Kimi K2.7 Code vs Qwen3.8 Flash
什么时候选哪个
如果看重处理量和广度，请选择 qwen3.8-flash：它的输入成本比 kimi-k2.7-code 低约 6.3x，输出成本低约 8.5x（输入 $0.15 对 $0.95，输出 $0.47 对 $4），提供 1000000-token 上下文（相比之下后者为 256000），最高支持 131072 个输出 token，并允许在对延迟敏感的调用中关闭 thinking。当你希望在专注于代码的模型中获得 Moonshot 始终开启的推理行为，且 256000 个 token 加上 32768 个输出的空间就足够时，请选择 kimi-k2.7-code。两者均接受文本、图像和视频输入并返回文本，因此主要区别在于价格、上下文大小以及对 thinking 的控制。
Benchmark 成绩
供应商公布： Alibaba (Qwen) Anthropic ByteDance DeepSeek Google MiniMax Moonshot OpenAI Tencent Z.ai
价格
| Kimi K2.7 Code | Qwen3.8 Flash | Δ | |
|---|---|---|---|
| 输入 / 1M token | $0.95 | $0.15 | 6.3× |
| 输出 / 1M token | $4 | $0.47 | 8.5× |
| 缓存读取 / 1M token | $0.19 | $0.016 | 12× |
| 缓存写入 | - | 1.25x | - |
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 80 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
| Kimi K2.7 Code | Qwen3.8 Flash | |
|---|---|---|
| 工具调用 | 是 | 是 |
| 思考控制 | 始终开启 | 可配置 |
| 结构化输出 | - | 是 |
| 提示词缓存 | 隐式（自动） | 隐式 + 显式 |
| 缓存有效期 | 未公布 | explicit: 5m, reset on hit |
| 最小缓存前缀 | 未公布 | 1,024 个 token |
规格
| Kimi K2.7 Code | Qwen3.8 Flash | |
|---|---|---|
| 输入模态 | 文本 图像 视频 | 文本 图像 视频 |
| 输出模态 | 文本 | 文本 |
| 发布日期 | 2026-06 | 2026-08-27 |
| 上下文窗口 | 256K | 1M |
| 最大输出 | 33K | 131K |
| 思考参数 |
|
|
| 可选值 | type
keep
| enable_thinking
thinking_budget
preserve_thinking
|
| 默认值 | thinking on with Preserved Thinking on | on qwen3.8-flash is hybrid thinking with thinking enabled by default, thinking_budget defaults to the 262,144-token chain maximum, and preserve_thinking is off |
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： Kimi K2.7 Code · Qwen3.8 Flash
同一条提示词，两个模型，经网关实测
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
Qwen3.8 Flash 通过 · 3 sentences
Enabling reasoning makes the model spend additional output tokens to plan and validate answers before responding, and those tokens are billed. It also increases compute and latency per request, so each task consumes more capacity than a non-reasoning call. As a result, billable tokens and compute roughly doubled, directly driving the higher invoice.
输出 330 tok (+262 思考) 延迟 8.9 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。
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
Qwen3.8 Flash 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 else: cur = 1 best = max(best, cur) return best ``` The bug is that the original version counts adjacent equal pairs rather than items in a run, starting both `cur` and `best` at `0`, and it resets `cur` to `0` after a mismatch, thereby discarding the first element of the new run. That makes single-element inputs return `0`, and because every run is effectively measured in “steps” between equal elements, the last element is not counted—particularly noticeable when the longest run ends at the final index.
输出 2123 tok (+1933 思考) 延迟 43.8 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。
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
Qwen3.8 Flash 通过 · 5/5 fields, guidance null
{ "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": 11, "guidance": null }
输出 519 tok (+463 思考) 延迟 10.5 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。
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
Qwen3.8 Flash 通过 · 120 words, 0 banned, 1 question
We are announcing prompt caching across providers in our API gateway. Store approved prompt outputs once and reuse them across supported model providers, cutting latency, cost, and duplicate token spend. The feature matches identical requests, checks validity, and returns cached results while preserving routing controls. Developers keep existing endpoints; the gateway manages storage, invalidation, and provider differences. This reduces noisy repeat calls, improves steady responses, and frees teams to focus on better agent workflows. OpenAI, Anthropic, Google, Mistral, and custom routes are supported. Cache hits appear in analytics with latency, token, and cost reductions visible. Check retention and privacy rules before enabling it. Ready to add cache controls to your gateway? Enable it in settings and watch spend drop now.
输出 5958 tok (+5805 思考) 延迟 88.8 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="kimi-k2.7-code",
# model="qwen3.8-flash", # 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: "kimi-k2.7-code",
// model: "qwen3.8-flash", // 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": "kimi-k2.7-code",
# "model": "qwen3.8-flash", # 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: "kimi-k2.7-code",
// Model: "qwen3.8-flash", // 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("kimi-k2.7-code")
// .model("qwen3.8-flash") // 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
Kimi K2.7 Code 和 Qwen3.8 Flash 哪个更便宜？
按「输入 / 1M token」算，Qwen3.8 Flash 更便宜（$0.15 对 $0.95，相差 6.3×）。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 Kimi K2.7 Code 和 Qwen3.8 Flash 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。
Kimi K2.7 Code 和 Qwen3.8 Flash 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。