Gemini 3.8 Flash vs Kimi K3
何時用哪一個
兩款模型皆提供相同的 1048576 token 上下文視窗，因此差異在於價格、模態與輸出長度：gemini-3.8-flash 的輸入為 $0.75、輸出為 $3.75，而 kimi-k3 則分別為 $3 與 $15，這使得 Google 的模型在兩端皆便宜 4x，且在快取讀取上也便宜 4x（$0.075 對比 $0.3）。對於價格敏感的高用量工作，或當您除了文字與圖片外還需要輸入音訊與視訊時，請選擇 gemini-3.8-flash。當單次回覆需要長篇幅時請選擇 kimi-k3，因為其 1048576 token 的最大輸出遠超過 Flash 的 65536 上限；請注意其推理功能無法關閉。
Benchmark 成績
雙方都被測過的 5 項。
廠商公布: Alibaba (Qwen) Anthropic ByteDance DeepSeek Google MiniMax Moonshot OpenAI Tencent Z.ai
定價
| Gemini 3.8 Flash | Kimi K3 | Δ | |
|---|---|---|---|
| 輸入 / 1M tokens | $0.75 | $3 | 0.25× |
| 輸出 / 1M tokens | $3.75 | $15 | 0.25× |
| 快取讀取 / 1M tokens | $0.075 | $0.3 | 0.25× |
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 71 個 聊天 模型的 每 1M tokens 的輸入價格（對數尺度）
能力
| Gemini 3.8 Flash | Kimi K3 | |
|---|---|---|
| 工具使用 | 是 | 是 |
| 思考控制 | 是 —— 廠商未公布調節參數 | 常駐開啟 |
| 結構化輸出 | 是 | 是 |
| 提示快取 | 隱式 + 顯式 | 隱式（自動） |
| 快取生命週期 | 未公開 | 未公開 |
| 最小快取前綴 | 4096 個 token | 未公開 |
規格
| Gemini 3.8 Flash | Kimi K3 | |
|---|---|---|
| 輸入模態 | 文字 影像 音訊 影片 | 文字 影像 影片 |
| 輸出模態 | 文字 | 文字 |
| 發布日期 | 2026-09-02 | - |
| 知識截止日期 | 2026-03 | - |
| 上下文視窗 | 1M | 1M |
| 最大輸出 | 66K | 1M |
| 思考參數 | - | reasoning_effort (top-level; the thinking object is not accepted) |
| 可接受的值 | - | reasoning_effort
|
| 預設值 | - | max |
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： Gemini 3.8 Flash · Kimi K3
單一提示詞，兩款模型 — 經由閘道測量
Gemini 3.8 Flash 通過 · 3 sentences
When we enabled reasoning, the model began generating hidden "thinking tokens" to work through logic step-by-step before producing a final answer. Because AI vendors bill for every single token processed—visible or not—this internal deliberation dramatically inflated our billable volume per query. In short, while our total number of user requests remained flat, the cost per transaction doubled to buy higher accuracy on complex tasks.
輸出 705 tok (+624 思考) 延遲 6.4 s
Kimi K3 通過 · 3 sentences
Reasoning models don't just answer questions—they "think" first, generating long internal chains of step-by-step logic before producing a response. Those hidden thinking steps are billed as output tokens (the most expensive kind, typically 3–5x the price of input tokens), and a single query can generate thousands of them even when the visible answer is only a paragraph long. So you're paying for dramatically more compute per request: the bill doubled because the model does far more work behind the scenes, not because usage increased.
輸出 755 tok (+637 思考) 延遲 20.8 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。
Gemini 3.8 Flash 通過 · 8/8 cases
Here is the corrected function: ```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 of the Bug The original function counted equal-neighbor transitions rather than the actual items making up each run. Because `cur` started at `0` (and was reset to `0` on mismatches), every run was undercounted by one: single-element lists returned `0` because the loop never executed, and any subsequent run—including those ending at the last index—recorded a count of $N - 1$ instead of $N$. The fix handles empty input upfront, initializes both `cur` and `best` to `1` (since any single element forms a run of length 1), and resets `cur` to `1` whenever a new run begins.
輸出 1393 tok (+1149 思考) 延遲 10.1 s
Kimi K3 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 a fencepost error: `cur` counts *matching adjacent pairs* rather than *items in the run*, because it starts at 0 and only increments when `xs[i] == xs[i - 1]`. A run of length L contains L−1 equal pairs, so every run was undercounted by exactly one. For a single-element list (a run of length 1) the loop never runs and the function returns 0 instead of 1; likewise any run ending at the last index — e.g. the two `2`s in `[1, 2, 2]` — reported 1 instead of 2, making it look like no run existed at all. […]
輸出 1837 tok (+1547 思考) 延遲 47.2 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。
Gemini 3.8 Flash 通過 · 5/5 fields, guidance null
```json { "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": null, "guidance": null } ```
輸出 3989 tok (+3932 思考) 延遲 30.4 s
Kimi K3 通過 · 5/5 fields, guidance null
{ "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": 11, "guidance": null }
輸出 924 tok (+863 思考) 延遲 25.9 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。
Gemini 3.8 Flash 通過 · 120 words, 0 banned, 1 question
Why pay multiple model providers for the exact same output? Introducing Universal Prompt Cache, our latest API gateway capability engineered to cut compute expenses and drop inference latency. When your application makes a call, the gateway inspects a central memory layer before routing traffic to external LLMs. If an identical query was previously processed by OpenAI, Anthropic, or Mistral, our gateway returns that response immediately. This shared cache eliminates duplicate token fees and insulates your production apps from vendor rate limits or unexpected downtime. Developers can easily customize expiration settings, enforce strict data privacy controls, and configure invalidation logic across every endpoint. Stop wasting your budget on repeated queries. Enable prompt caching in your dashboard to accelerate your pipeline today.
輸出 3833 tok (+3688 思考) 延遲 21.8 s
Kimi K3 通過 · 120 words, 0 banned, 1 question
Meet Prompt Cache, a new API gateway capability that stores prompt responses and serves them across OpenAI, Anthropic, Google, and Azure endpoints. It matches requests by model, prompt hash, tools, temperature, and tenant policy, so repeated work returns fast while sensitive contexts stay isolated. Teams set TTLs, stale rules, encryption scopes, and bypass flags per route. Analytics show hit rate, latency saved, token spend avoided, and drift risk by provider. What changes for developers? Keep one integration, add cache headers, and watch fallback logic respect consent, residency, and audit needs. During rollout, canary keys compare fresh answers with cached copies before promotion. Prompt Cache cuts vendor calls, steadies p95 latency, and gives platform owners controls for cost, quality, and compliance.
輸出 1527 tok (+1354 思考) 延遲 37.9 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="gemini-3.8-flash",
# model="kimi-k3", # 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: "gemini-3.8-flash",
// model: "kimi-k3", // 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": "gemini-3.8-flash",
# "model": "kimi-k3", # 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: "gemini-3.8-flash",
// Model: "kimi-k3", // 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("gemini-3.8-flash")
// .model("kimi-k3") // 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
Gemini 3.8 Flash 和 Kimi K3 哪個比較便宜？
Gemini 3.8 Flash 在 輸入 / 1m tokens 上較便宜（$0.75 對比 $3，相差 4.0×）。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 Gemini 3.8 Flash 和 Kimi K3 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。
Gemini 3.8 Flash 與 Kimi K3 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。