Gemini 3.8 Flash vs GPT-6 Sol
什麼情況選哪一個
兩者涵蓋的上下文視窗大小大致相同（gemini-3.8-flash 為 1048576，gpt-6-sol 為 1050000），並具備聊天、視覺、程式碼、工具與推理功能，因此差異在於價格與 I/O 形態：gpt-6-sol 在輸入（$2 對比 $0.75）、輸出（$10 對比 $3.75）與快取讀取（$0.2 對比 $0.075）的成本大約高出 2.7x。當需要較便宜的大批量工作，或是需要 gpt-6-sol 不接受的音訊與視訊輸入時，請選擇 gemini-3.8-flash。當您需要單次回覆達到 128000 個 token 而非 65536 個，或者需要停用思考的選項時，請選擇 gpt-6-sol。
Benchmark 成績
供應商公布： Alibaba (Qwen) Anthropic ByteDance DeepSeek Google MiniMax Moonshot OpenAI Tencent Z.ai
定價
| Gemini 3.8 Flash | GPT-6 Sol | Δ | |
|---|---|---|---|
| 輸入 / 1M tokens | $0.75 | $2 | 0.38× |
| 輸出 / 1M tokens | $3.75 | $10 | 0.38× |
| 快取讀取 / 1M tokens | $0.075 | $0.2 | 0.37× |
| 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
| Gemini 3.8 Flash | GPT-6 Sol | |
|---|---|---|
| 工具使用 | 是 | 是 |
| 思考控制 | 是，但供應商未公布調整參數 | 可設定 |
| 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 | at least 30m by default, 12h option on the Responses API |
| 最小快取前綴 | 4,096 個 token | 1,024 個 token |
規格
| Gemini 3.8 Flash | GPT-6 Sol | |
|---|---|---|
| 輸入模態 | 文字 圖像 音訊 影片 | 文字 圖像 |
| 輸出模態 | 文字 | 文字 |
| 發布日期 | 2026-09-02 | 2026-09-22 |
| 知識截止日期 | 2026-03 | 2026-04 |
| 上下文視窗 | 1M | 1.1M |
| 最大輸出 | 66K | 128K |
| 思考參數 | - | reasoning.effort |
| 可接受的值 | - | reasoning.effort
|
| 預設值 | - | medium |
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 3.8 Flash · GPT-6 Sol
同一段提示詞，兩個模型，經閘道實測
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
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
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 3.8 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
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
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 3.8 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
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
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 3.8 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
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
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-3.8-flash",
# model="gpt-6-sol", # 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-3.8-flash",
// model: "gpt-6-sol", // 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-3.8-flash",
# "model": "gpt-6-sol", # 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-3.8-flash",
// Model: "gpt-6-sol", // 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-3.8-flash")
// .model("gpt-6-sol") // 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 3.8 Flash 和 GPT-6 Sol 哪個比較便宜？
以「輸入 / 1M tokens」來看，Gemini 3.8 Flash 比較便宜（$0.75 對 $2，相差 2.7×）。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 Gemini 3.8 Flash 和 GPT-6 Sol 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 3.8 Flash 與 GPT-6 Sol 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。