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Claude Haiku 5.5 vs Gemini 3.8 Flash

vs

什么时候选哪个

两者之中 claude-haiku-5-5 更便宜:输入 $0.1、输出 $0.5,gemini-3.8-flash 为 $0.75 和 $3.75,两项都便宜 7.5x,缓存读取为 $0.01 对 $0.075。上下文大致相同(1000000 和 1048576),claude-haiku-5-5 的回复可以更长,128000 token 对 65536。它对超过 100000 token 的提示词按 5x 计费,即便如此两项费率仍低于 gemini-3.8-flash。gemini-3.8-flash 接受音频和视频输入,claude-haiku-5-5 不接受。大批量的文本和图像工作选 claude-haiku-5-5,音频或视频选 gemini-3.8-flash。

Benchmark 成绩

Claude Haiku 5.5:供应商没有公布 benchmark 成绩。

高于同类均值无更高分Gemini 3.8 Flash12 / 175 / 17
Claude Haiku 5.5 Gemini 3.8 Flash 其他参测模型 同类均值 ★ 没有模型得分更高
DeepSWE 1.1
N/A
73.7%
BioMysteryBench hard
N/A
没有模型得分更高 56.5%
OSWorld 2.0 Partial score, batch tool enabled
N/A
59%
HealthBench Professional
N/A
52.1%
Finance Agent v2
N/A
没有模型得分更高 61.4%
Legal Agent Benchmark
N/A
10%
GPQA Diamond
N/A
95.3%
CharXiv (RQ) no tools
N/A
没有模型得分更高 86.2%

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

价格

Claude Haiku 5.5 Gemini 3.8 Flash Δ
输入 / 1M token $0.1 $0.75 0.13×
输出 / 1M token $0.5 $3.75 0.13×
缓存读取 / 1M token $0.01 $0.075 0.13×
缓存写入 1.25x (5m) / 2x (1h) - -

价格取自构建时的实时目录,最新价格见各模型页面。

两个模型所处的位置:全部 78 个按同一单位计费的聊天模型的每 1M token 输入价分布(对数刻度)

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

能力

Claude Haiku 5.5 Gemini 3.8 Flash
工具调用 是 是
思考控制 可配置 是,但供应商未公布调节参数
结构化输出 是 是
提示词缓存 显式(由你标记前缀) 隐式 + 显式
缓存有效期 5m default, 1h option 未公布
最小缓存前缀 1024 个 token 4096 个 token

规格

Claude Haiku 5.5 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 2026-10-07 2026-09-02
知识截止日期 2026-06 2026-03
上下文窗口 1M 1M
最大输出 128K 66K
思考参数 output_config.effort -
可选值
output_config.effort
  • low
  • medium
  • high
  • xhigh
  • max
-
默认值 medium -

规格照录自各供应商的文档;供应商没有公布的项目,对应的行直接省略,不做推断。 完整来源: Claude Haiku 5.5 · Gemini 3.8 Flash

同一条提示词,两个模型,经网关实测

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 Haiku 5.5 通过 · 3 sentences

Enabling reasoning makes the model generate a long internal chain of thought before its answer, and those thinking tokens are billed as output tokens, which cost several times more than input tokens. The model often produces many times more output per request than it did without reasoning, so the per-call cost rises even when the visible answer looks about the same length. Because that extra spend scales with every request, the bill roughly doubled, and we can control it by setting reasoning budgets, using reasoning only for tasks that need it, or routing simpler requests to a non-reasoning model.

输出 332 tok (+159 思考) 延迟 2.9 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,数得出来)、受众适配(对 CFO 说话的口吻),以及下方 token 计数暴露出的隐藏思考计费差额。

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 Haiku 5.5 通过 · 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 bug is that `cur` counts adjacent equal *pairs*, not run *elements*. A run of k equal items produces k−1 matching pairs, so the function returns one less than the true length, and a single-element input produces no pairs at all, so it returns 0 instead of 1. Resetting `cur` to 0 on a mismatch compounds this by starting every run one step behind. The fix starts both `cur` and `best` at 1 (a non-empty list always has a run of at least one element), resets `cur` to 1 on a mismatch, and updates `best` after every element rather than only inside the equal-branch. The empty-list guard keeps the function from returning 1 for `[]`. […]

输出 1012 tok (+591 思考) 延迟 6.0 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(能运行)、解释的信息密度,以及在一个范围明确的任务上的 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,严格符合 schema

Claude Haiku 5.5 通过 · 5/5 fields, guidance "Formal guidance withheld pending the Q4 "

```json { "company": "Meridian Robotics", "quarter": "September quarter", "revenue_usd_m": null, "growth_pct": 11, "guidance": "Formal guidance withheld pending the Q4 close" } ``` Notes: The text gives revenue as 412 but doesn't state the currency, so I left `revenue_usd_m` as null. Growth is "eleven and change," so 11 is an approximation.

输出 725 tok (+572 思考) 延迟 4.6 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

是否严守 schema(不臆造字段)、能否顶住幻觉压力(原文明说暂不给出 guidance),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 Haiku 5.5 通过 · 120 words, 0 banned, 1 question

**Introducing Prompt Cache Sync, the newest feature in GatewayOne.** Teams routing requests across OpenAI, Anthropic, and Google have often paid to process the same long system prompts again and again. Now the gateway stores those prompts once and reuses them wherever they appear, regardless of provider. Repeated context is matched by content hash, so cached prefixes return faster and cost less, with no changes to your application code. How much could this save your team? Enable Prompt Cache Sync from the dashboard, and your next billing report will show the difference. Cache entries follow your existing retention settings and can be purged per project at any time. Availability begins this week for all Scale and Enterprise plans worldwide. Start today.

输出 2164 tok (+1929 思考) 延迟 9.9 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

是否守住约束(字数预算、禁用词表、只能有一个问句)、文风特征,以及长度控制。

改一行代码就能在两个模型之间切换

下方每个标签页里都有两个模型 ID,高亮的那两行是唯一要改的地方。端点不变,API key 不变,请求结构也不变。

from openai import OpenAI

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

resp = client.chat.completions.create(
    model="claude-haiku-5-5",
    # 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 key →

常见问题

Claude Haiku 5.5 和 Gemini 3.8 Flash 哪个更便宜?

按「输入 / 1M token」算,Claude Haiku 5.5 更便宜($0.1 对 $0.75,相差 7.5×)。其他计费项的结论可能相反,完整价格见上方表格,实际成本取决于你的用量构成。

不用分别集成两次,就能对 Claude Haiku 5.5 和 Gemini 3.8 Flash 做 A/B 测试吗?

可以。两个模型走同一个 OpenAI 兼容端点,用同一个 API key,切换时只要改一行里的模型名,所以可以给两个模型各分一部分流量,直接对比账单。

Claude Haiku 5.5 和 Gemini 3.8 Flash 支持提示词缓存吗?

支持。两个模型的缓存读取价都低于各自的输入价,所以前缀能反复命中缓存的负载,实际成本会比按官网价估算的低。具体的缓存读取价见上方价格表。

相关对比

我们的实测研究