Claude Haiku 5.5 vs DeepSeek V4.1 Flash (1017)
什么时候选哪个
两者的上下文窗口均为 1,000,000 个 token,都接受文本和图像输入并输出文本,因此主要区别在于价格和输出长度:claude-haiku-5-5 的每百万 token 输入和输出价格分别为 $0.1 和 $0.5,deepseek-v4.1-flash-1017 则为 $0.3 和 $1.2,分别是前者的 3 倍和 2.4 倍。对于大批量文本和图像任务,或者希望关闭思考、直接获取回答的场景,请选择 claude-haiku-5-5。如果单次回答必须超过 128000 个 token,请选择 deepseek-v4.1-flash-1017,因为它最多允许输出 393216 个 token;如果看重更低的缓存读取价格,也可以选择它,其价格为 $0.006。
价格
| Claude Haiku 5.5 | DeepSeek V4.1 Flash (1017) | Δ | |
|---|---|---|---|
| 输入 / 1M token | $0.1 | $0.3 | 0.33× |
| 输出 / 1M token | $0.5 | $1.2 | 0.42× |
| 缓存读取 / 1M token | $0.01 | $0.006 | 1.7× |
| 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 79 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 V4.1 Flash (1017) | |
|---|---|---|
| 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 | - |
| 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 V4.1 Flash (1017) | |
|---|---|---|
| 输入模态 | 文本 图像 | 文本 图像 |
| 输出模态 | 文本 | 文本 |
| 发布日期 | 2026-10-07 | 2026-10-17 |
| 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 | - |
| 可选值 | output_config.effort
| - |
| 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 V4.1 Flash (1017)
同一条提示词,两个模型,经网关实测
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 V4.1 Flash (1017) 通过 · 3 sentences
Enabling reasoning caused each request to generate additional internal reasoning tokens, which are billed like output tokens and can be several times longer than the final answer. Those extra tokens raise cost per call, and longer responses plus occasional retries pushed total usage to roughly double. We can control this with reasoning-effort limits, token budgets, caching, and spend monitoring by use case.
输出 253 tok (+178 思考) 延迟 5.4 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。
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 V4.1 Flash (1017) 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` starts at `0` and is incremented only when two adjacent items match, so it counts equal adjacent pairs rather than run length; a run of length `k` is recorded as `k - 1`. Consequently, a single-element input returns `0`, and a run ending at the last index—especially a one-element final run—is undercounted or never counted. The fix initializes every run to length `1`, increments on a match, resets to `1` on a mismatch, updates `best` each iteration, and handles the empty input separately.
输出 1300 tok (+1095 思考) 延迟 11.8 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。
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 V4.1 Flash (1017) 通过 · 5/5 fields, guidance "withheld pending the Q4 close"
{"company":"Meridian Robotics","quarter":"September quarter","revenue_usd_m":412,"growth_pct":11,"guidance":"withheld pending the Q4 close"}
输出 1375 tok (+1337 思考) 延迟 12.2 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。
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 V4.1 Flash (1017) 通过 · 120 words, 0 banned, 1 question
Today we’re introducing Cross-Provider Prompt Cache in the API gateway. It stores identical prompt requests once and serves cached responses across supported model providers, reducing duplicate calls, latency, and spend. Teams can set cache keys by model, temperature, system prompt, and custom metadata, then choose TTLs per route or workspace. The gateway handles provider fallback while keeping cached results consistent with your policy. Is your team paying twice for the same prompt? Cross-Provider Prompt Cache helps you answer that. Configure it in the gateway dashboard, inspect hit rates by provider, and invalidate entries on demand. Existing gateway routes need no code changes; enable the feature and define your caching rules. Cross-Provider Prompt Cache is available for all API gateway customers.
输出 2487 tok (+2335 思考) 延迟 17.1 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-v4.1-flash-1017", # 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)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-haiku-5-5",
// model: "deepseek-v4.1-flash-1017", // 取消注释此行,注释上一行
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-haiku-5-5",
# "model": "deepseek-v4.1-flash-1017", # 取消注释此行,注释上一行
"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-haiku-5-5",
// Model: "deepseek-v4.1-flash-1017", // 取消注释此行,注释上一行
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-haiku-5-5")
// .model("deepseek-v4.1-flash-1017") // 取消注释此行,注释上一行
.addUserMessage("Summarize this diff")
.reasoningEffort(ReasoningEffort.MEDIUM)
.build());
System.out.println(resp.choices().get(0).message().content().orElse(""));常见问题
Claude Haiku 5.5 和 DeepSeek V4.1 Flash (1017) 哪个更便宜?
按「输入 / 1M token」算,Claude Haiku 5.5 更便宜($0.1 对 $0.3,相差 3.0×)。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 V4.1 Flash (1017) 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 V4.1 Flash (1017) 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。