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Claude Sonnet 5.5 vs Gemini 3.7 Flash

vs

什麼情況選哪一個

兩款都按每百萬 token 計費,上下文幾乎一樣(claude-sonnet-5-5 為 1000000,gemini-3.7-flash 為 1048576),真正的差別在價格與輸入類型:Anthropic 的輸入 $2、輸出 $10 約是 Google $0.75 與 $3.75 的 2.7x,快取讀取則較接近,$0.1 對 $0.075。單次回應需要最多 128000 個輸出 token 時選 claude-sonnet-5-5,是 gemini-3.7-flash 65536 上限的兩倍。想要更便宜的價目表,或需要音訊或影片輸入(僅支援文字與圖片的 Anthropic 模型無法接受)時選 gemini-3.7-flash。

Benchmark 成績

Claude Sonnet 5.5:供應商沒有公布 benchmark 成績。

高於平均沒有模型更高Gemini 3.7 Flash17 / 243 / 24
Claude Sonnet 5.5 Gemini 3.7 Flash 其他有成績的模型 其他模型平均 ★ 沒有模型分數更高
DeepSWE 1.1
N/A
65.3%
BioMysteryBench hard
N/A
43.5%
OSWorld 2.0
N/A
47.9%
Finance Agent v2
N/A
59%
Harvey Lab-AA
N/A
90.7%
HLE-Verified
N/A
53.6%
AutomationBench (v1.0.6)
N/A
52.3%
LVBench
N/A
沒有模型分數更高 85.4%

供應商公布: 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 $0.1 $0.075 1.3×
快取寫入 1.25x (5m) / 2x (1h) - -

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 77 個聊天模型(對數尺度)

功能

Claude Sonnet 5.5 Gemini 3.7 Flash
工具使用 是 是
思考控制 一律開啟 是,但供應商未公布調整參數
結構化輸出 是 是
提示詞快取 顯式(由你標記前綴) 隱式 + 顯式
快取存活時間 5m default, 1h option 未公布
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 圖像 文字 圖像 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
  • adaptive (default)
  • between_tools
-
預設值 adaptive, effort high -

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: Claude Sonnet 5.5 · Gemini 3.7 Flash

同一段提示詞,兩個模型,經閘道實測

提示詞 Explain to a CFO, in exactly three sentences, why our LLM bill doubled after we enabled reasoning. 檢查 恰好 3 句

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

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。

提示詞 This function is supposed to return the longest run of consecutive equal items, but callers report it is off by one on single-element inputs and misses runs that end at the last index. Fix it and explain the bug in one paragraph. 檢查 修正後通過測試

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。

提示詞 Extract a JSON object with fields {company, quarter, revenue_usd_m, growth_pct, guidance} from this text. Use null for anything not stated; add no other fields. 檢查 有效的 JSON,完全符合 schema

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

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。

提示詞 Write a 120-word product announcement for an API gateway feature that caches prompts across providers. Forbidden words: "seamless", "unlock", "game-changer", "revolutionize", "empower". Exactly one sentence must be a question. 檢查 120 詞,0 個禁用詞

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

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="claude-sonnet-5-5",
    # model="gemini-3.7-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)

取得 API 金鑰 →

常見問題

Claude Sonnet 5.5 和 Gemini 3.7 Flash 哪個比較便宜?

以「輸入 / 1M tokens」來看,Gemini 3.7 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 Claude Sonnet 5.5 和 Gemini 3.7 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。

Claude Sonnet 5.5 與 Gemini 3.7 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。

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