Gemini 3.7 Flash vs Gemini 3.8 Flash
什麼情況選哪一個
根據已知事實，這兩者基本上可相互替換：gemini-3.7-flash 與 gemini-3.8-flash 皆收取每百萬輸入 token $0.75、輸出 $3.75 與快取讀取 $0.075 的費用，兩者皆具備 1048576 token 的上下文與 65536 最大輸出，且皆接受文字、圖片、音訊與影片輸入並產生文字輸出，外加對話、視覺、程式碼、工具與推理功能。唯一列出的差異在於世代與發布日期，為 2026-08-13 對比 2026-09-02，並具備相同的 2026-03 知識截止日期。針對新工作請預設使用較新的 gemini-3.8-flash，並僅在您需要針對已測試過的組建確保輸出穩定性時，才鎖定使用 gemini-3.7-flash。
Benchmark 成績
兩邊都有成績的有 14 項，其中 1 項平手。
供應商公布： Alibaba (Qwen) Anthropic ByteDance DeepSeek Google MiniMax Moonshot OpenAI Tencent Z.ai
定價
| Gemini 3.7 Flash | Gemini 3.8 Flash | Δ | |
|---|---|---|---|
| 輸入 / 1M tokens | $0.75 | $0.75 | = |
| 輸出 / 1M tokens | $3.75 | $3.75 | = |
| 快取讀取 / 1M tokens | $0.075 | $0.075 | = |
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 80 個聊天模型（對數尺度）
功能
| Gemini 3.7 Flash | Gemini 3.8 Flash | |
|---|---|---|
| 工具使用 | 是 | 是 |
| 思考控制 | 是，但供應商未公布調整參數 | 是，但供應商未公布調整參數 |
| 結構化輸出 | 是 | 是 |
| 提示詞快取 | 隱式 + 顯式 | 隱式 + 顯式 |
| 快取存活時間 | 未公布 | 未公布 |
| 最小快取前綴 | 4,096 個 token | 4,096 個 token |
規格
| Gemini 3.7 Flash | Gemini 3.8 Flash | |
|---|---|---|
| 輸入模態 | 文字 圖像 音訊 影片 | 文字 圖像 音訊 影片 |
| 輸出模態 | 文字 | 文字 |
| 發布日期 | 2026-08-13 | 2026-09-02 |
| 知識截止日期 | 2026-03 | 2026-03 |
| 上下文視窗 | 1M | 1M |
| 最大輸出 | 66K | 66K |
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： Gemini 3.7 Flash · Gemini 3.8 Flash
同一段提示詞，兩個模型，經閘道實測
Gemini 3.7 Flash 通過 · 3 sentences
Enabling reasoning causes the model to generate a high volume of invisible "thinking tokens" behind the scenes to deliberate and solve complex logic before delivering a final response. Because our vendor bills strictly on token volume, this additional internal computation effectively doubled our billable output per query. In short, our costs increased because we are now paying for the extra compute capacity required to produce significantly higher-accuracy answers.
輸出 878 tok (+799 思考) 延遲 6.9 s
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
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。
Gemini 3.7 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 else: cur = 1 best = max(best, cur) return best ``` ### Explanation of the Bug The original function tracked equal adjacent transitions instead of the actual number of elements in a run, causing all non-empty run lengths to be undercounted by one (e.g., returning `0` instead of `1` for single-element lists because the loop never executes, and `1` instead of `2` for pairs). Initializing both `best` and `cur` to `1` (after handling empty inputs) and resetting `cur` to `1` on a mismatch ensures every individual element is counted as a valid run of at least length one.
輸出 1050 tok (+827 思考) 延遲 6.4 s
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
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。
Gemini 3.7 Flash 通過 · 5/5 fields, guidance "withheld"
```json { "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": null, "guidance": "withheld" } ```
輸出 918 tok (+858 思考) 延遲 6.2 s
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
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。
Gemini 3.7 Flash 通過 · 120 words, 0 banned, 1 question
Why pay twice for identical AI queries simply because you routed them to different model vendors? Today, we introduce Universal Prompt Caching directly within our unified API gateway architecture. This capability stores repeated prompt contexts across OpenAI, Anthropic, and local models, instantly returning stored results to eliminate redundant computation fees. When your application sends an LLM request, the gateway inspects the payload, identifies semantic matches, and returns accurate cached responses in under ten milliseconds. Engineering teams can now slash inference latency by eighty percent while dramatically reducing monthly token expenditures across diverse production deployments. You retain complete privacy control, flexible cache eviction policies, and granular metrics through a single dashboard. Update your routing settings today to accelerate overall system performance.
輸出 2858 tok (+2718 思考) 延遲 14.1 s
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
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="gemini-3.7-flash",
# model="gemini-3.8-flash", # 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: "gemini-3.7-flash",
// model: "gemini-3.8-flash", // 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": "gemini-3.7-flash",
# "model": "gemini-3.8-flash", # 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: "gemini-3.7-flash",
// Model: "gemini-3.8-flash", // 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("gemini-3.7-flash")
// .model("gemini-3.8-flash") // 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
Gemini 3.7 Flash 和 Gemini 3.8 Flash 哪個比較便宜？
兩者的「輸入 / 1M tokens」相同（$0.75），這一項分不出高下，請看下方的規格與功能。
可以只串接一次，就對 Gemini 3.7 Flash 和 Gemini 3.8 Flash 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。
Gemini 3.7 Flash 與 Gemini 3.8 Flash 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。