Gemini 3.5 Flash vs Gemini 3.7 Flash
何時該用哪一個 — 綜合評斷，而非基準測試數據表
這兩款 Google Flash 模型在基本規格上一致 — 1048576 個 token 的上下文、65536 的最大輸出，以及支援文字、圖片、影片與音訊輸入加上文字輸出 — 因此差異在於價格與生成：gemini-3.7-flash（於 2026-08-13 發布）收費為輸入 $0.75 且輸出 $3.75，對比 gemini-3.5-flash 的 $1.5 與 $9，這使得前者的輸入便宜 2x，輸出便宜 2.4x，且每百萬音訊輸入的 $0.75 對比 $5 大約便宜 6.7x。它還帶有推論能力標籤。只有在你已經鎖定於 gemini-3.5-flash 的情況下才選擇它；否則 gemini-3.7-flash 能以更低的價格涵蓋相同的規格。
定價
| Gemini 3.5 Flash | Gemini 3.7 Flash | Δ | |
|---|---|---|---|
| 輸入 / 1M tokens | $1.5 | $0.75 | 2× |
| 輸出 / 1M tokens | $9 | $3.75 | 2.4× |
| 快取讀取 / 1M tokens | $0.15 | $0.075 | 2× |
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
| Gemini 3.5 Flash | Gemini 3.7 Flash | |
|---|---|---|
| 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 | 是 |
| 提示快取 | 隱式 + 顯式 | 隱式 + 顯式 |
| 快取生命週期 | 未公開 | 未公開 |
| 最小快取前綴 | 4096 個 token | 4096 個 token |
規格
| Gemini 3.5 Flash | Gemini 3.7 Flash | |
|---|---|---|
| 輸入模態 | 文字 影像 音訊 影片 | 文字 影像 音訊 影片 |
| 輸出模態 | 文字 | 文字 |
| 發布日期 | 2026-05-19 | 2026-08-13 |
| 知識截止日期 | 2025-01 | — |
| 上下文視窗 | 1M | 1M |
| 最大輸出 | 66K | 66K |
| 思考參數 | thinkingLevel | — |
| 可接受的值 |
| — |
| 預設值 | medium | — |
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 3.5 Flash · Gemini 3.7 Flash
單一提示詞，兩款模型 — 經由閘道測量
Gemini 3.5 Flash 通過 · 3 sentences
Reasoning models do not just generate a final answer; they perform a silent, multi-step "thinking" process beforehand to ensure higher accuracy. We are billed for every single one of these invisible reasoning steps (called "thinking tokens"), which drastically increases the computational volume per query. Consequently, while our user activity remained flat, our unit costs doubled because the AI is now doing significantly more backend processing to deliver these higher-quality results.
輸出 1152 tok (+1065 思考) 延遲 7.7 s
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
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 3.5 Flash 通過 · 8/8 cases
Here is the corrected version of the 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 code initialized `best` and `cur` to `0` instead of `1` (the minimum run length for any non-empty list), which caused it to count the number of adjacent duplicate *transitions* rather than the actual number of *elements* in a run. This resulted in an off-by-one error for all runs and caused single-element lists to incorrectly return `0`. […]
輸出 2386 tok (+2110 思考) 延遲 12.7 s
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
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 3.5 Flash 通過 · 5/5 fields, guidance "withheld pending the Q4 close"
```json { "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": null, "guidance": "withheld pending the Q4 close" } ```
輸出 1950 tok (+1885 思考) 延遲 12.7 s
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
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 3.5 Flash 未命中 · 50 words, 0 banned, 0 questions
thought 51: OpenAI, 52: Anthropic, 53: and 54: Cohere. 55: When 56: a 57: client 58: submits 59: a 60: matching 61: request, 62: the 63: gateway 64: retrieves 65: the 66: cached 67: result 68: instantly, 69: slashing 70: latency 71: and 72: reducing 73: your 74: monthly 75: […]
輸出 4092 tok (+3933 思考) 延遲 19.1 s
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
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-3.5-flash",
# model="gemini-3.7-flash", # 取消註解此行，並註解上一行
messages=[{"role": "user", "content": "Summarize this diff"}],
)
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-3.5-flash",
// model: "gemini-3.7-flash", // 取消註解此行，並註解上一行
messages: [{ role: "user", content: "Summarize this diff" }],
});
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-3.5-flash",
# "model": "gemini-3.7-flash", # 取消註解此行，並註解上一行
"messages": [{"role": "user", "content": "Hello"}]
}'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-3.5-flash",
// Model: "gemini-3.7-flash", // 取消註解此行，並註解上一行
Messages: []openai.ChatCompletionMessageParamUnion{
openai.UserMessage("Summarize this diff"),
},
})
fmt.Println(resp.Choices[0].Message.Content)
}import com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;
import com.openai.models.chat.completions.*;
OpenAIClient client = OpenAIOkHttpClient.builder()
.baseUrl("https://synthorai.io/v1")
.apiKey("sk-syn-...")
.build();
ChatCompletion resp = client.chat().completions().create(
ChatCompletionCreateParams.builder()
.model("gemini-3.5-flash")
// .model("gemini-3.7-flash") // 取消註解此行，並註解上一行
.addUserMessage("Summarize this diff")
.build());
System.out.println(resp.choices().get(0).message().content().orElse(""));常見問題
Gemini 3.5 Flash 和 Gemini 3.7 Flash 哪個比較便宜？
Gemini 3.7 Flash 在 輸入 / 1m tokens 上較便宜（$0.75 對比 $1.5，相差 2.0×）。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 3.5 Flash 和 Gemini 3.7 Flash 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 3.5 Flash 與 Gemini 3.7 Flash 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。