Claude Sonnet 5.5 vs Gemini 3.7 Flash
何時用哪一個
兩款模型皆以每百萬 token 計費,且上下文長度極為相近(claude-sonnet-5-5 為 1000000,而 gemini-3.7-flash 為 1048576),因此真正的差異在於價格與輸入類型:Anthropic 的費率為輸入 $2、輸出 $10 以及快取讀取 $0.2,全面約為 Google $0.75、$3.75 與 $0.075 的 2.7x。當您需要在單次回應中獲得高達 128000 個輸出 token(即 gemini-3.7-flash 65536 上限的兩倍)時,請選擇 claude-sonnet-5-5。若需更便宜的費率表以及音訊或視訊輸入(僅支援文字與圖片的 Anthropic 模型無法接受此類輸入),請選擇 gemini-3.7-flash。
Benchmark 成績
Claude Sonnet 5.5:廠商沒有公布過 benchmark 成績。
廠商公布: Alibaba (Qwen) Anthropic DeepSeek Google Moonshot OpenAI Tencent Z.ai
定價
| Claude Sonnet 5.5 | Gemini 3.7 Flash | Δ | |
|---|---|---|---|
| 輸入 / 1M tokens | $2 | $0.75 | 2.7× |
| 輸出 / 1M tokens | $10 | $3.75 | 2.7× |
| 快取讀取 / 1M tokens | $0.2 | $0.075 | 2.7× |
| 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 76 個 聊天 模型的 每 1M tokens 的輸入價格(對數尺度)
能力
| Claude Sonnet 5.5 | 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 | 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 5.5 | Gemini 3.7 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 | 2026-09-28 | 2026-08-13 |
| 知識截止日期 | 2026-06 | 2026-03 |
| 上下文視窗 | 1M | 1M |
| 最大輸出 | 128K | 66K |
| 思考參數 | thinking.type | - |
| 可接受的值 | thinking.type
| - |
| 預設值 | adaptive, effort high | - |
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 5.5 · Gemini 3.7 Flash
單一提示詞,兩款模型 — 經由閘道測量
Claude Sonnet 5.5 通過 · 3 sentences
Enabling reasoning makes the model "think" before it answers, and that internal thinking is generated as extra text (reasoning tokens) that we are billed for at the same rate as output tokens, even though users never see it. Output tokens also cost several times more than input tokens, so a single request that used to produce a short answer can now generate many times more billable text behind the scenes. Because our request volume and prompts didn't change, the doubling comes almost entirely from this hidden thinking, and we can bring it down by limiting reasoning effort, turning it off for simple tasks, and reserving it for the workloads where the accuracy gain justifies the cost.
輸出 205 tok 延遲 3.6 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。
Claude Sonnet 5.5 通過 · 8/8 cases
**The bug:** `cur` counts the number of *adjacent equal pairs* rather than the number of items in the run. A run of length k contains only k−1 such pairs, so every result is one too small. The counter also starts at 0 and the loop starts at index 1, so a single-element list never updates `best` and returns 0 instead of 1. (An empty list should return 0.) In addition, `cur` resets to 0 on a mismatch, when it should reset to 1, because the item at `xs[i]` begins a new run of length 1. The claim that it "misses runs ending at the last index" is really the same off-by-one: the final run is counted, but as one less than its true length. Because `best` is updated inside the loop on every extension, the last run isn't skipped. […]
輸出 444 tok 延遲 6.7 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。
Claude Sonnet 5.5 通過 · 5/5 fields, guidance null
Here is the extracted JSON: ```json { "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": 11, "guidance": null } ``` Notes on the choices: - **quarter**: The text says "September quarter" without specifying a fiscal year or Q-number, so I kept the wording as stated. - **revenue_usd_m**: The text gives "412" for the topline, with no currency or unit stated explicitly. I've taken it as 412 million USD per the field definition, but the text does not confirm that, and the figure is constant-currency. - **growth_pct**: "Eleven and change" means slightly above 11, so 11 is an approximation. The exact figure isn't given. - **guidance**: Guidance is withheld pending the Q4 close, so no guidance is stated, hence null.
輸出 300 tok 延遲 3.5 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。
Claude Sonnet 5.5 通過 · 122 words, 0 banned, 1 question
**Introducing Cross-Provider Prompt Caching** Your API gateway now caches prompts across every connected LLM provider, whether you route traffic to OpenAI, Anthropic, Google, or a self-hosted model. Here's how it works: the gateway fingerprints each prompt prefix, stores it centrally, and applies the right provider-specific caching mechanism automatically. When you switch providers or fail over during an outage, your cached context follows you. Early testing shows input token costs dropping by up to 70% on repeated system prompts and long documents, with latency improvements of 40% or more on cache hits. Tired of paying to send the same instructions thousands of times a day? Enable it with one configuration flag. Cache rules, TTLs, and per-team isolation are available in the dashboard today.
輸出 266 tok 延遲 3.8 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="claude-sonnet-5-5",
# model="gemini-3.7-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-5-5",
// model: "gemini-3.7-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-5-5",
# "model": "gemini-3.7-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-5-5",
// 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"),
},
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-5-5")
// .model("gemini-3.7-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 5.5 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 $2,相差 2.7×)。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 5.5 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。
Claude Sonnet 5.5 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。