GPT-6 Sol vs Kimi K2.7 Code
什麼情況選哪一個
當任務需要較大空間時請選擇 gpt-6-sol：其 1050000-token 的 context 大約是 kimi-k2.7-code 256000 的 4x，可輸出高達 128000 個 token（相較於 32768），具備 vision 標記，且其 thinking 能夠為了延遲敏感的呼叫被關閉。若要降低高用量 coding 與工具任務的成本請選擇 kimi-k2.7-code，其 input 為 $0.95 且 output 為 $4，相較於 $2 與 $10 大約便宜 2.1x 與 2.5x，接受 video 作為 input 且 reasoning 永遠開啟。Cache 讀取成本幾乎相同，為 $0.19 對比 $0.2，因此快取策略幾乎不會改變選擇。
Benchmark 成績
供應商公布： Alibaba (Qwen) Anthropic DeepSeek Google Moonshot OpenAI Z.ai
定價
| GPT-6 Sol | Kimi K2.7 Code | Δ | |
|---|---|---|---|
| 輸入 / 1M tokens | $2 | $0.95 | 2.1× |
| 輸出 / 1M tokens | $10 | $4 | 2.5× |
| 快取讀取 / 1M tokens | $0.2 | $0.19 | 1.1× |
| 快取寫入 | 1.25x (30m) / 2.5x (12h) | - | - |
費率取自網站建置時的即時目錄；各模型頁面都列有最新的價目。
兩者的相對位置：每 1M tokens 的輸入價格，涵蓋同一計費單位下全部 83 個聊天模型（對數尺度）
功能
| GPT-6 Sol | Kimi K2.7 Code | |
|---|---|---|
| 工具使用 | 是 | 是 |
| 思考控制 | 可設定 | 一律開啟 |
| 結構化輸出 | 是 | - |
| 提示詞快取 | 隱式 + 顯式 | 隱式（自動） |
| 快取存活時間 | at least 30m by default, 12h option on the Responses API | 未公布 |
| 最小快取前綴 | 1,024 個 token | 未公布 |
規格
| GPT-6 Sol | Kimi K2.7 Code | |
|---|---|---|
| 輸入模態 | 文字 圖像 | 文字 圖像 影片 |
| 輸出模態 | 文字 | 文字 |
| 發布日期 | 2026-09-22 | 2026-06 |
| 知識截止日期 | 2026-04 | - |
| 上下文視窗 | 1.1M | 256K |
| 最大輸出 | 128K | 33K |
| 思考參數 | reasoning.effort |
|
| 可接受的值 | reasoning.effort
| type
keep
|
| 預設值 | medium | thinking on with Preserved Thinking on |
規格摘錄自各供應商的文件；供應商沒有公布的項目就直接略過，不自行推測。 完整來源： GPT-6 Sol · Kimi K2.7 Code
同一段提示詞，兩個模型，經閘道實測
GPT-6 Sol 通過 · 3 sentences
Enabling reasoning made the model generate extra internal tokens to work through each request, and those tokens are billed even though users don’t see them. If request volume stayed roughly the same, more billed tokens per request could explain why the bill doubled. We should check the usage breakdown to confirm, then lower reasoning effort or limit it to tasks that need it.
輸出 188 tok (+111 思考) 延遲 5.0 s
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
是否照指令做（剛好三句，數得出來）、語氣是否貼合對象（寫給 CFO 看），以及下方 token 計量顯示出的隱藏思考計費落差。
GPT-6 Sol 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 original `cur` counted matching *adjacent pairs*, not items, so every run was one too short—including a single-element input, which returned 0. Starting each run at 1 counts its first item, and updating `best` on every iteration ensures a run that reaches the last index is included.
輸出 206 tok (+59 思考) 延遲 5.9 s
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
修正是否真的正確（跑得起來）、說明的資訊密度，以及在範圍明確的任務上的 token 效率。
GPT-6 Sol 通過 · 5/5 fields, guidance "Withheld pending the Q4 close"
{"company":"Meridian Robotics","quarter":"September quarter","revenue_usd_m":null,"growth_pct":null,"guidance":"Withheld pending the Q4 close"}
輸出 227 tok (+185 思考) 延遲 5.2 s
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
是否遵守 schema（不自創欄位）、面對幻覺誘因的表現（題目明確不給 guidance），以及結構化輸出路徑的差異。
GPT-6 Sol 通過 · 120 words, 0 banned, 1 question
Today, we’re introducing cross-provider prompt caching for our API gateway, helping teams reuse repeated prompt content when routing requests among supported AI providers. The gateway identifies eligible prompt prefixes, stores cache references, and applies them to subsequent requests where provider capabilities allow. Why send the same context again if it can be reused? With fewer duplicate input tokens, applications can reduce costs and improve latency without changing how developers call the gateway. Configure caching by route, monitor hit rates and savings in your dashboard, and keep existing provider fallback rules in place. Availability and cache behavior vary by provider, so the gateway reports eligibility and usage for each request. Get started today with our documentation and test your existing workloads.
輸出 586 tok (+443 思考) 延遲 7.7 s
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
是否遵守限制（字數上限、禁用詞清單、只能有一句問句）、文字風格的特徵，以及長度控制。
改一行程式碼就能在兩者之間切換
下面每個頁籤都列了這兩個模型 ID，要改的只有醒目標示的那兩行。端點、金鑰和請求格式都不變。
from openai import OpenAI
client = OpenAI(
base_url="https://synthorai.io/v1",
api_key="sk-syn-...",
)
resp = client.chat.completions.create(
model="gpt-6-sol",
# model="kimi-k2.7-code", # 取消這一行的註解，並把上一行註解掉
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: "gpt-6-sol",
// model: "kimi-k2.7-code", // 取消這一行的註解，並把上一行註解掉
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": "gpt-6-sol",
# "model": "kimi-k2.7-code", # 取消這一行的註解，並把上一行註解掉
"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: "gpt-6-sol",
// Model: "kimi-k2.7-code", // 取消這一行的註解，並把上一行註解掉
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("gpt-6-sol")
// .model("kimi-k2.7-code") // 取消這一行的註解，並把上一行註解掉
.addUserMessage("Summarize this diff")
.reasoningEffort(ReasoningEffort.MEDIUM)
.build());
System.out.println(resp.choices().get(0).message().content().orElse(""));常見問題
GPT-6 Sol 和 Kimi K2.7 Code 哪個比較便宜？
以「輸入 / 1M tokens」來看，Kimi K2.7 Code 比較便宜（$0.95 對 $2，相差 2.1×）。其他項目的結果可能相反，完整價目請看上表；實際成本要看你的用量組合。
可以只串接一次，就對 GPT-6 Sol 和 Kimi K2.7 Code 做 A/B 測試嗎？
可以。兩個模型都走同一個 OpenAI 相容端點，用的也是同一把 API 金鑰，切換時只要改一行裡的模型名稱字串。你可以把一部分流量分別導到兩邊，再直接比較帳單。
GPT-6 Sol 與 Kimi K2.7 Code 支援提示詞快取嗎？
支援。兩者的快取讀取費率都低於輸入費率，所以前綴已經進快取的工作負載，實際成本會比官網價算出來的低。確切的快取讀取價格請見上方定價表。