Claude Sonnet 5 vs Gemini 3.6 Flash
何時該用哪一個 — 綜合評斷,而非基準測試數據表
需要可關閉的明確思考與推理,加上最多 128,000 輸出 token 的長答案空間,並且能接受只有文字和影像輸入時,選 claude-sonnet-5,每百萬輸入 $2、輸出 $10。需要視訊和音訊輸入時選 gemini-3.6-flash,脈絡相近(約 1,048,576 token),輸入 $1.5、輸出 $7.5、快取讀取 $0.15,音訊按每百萬音訊 token $5 單獨計量;注意它的思考無法關閉,最大輸出為 65,536。粗略地說,Sonnet 5 在兩個方向上每 token 都貴約 1.33 倍。
定價
| Claude Sonnet 5 | Gemini 3.6 Flash | Δ | |
|---|---|---|---|
| 輸入 / 1M tokens | $2 | $1.5 | 1.3× |
| 輸出 / 1M tokens | $10 | $7.5 | 1.3× |
| 快取讀取 / 1M tokens | $0.2 | $0.15 | 1.3× |
| 快取寫入 | 1.25x (5m) / 2x (1h) | — | — |
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
| Claude Sonnet 5 | Gemini 3.6 Flash | |
|---|---|---|
| 工具使用 | 是 | 是 |
| 思考控制 | 可配置 | 常駐開啟 |
| 結構化輸出 | 是 | 是 |
| 提示快取 | 顯式(由您標記前綴) | 隱式 + 顯式 |
| 快取生命週期 | 5m default, 1h option | 未公開 |
| 最小快取前綴 | 1024 個 token | 4096 個 token |
規格
| Claude Sonnet 5 | Gemini 3.6 Flash | |
|---|---|---|
| 輸入模態 | 文字 影像 | 文字 影像 音訊 影片 |
| 輸出模態 | 文字 | 文字 |
| 發布日期 | 2026-06-30 | — |
| 知識截止日期 | 2026-01 | 2026-03 |
| 上下文視窗 | 1M | 1M |
| 最大輸出 | 128K | 66K |
| 思考參數 |
| thinkingLevel |
| 可接受的值 | thinking.type
effort
"enabled" returns 400 |
|
| 預設值 | thinking on (adaptive) effort
| 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: Claude Sonnet 5 · Gemini 3.6 Flash
單一提示詞,兩款模型 — 經由閘道測量
Claude Sonnet 5 通過 · 3 sentences
Reasoning mode works by having the model generate extended internal "thinking" steps before producing its final answer, and since we're billed on total tokens, that hidden reasoning text adds significantly to what we pay for—even though you never see it in the output. This is different from simply getting longer visible responses; the model is essentially "showing its work" behind the scenes, and complex prompts can trigger thousands of extra reasoning tokens per request. The upside is improved accuracy on complex tasks, but if we want to control costs, we should selectively enable reasoning only for queries that truly need it, rather than applying it universally.
輸出 194 tok 延遲 4.3 s
Gemini 3.6 Flash 通過 · 3 sentences
Enabling reasoning causes the model to generate thousands of internal "thinking" tokens behind the scenes to process logic before delivering the final response. Because our vendor bills us for every token generated—including these intermediate processing steps—our billable output volume per query surged significantly. Since output tokens carry a much higher unit cost than input tokens, this jump in processing volume directly doubled our overall spend.
輸出 893 tok (+816 思考) 延遲 4.7 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。
Claude Sonnet 5 通過 · 8/8 cases
## The Bug The function has two related off-by-one issues rooted in the same cause: `cur` and `best` are initialized to `0` instead of `1`, treating run lengths as the number of *matches* between adjacent elements rather than the number of *elements* in the run. This means a single-element input never enters the loop (since `range(1, 1)` is empty) and returns `0` instead of the correct answer of `1`. It also means that for any input, the run length is undercounted by exactly one — a run of 3 equal elements only produces 2 "match" increments, so `best` ends up as 2 instead of 3. The claim that it "misses runs that end at the last index" is actually a symptom of this same undercount: the logic does update `best` correctly on every iteration (including the last), but because it's counting matches instead of elements, the final reported value is always one less than the true longest run. […]
輸出 536 tok 延遲 7.1 s
Gemini 3.6 Flash 通過 · 8/8 cases
Here is the fixed function along with an explanation of the bugs: ```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 The original function initialized `cur` and `best` to `0` and incremented `cur` on matching adjacent items, which meant it was counting identical adjacent *pairs* rather than the total number of *elements* in a run. This caused single-element inputs to skip the loop and return `0` instead of `1`, and undercounted all run lengths by 1 (for example, a run of two items at the end of the list resulted in `cur = 1`). […]
輸出 2034 tok (+1768 思考) 延遲 10.0 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。
Claude Sonnet 5 通過 · 5/5 fields, guidance null
```json { "company": "Meridian Robotics", "quarter": "Q3", "revenue_usd_m": 412, "growth_pct": 11.5, "guidance": null } ```
輸出 348 tok 延遲 5.3 s
Gemini 3.6 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 2843 tok (+2783 思考) 延遲 13.1 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。
Claude Sonnet 5 通過 · 123 words, 0 banned, 1 question
## Introducing Cross-Provider Prompt Caching We're rolling out a new caching layer for our API gateway that works across every major LLM provider you connect through it. Instead of resending identical prompts and paying full price each time, the gateway recognizes repeated requests and serves cached responses in milliseconds—regardless of whether you're routing to OpenAI, Anthropic, or Google. What does this mean for your bill and your latency? Both drop, often significantly, especially for teams running high-volume, repetitive workloads like customer support bots or batch content generation. The cache is configurable per route, with adjustable TTLs and invalidation rules, so you stay in control of freshness versus cost. Available now for all Pro and Enterprise plans. Check your dashboard to enable it today.
輸出 259 tok 延遲 4.8 s
Gemini 3.6 Flash 未命中 · 60 words, 0 banned, 0 questions
72: reducing 73: your 74: monthly 75: token 76: spend. S6 (21): 77: You 78: can 79: easily 80: set 81: custom 82: expiration 83: rules, 84: configure 85: TTL 86: settings, 87: and 88: manage 89: cache 90: invalidation 91: across 92: all 93: vendors 94: from 95: one 96: centralized 97: dashboard. S7 (23): 98: Start 99: optimizing […]
輸出 4092 tok (+3929 思考) 延遲 16.3 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="claude-sonnet-5",
# model="gemini-3.6-flash", # 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: "claude-sonnet-5",
// model: "gemini-3.6-flash", // 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": "claude-sonnet-5",
# "model": "gemini-3.6-flash", # 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: "claude-sonnet-5",
// Model: "gemini-3.6-flash", // 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("claude-sonnet-5")
// .model("gemini-3.6-flash") // 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
Claude Sonnet 5 和 Gemini 3.6 Flash 哪個比較便宜?
Gemini 3.6 Flash 在 輸入 / 1m tokens 上較便宜($1.5 對比 $2,相差 1.3×)。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 Claude Sonnet 5 和 Gemini 3.6 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。
Claude Sonnet 5 與 Gemini 3.6 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。