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Claude Haiku 5.5 vs DeepSeek V4 Pro (0813)

vs

什么时候选哪个

claude-haiku-5-5 输入 $0.1、输出 $0.5,deepseek-v4-pro-0813 为 $1.32 和 $3.96,输入便宜约 13x、输出便宜约 8x,缓存读取为 $0.01 对 $0.132。两者的上下文都是 1000000 token。claude-haiku-5-5 接受图像输入,对超过 100000 token 的提示词按 5x 计费,即便如此仍然更便宜;deepseek-v4-pro-0813 仅支持文本,最多可输出 393216 token,对方为 128000。大批量工作和图像输入选 claude-haiku-5-5,超长的文本输出选 deepseek-v4-pro-0813。

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DeepSeek V4 Pro (0813)13 / 211 / 21
Claude Haiku 5.5 DeepSeek V4 Pro (0813) 其他参测模型 同类均值 ★ 没有模型得分更高
DeepSWE 1.1
N/A
62.7%
Cybergym
N/A
83.3%
GDPval-AA v2 Elo · 1508-1769 据 Z.ai 公布 · 2026-09-04
N/A
1590
GPQA Diamond
N/A
92.4%
Agents' Last Exam
N/A
25.7%

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

价格

Claude Haiku 5.5 DeepSeek V4 Pro (0813) Δ
输入 / 1M token $0.1 $1.32 0.076×
输出 / 1M token $0.5 $3.96 0.13×
缓存读取 / 1M token $0.01 $0.132 0.076×
缓存写入 1.25x (5m) / 2x (1h) 不单独收费 -

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)

能力

Claude Haiku 5.5 DeepSeek V4 Pro (0813)
工具调用 是 是
思考控制 可配置 始终开启
结构化输出 是 是
提示词缓存 显式(由你标记前缀) 隐式(自动)
缓存有效期 5m default, 1h option no fixed TTL (evicted when unused)
最小缓存前缀 1024 个 token 未公布

规格

Claude Haiku 5.5 DeepSeek V4 Pro (0813)
输入模态 文本 图像 文本
输出模态 文本 文本
发布日期 2026-10-07 2026-08-13
知识截止日期 2026-06 -
上下文窗口 1M 1M
最大输出 128K 393K
思考参数 output_config.effort reasoning_effort
可选值
output_config.effort
  • low
  • medium
  • high
  • xhigh
  • max
reasoning_effort
  • the model card documents low
  • high
  • 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 · DeepSeek V4 Pro (0813)

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

DeepSeek V4 Pro (0813) 通过 · 3 sentences

Enabling reasoning causes the model to generate a hidden chain-of-thought with many additional tokens before producing the final answer, which sharply increases compute consumption per request. Those extra reasoning tokens are billed at the same or higher rates, so total usage doubled even though the volume of final outputs stayed similar. We can control this by adjusting reasoning effort, setting token limits, or routing only complex requests through reasoning mode.

输出 308 tok (+226 思考) 延迟 5.1 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

DeepSeek V4 Pro (0813) 通过 · 8/8 cases

```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 ``` The bug is that `cur` was initialized to `0` and only counted adjacent matching pairs, so a run of length `n` produced `n - 1` (or `0` for a single element) instead of `n`. The code also reset to `0` on a change, when the new run should start at length `1`. Initializing `cur` and `best` to `1` fixes both the single-element case and runs ending at the last index.

输出 1315 tok (+1130 思考) 延迟 16.9 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

DeepSeek V4 Pro (0813) 通过 · 5/5 fields, guidance null

{"company":"Meridian Robotics","quarter":"September quarter","revenue_usd_m":412,"growth_pct":11.0,"guidance":null}

输出 2155 tok (+2121 思考) 延迟 26.0 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

DeepSeek V4 Pro (0813) 通过 · 121 words, 0 banned, 1 question

Introducing cross-provider prompt caching in our API gateway. Send a prompt once, and the gateway stores its computed prefix across Anthropic, OpenAI, and other LLM providers. Subsequent requests with the same prompt hit the cache, cutting latency and token costs while keeping outputs consistent across routing decisions and provider failovers. Teams can route identical prompts between providers without reprocessing shared context or lengthy system instructions. How much could you save on repeated prompt prefixes? The cache respects provider-specific key formats, handles TTLs automatically, and works with streaming and batch requests. Enable it with one configuration flag—no changes to your application code. Available today on all plans. Monitor cache hit rates, token savings, and provider-specific performance metrics in the live dashboard.

输出 2845 tok (+2694 思考) 延迟 25.5 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="deepseek-v4-pro-0813",  # 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 DeepSeek V4 Pro (0813) 哪个更便宜?

按「输入 / 1M token」算,Claude Haiku 5.5 更便宜($0.1 对 $1.32,相差 13×)。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 DeepSeek V4 Pro (0813) 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 DeepSeek V4 Pro (0813) 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

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