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Claude Opus 5 vs Gemini 3.8 Flash

vs

何时用哪一个

claude-opus-5 在输入、输出和缓存读取上的成本比 gemini-3.8-flash 高约 6.7x(每百万 $5/$25/$0.5 对比 $0.75/$3.75/$0.075),它自带可关闭的思考功能以及 128000-token 的最大输出。gemini-3.8-flash 在文本和图像之外还支持音频和视频,具有稍大的 1048576-token 上下文,并将输出限制在 65536 token。如果文本和图像工作需要超长单次响应或显式的推理控制,请选择 claude-opus-5;如果是为了更便宜的高容量调用,或除了图像之外还需要任何多模态输入,请选择 gemini-3.8-flash。

Benchmark 成绩

领先高于同侪均值无人分数更高Claude Opus 5836 / 4310 / 43Gemini 3.8 Flash912 / 175 / 17

双方都被测过的 17 项。

Claude Opus 5 Gemini 3.8 Flash 其他被测模型 同侪均值 无人分数更高
DeepSWE 1.1
68.8%
73.7%
BioMysteryBench hard
49.4%
无人分数更高 56.5%
OSWorld 2.0 Partial score, batch tool enabled
无人分数更高 75.4%
59%
ExploitBench (Cap%)
70%
N/A
HealthBench Professional
59.8%
52.1%
Finance Agent v2
58.6%
无人分数更高 61.4%
Legal Agent Benchmark
6.7%
10%
GPQA Diamond
93.4%
95.3%
BrowseComp
90.8%
N/A
CharXiv (RQ) no tools
83.7%
无人分数更高 86.2%

厂商公布: Alibaba (Qwen) Anthropic ByteDance DeepSeek Google MiniMax Moonshot OpenAI Tencent Z.ai

定价

Claude Opus 5 Gemini 3.8 Flash Δ
输入 / 1M tokens $5 $0.75 6.7×
输出 / 1M tokens $25 $3.75 6.7×
缓存读取 / 1M tokens $0.5 $0.075 6.7×
缓存写入 1.25x (5m) / 2x (1h) - -

费率取自构建时的实时目录;每个模型页面均附有当前的费率卡。

它们的位置 — 以该计费单位计费的所有 71 个 聊天 模型的 每 1M token 的输入价格(对数刻度)

$0.05 · Qwen3 VL Flash $30 · GPT-5.4 Pro

能力

Claude Opus 5 Gemini 3.8 Flash
工具使用
思考控制 可配置 是 —— 厂商未公布调节参数
结构化输出
提示词缓存 显式(由您标记前缀) 隐式 + 显式
缓存生存时间 5m default, 1h option 未公开
最小缓存前缀 1024 个 token 4096 个 token

规格

Claude Opus 5 Gemini 3.8 Flash
输入模态 文本 图像 文本 图像 音频 视频
输出模态 文本 文本
发布日期 2026-07-24 2026-09-02
知识截止日期 2026-05 2026-03
上下文窗口 1M 1M
最大输出 128K 66K
思考参数
  • thinking.type
  • output_config.effort
-
允许的值
thinking.type
  • adaptive
  • disabled
effort
  • low
  • medium
  • high
  • xhigh
  • max
-
默认值

thinking on

effort
  • high (Claude API and Claude Code)
-

规格转录自各供应商的文档;若供应商未发布某项数据,则直接省略该行,而非进行推断。 完整来源: Claude Opus 5 · Gemini 3.8 Flash

单个 Prompt,两个模型 —— 通过网关实测

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

Claude Opus 5 通过 · 3 sentences

When reasoning is enabled, the model generates a hidden chain of "thinking" tokens before it writes the visible answer, and those internal tokens are billed at the output rate even though no one ever sees them — commonly 3–10x more tokens than the response itself. Because output tokens are priced several times higher than input tokens, that added volume hits us at our most expensive rate, which is why the bill roughly doubled while our request count stayed flat. The spend is controllable: we can cap the reasoning budget per call, route only genuinely hard requests to reasoning mode and cheaper models for the rest, and track cost per successfully completed task rather than cost per token, since a single correct answer usually beats three cheap retries plus human cleanup.

输出 529 tok 延迟 9.0 s

Gemini 3.8 Flash 通过 · 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.

