Kimi K2.7 Code vs Qwen3.8 Max
何時該用哪一個 — 綜合評斷，而非基準測試數據表
kimi-k2.7-code 在價目表的每一行都更便宜，輸入 $0.95 對 $2（低約 2.1 倍）、輸出 $4 對 $6，而且它獨有地在文字和影像之外還接受視訊，儘管它的思考模式無法關閉。qwen3.8-max 每 token 更貴，但換來約 3.8 倍的脈絡（983616 token）、131072 token 的最大輸出（是 Kimi 的 32768 的 4 倍），以及明確的長脈絡和視覺標誌。常規大小輸入上的大批量編碼與推理，或視訊輸入工作選 kimi-k2.7-code；整個程式庫或超長生成必須裝進一次呼叫時選 qwen3.8-max。
定價
| Kimi K2.7 Code | Qwen3.8 Max | Δ | |
|---|---|---|---|
| 輸入 / 1M tokens | $0.95 | $2 | 0.47× |
| 輸出 / 1M tokens | $4 | $6 | 0.67× |
| 快取讀取 / 1M tokens | $0.19 | $0.25 | 0.76× |
| 快取寫入 | — | 1.25x | — |
費率取自建置時的即時目錄；各模型頁面皆附有目前的費率卡。
它們的相對位置 — 在此計費單位下，所有 63 個 聊天 模型的 每 1M tokens 的輸入價格（對數尺度）
能力
| Kimi K2.7 Code | Qwen3.8 Max | |
|---|---|---|
| 工具使用 | 是 | 是 |
| 思考控制 | 常駐開啟 | 是 —— 廠商未公布調節參數 |
| 結構化輸出 | — | 是 |
| 提示快取 | 隱式（自動） | 隱式 + 顯式 |
| 快取生命週期 | 未公開 | explicit: 5m, reset on hit |
| 最小快取前綴 | 未公開 | 1024 個 token |
規格
| Kimi K2.7 Code | Qwen3.8 Max | |
|---|---|---|
| 輸入模態 | 文字 影像 影片 | 文字 影像 |
| 輸出模態 | 文字 | 文字 |
| 發布日期 | 2026-06 | 2026-08-03 |
| 上下文視窗 | 256K | 984K |
| 最大輸出 | 33K | 131K |
| 思考參數 |
| — |
| 可接受的值 | type
keep
| — |
| 預設值 | thinking on with Preserved Thinking on | — |
規格摘錄自各供應商的文件；供應商未發布的資料列會直接省略，而非自行推測。 完整來源： Kimi K2.7 Code · Qwen3.8 Max
單一提示詞，兩款模型 — 經由閘道測量
Kimi K2.7 Code 通過 · 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.
輸出 259 tok (+174 思考) 延遲 5.4 s
Qwen3.8 Max 通過 · 3 sentences
Enabling reasoning makes the model produce additional hidden steps before responding, and those tokens are billable. It also tends to lengthen each interaction because the model works through more possibilities before settling on an answer. Therefore, the bill doubled mainly due to higher compute and token usage per request, not necessarily because the number of requests doubled.
輸出 378 tok (+305 思考) 延遲 8.6 s
指令遵循（恰好三句，可數）、受眾適配（面向 CFO 的語氣），以及下方 token 計量所暴露的隱藏思考計費缺口。
Kimi K2.7 Code 通過 · 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.
輸出 410 tok (+222 思考) 延遲 9.4 s
Qwen3.8 Max 通過 · 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: cur = 1 best = max(best, cur) return best ``` The bug is that the original code starts `best` and `cur` at `0`, so it counts adjacent equal *transitions* rather than the number of items in the run. A run of length `n` has only `n - 1` equal-neighbor transitions, so single-element inputs return `0`, and runs that reach the end are undercounted by one. Initializing the current run to `1` for the first element, resetting it to `1` on a break, and updating `best` from that count fixes the off-by-one.
輸出 1616 tok (+1411 思考) 延遲 34.7 s
修復是否真的正確（可執行）、解釋的資訊密度，以及在一個邊界明確的任務上的 token 效率。
Kimi K2.7 Code 通過 · 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" } ```
輸出 721 tok (+663 思考) 延遲 13.2 s
Qwen3.8 Max 通過 · 5/5 fields, guidance null
{ "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": 11, "guidance": null }
輸出 1199 tok (+1141 思考) 延遲 24.4 s
schema 服從度（不臆造欄位）、幻覺壓力（guidance 明確被暫緩給出），以及結構化輸出路徑的差異。
Kimi K2.7 Code 通過 · 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.
輸出 2375 tok (+2235 思考) 延遲 38.6 s
Qwen3.8 Max 通過 · 120 words, 0 banned, 1 question
Today, our API gateway adds prompt caching across major model providers. It stores prompts and responses in one fast cache layer. Teams can lower token spend, reduce latency, and repeat reliable answers. The feature supports OpenAI, Anthropic, Google, and Mistral through one configuration. You can set retention rules, scope access, and invalidate entries quickly. How does your team maintain consistent results during provider outages? Approved cached responses keep applications stable while fallback routes recover. The dashboard shows hit rates, savings, latency, and provider usage. Engineers receive audit trails for every cached prompt, enabling safer testing. Product managers can compare cost trends before and after cache adoption. Start with a small route, then safely expand caching to production traffic right now.
輸出 2744 tok (+2591 思考) 延遲 46.3 s
約束服從度（字數預算、禁用詞表、唯一的那句問句）、文風指紋，以及長度控制。
只需一行程式碼即可在兩者間切換
以下每個頁籤中都有這兩個 ID — 醒目提示的這兩行是唯一的修改處。相同的端點，相同的金鑰，相同的請求結構。
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-max", # 取消註解此行，並註解上一行
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-max", // 取消註解此行，並註解上一行
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-max", # 取消註解此行，並註解上一行
"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-max", // 取消註解此行，並註解上一行
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-max") // 取消註解此行，並註解上一行
.addUserMessage("Summarize this diff")
.reasoningEffort(ReasoningEffort.MEDIUM)
.build());
System.out.println(resp.choices().get(0).message().content().orElse(""));常見問題
Kimi K2.7 Code 和 Qwen3.8 Max 哪個比較便宜？
Kimi K2.7 Code 在 輸入 / 1m tokens 上較便宜（$0.95 對比 $2，相差 2.1×）。其他項目可能呈現相反結果 — 上表提供完整資訊，實際成本取決於您的使用組合。
我可以在不進行兩次整合的情況下，對 Kimi K2.7 Code 和 Qwen3.8 Max 進行 A/B 測試嗎？
可以。兩者皆透過同一個相容 OpenAI 的端點提供服務，並使用同一把 API 金鑰 — 切換只需更改一行的模型字串，因此您可以將部分流量分別導向兩者並直接比較帳單。
Kimi K2.7 Code 與 Qwen3.8 Max 支援提示快取嗎？
是的 — 兩者的快取讀取費率皆低於其輸入費率，因此具有暖前綴的工作負載成本會低於牌價所示。確切的快取讀取列請見上方的定價表。