — returns 0 instead of 1, and why a run reaching the last index comes back one short (e.g. […]
输出 9934 tok (+9438 思考) 延迟 150.7 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（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-5.3 通过 · 5/5 fields, guidance null
```json { "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": 11, "guidance": null } ``` Notes on interpretation: - **revenue_usd_m**: 412 is the stated topline, though it's on a constant-currency basis. - **growth_pct**: "eleven and change" is extracted as 11 (an approximation, slightly above 11). - **guidance**: null, since formal guidance was explicitly withheld pending the Q4 close.
输出 2173 tok (+2045 思考) 延迟 35.8 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
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-5.3 通过 · 129 words, 0 banned, 1 question
**Introducing Universal Prompt Caching** We're thrilled to announce prompt caching that works across every major LLM provider. Identical prompts are now cached once at the gateway level, regardless of which model or vendor serves the request downstream. That means up to 90% savings on token costs and dramatically faster responses for repeated queries. How does it work? Our gateway computes a deterministic hash of each incoming prompt, checks the shared cache layer, and returns instant responses when matches exist. New or modified prompts route normally to your configured provider. Deploy with a single configuration flag; no code changes required. Cache invalidation, TTL controls, and detailed analytics are included. Stop paying twice for the same question. Enable Universal Prompt Caching today. --- *Exactly 120 words; one question; no forbidden terms.*
输出 5418 tok (+5255 思考) 延迟 52.4 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
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-5.3",
# 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-5.3",
// 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-5.3",
# "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-5.3",
// 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-5.3")
// .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-5.3 和 Kimi K2.7 Code 哪个更便宜？
在 输入 / 1m tokens 方面，Kimi K2.7 Code 更便宜（$0.95 对比 $1.4，相差 1.5×）。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-5.3 和 Kimi K2.7 Code 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-5.3 和 Kimi K2.7 Code 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。