输出 705 tok (+624 思考) 延迟 6.4 s

指令遵循(恰好三句,可数)、受众适配(面向 CFO 的语气),以及下方 token 计量所暴露的隐藏思考计费缺口。

提示词 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. 检查 修复通过测试

Claude Opus 5 通过 · 8/8 cases

## Fixed version ```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 if cur > best: best = cur return best ``` ## The bug The counters were tracking the wrong quantity: `cur` counted *matching adjacent pairs* rather than *elements in the current run*, and both counters started at `0` instead of `1`. Since a run of length *n* contains only *n − 1* adjacent equal pairs, every result came back one too small — `[1, 1]` returned 1, `[3, 3, 3]` returned 2 — and a single-element list returned 0 because the loop body never executed and `best` kept its initial value of `0`. […]

输出 1094 tok 延迟 16.1 s

Gemini 3.8 Flash 通过 · 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.

输出 1393 tok (+1149 思考) 延迟 10.1 s

修复是否真的正确(可运行)、解释的信息密度,以及在一个边界明确的任务上的 token 效率。

提示词 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. 检查 合法 JSON,schema 精确

Claude Opus 5 通过 · 5/5 fields, guidance "Withheld pending Q4 close"

```json { "company": "Meridian Robotics", "quarter": "Q3", "revenue_usd_m": 412, "growth_pct": 11, "guidance": "Withheld pending Q4 close" } ``` Notes on judgment calls: - **quarter**: "September quarter" is labeled Q3 because the text refers to an upcoming "Q4 close," implying a calendar-aligned fiscal year. No fiscal year is given. - **revenue_usd_m**: 412 is stated as constant-currency and hedged ("call it"), so it's approximate; the currency is not explicitly stated as USD. - **growth_pct**: "up eleven and change" means slightly more than 11, so 11 is a floor rather than an exact figure.

输出 539 tok 延迟 7.6 s

Gemini 3.8 Flash 通过 · 5/5 fields, guidance null

```json { "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": 412, "growth_pct": null, "guidance": null } ```

输出 3989 tok (+3932 思考) 延迟 30.4 s

schema 服从度(不臆造字段)、幻觉压力(guidance 明确被暂缓给出),以及结构化输出路径的差异。

提示词 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. 检查 120 词,0 个禁用词

Claude Opus 5 通过 · 123 words, 0 banned, 1 question

**Cross-Provider Prompt Caching** We're introducing Cross-Provider Prompt Caching in the API Gateway. Repeated system prompts, long documents, and few-shot examples are stored once at the gateway layer and reused across OpenAI, Anthropic, Google, and self-hosted models. Instead of paying full input token costs on every request, your application sends a cache reference, and the gateway rehydrates the context before forwarding it downstream. Why does that matter? Teams running high-volume agents and retrieval pipelines typically see input token spend fall 40 to 70 percent, with median latency dropping by several hundred milliseconds. Caches are scoped per project, encrypted at rest, and invalidated automatically when a prompt template changes. Enable it with a single header, and see the docs for TTL tuning and per-route controls.

输出 1593 tok 延迟 19.1 s

Gemini 3.8 Flash 通过 · 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.

输出 3833 tok (+3688 思考) 延迟 21.8 s

约束服从度(字数预算、禁用词表、唯一的那句问句)、文风指纹,以及长度控制。

只需一行代码即可在它们之间切换

两个 ID 都包含在下方的每个选项卡中 — 高亮显示的两行是唯一的修改。相同的端点,相同的密钥,相同的请求结构。

from openai import OpenAI

client = OpenAI(
    base_url="https://synthorai.io/v1",
    api_key="sk-syn-...",
)

resp = client.chat.completions.create(
    model="claude-opus-5",
    # model="gemini-3.8-flash",  # 取消注释此行,注释上一行
    messages=[{"role": "user", "content": "Summarize this diff"}],
    reasoning_effort="medium",
)
print(resp.choices[0].message.content)

获取 API 密钥 →

常见问题

Claude Opus 5 和 Gemini 3.8 Flash 哪个更便宜?

在 输入 / 1m tokens 方面,Gemini 3.8 Flash 更便宜($0.75 对比 $5,相差 6.7×)。其他行可能得出相反的结论——上方表格提供了完整信息,实际成本取决于你的组合使用情况。

我可以在不进行两次集成的情况下,对 Claude Opus 5 和 Gemini 3.8 Flash 进行 A/B 测试吗?

可以。两者均通过同一个兼容 OpenAI 的端点提供服务,并使用同一把 API 密钥——切换只需更改一行模型字符串,因此你可以将一部分流量路由到各个模型并直接比较账单。

Claude Opus 5 和 Gemini 3.8 Flash 支持提示词缓存吗?

是的 —— 两者对缓存读取的计费均低于其输入费率,因此热前缀工作负载的成本低于标价。准确的缓存读取行位于上方的定价表中。

相关对比

来自我们的实测研究