DeepSeek V4 Flash 对比 DeepSeek V4.1 Flash:价格与规格实测

Q: DeepSeek V4 Flash 和 DeepSeek V4.1 Flash 哪个更便宜？

在 输入 / 1m tokens 方面，DeepSeek V4 Flash 更便宜（$0.138 对比 $0.3，相差 2.2×）。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。

何时用哪一个

两者均支持 1,000,000 token 的上下文和 393,216 token 的最大输出，且均支持工具调用和推理，因此升级与否取决于输入类型和价格：deepseek-v4.1-flash 增加了图像输入，输入价格为 $0.3（约 2.2x），输出价格为 $1.2（约 4.4x），而仅支持文本的 deepseek-v4-flash 的输入和输出价格分别为 $0.138 和 $0.275，前者的缓存读取价格为 $0.03，后者为 $0.0028（约 10.7x）。对于关注更低费率和可以选择关闭思考功能的大发送量文本对话和代码任务，请继续使用 deepseek-v4-flash；当需要发送图像时，请迁移到 deepseek-v4.1-flash。

Benchmark 成绩

高于同侪均值无人分数更高DeepSeek V4 Flash仅 2 项可比DeepSeek V4.1 Flash16 ~~/ 19~~4 ~~/ 19~~

DeepSeek V4 Flash DeepSeek V4.1 Flash 其他被测模型同侪均值 ★ 无人分数更高

NL2Repo

N/A

64%

Cybergym

N/A

无人分数更高 88.1%

GPQA Diamond

N/A

90.9%

Agents' Last Exam

N/A

31.8%

BabyVision with tools

N/A

89.6%

GPQA Diamond

N/A

90.9%

Humanity's Last Exam no tools

N/A

36.8%

Humanity's Last Exam with tools

N/A

63.9%

Humanity's Last Exam no tools, text-only

N/A

39.1%

MathArena Apex

N/A

65.6%

GPQA Diamond max

88.1%

N/A

Apex Shortlist high

72.1%

N/A

Apex Shortlist max

85.7%

N/A

Chinese-SimpleQA high

73.2%

N/A

Chinese-SimpleQA max

78.9%

N/A

CorpusQA 1M high

59.3%

N/A

CorpusQA 1M max

60.5%

N/A

GPQA Diamond high

87.4%

N/A

HMMT Feb. 2026 high

91.9%

N/A

HMMT Feb. 2026 max

94.8%

N/A

Humanity's Last Exam no tools, high

29.4%

N/A

Humanity's Last Exam no tools, max

34.8%

N/A

Humanity's Last Exam with tools, high

40.3%

N/A

Humanity's Last Exam with tools, max

45.1%

N/A

IMOAnswerBench high

85.1%

N/A

IMOAnswerBench max

88.4%

N/A

MMLU-Pro high

86.4%

N/A

MMLU-Pro max

86.2%

N/A

MRCR 1M high

76.9%

N/A

MRCR 1M max

78.7%

N/A

SimpleQA-Verified high

28.9%

N/A

SimpleQA-Verified max

34.1%

N/A

Apex Shortlist non-think

9.3%

N/A

Chinese-SimpleQA non-think

71.5%

N/A

CorpusQA 1M non-think

15.5%

N/A

GPQA Diamond non-think

71.2%

N/A

HMMT Feb. 2026 non-think

40.8%

N/A

Humanity's Last Exam no tools, non-think

8.1%

N/A

IMOAnswerBench non-think

41.9%

N/A

MMLU-Pro non-think

83%

N/A

MRCR 1M non-think

37.5%

N/A

SimpleQA-Verified non-think

23.1%

N/A

NL2Repo

N/A

64%

Terminal-Bench 2.1

N/A

无人分数更高 90.6%

DeepSWE 1.1

N/A

无人分数更高 74.2%

Terminal-bench 3.0

N/A

30%

Terminal-bench 4.0

N/A

31.2%

ProgramBench Almost Solved

N/A

20.3%

SWE-Bench Pro max

52.6%

N/A

Codeforces (Elo) high · 2816-3052 据 DeepSeek 公布 · 2026-08-26

2816

N/A

Codeforces (Elo) max · 3289-3471 据 DeepSeek 公布 · 2026-09-14

N/A

3471

LiveCodeBench high

88.4%

N/A

LiveCodeBench max

91.6%

N/A

SWE-bench Multilingual max

73.3%

N/A

SWE-Bench Pro high

52.3%

N/A

SWE-bench Verified high

78.6%

N/A

SWE-bench Verified max

79%

N/A

Terminal Bench 2.0 high

56.6%

N/A

Terminal Bench 2.0 max

56.9%

N/A

LiveCodeBench non-think

55.2%

N/A

SWE-bench Multilingual non-think

69.7%

N/A

SWE-bench Multilingual high

70.2%

N/A

SWE-Bench Pro non-think

49.1%

N/A

SWE-bench Verified non-think

73.7%

N/A

Terminal Bench 2.0 non-think

49.1%

N/A

DeepSWE 1.1 Claude Code 据 DeepSeek 公布

没有其他模型的公开成绩

N/A

69.8%

DeepSWE 1.1 Codex 据 DeepSeek 公布

没有其他模型的公开成绩

N/A

65.6%

DeepSWE 1.1 DSH Minimal 据 DeepSeek 公布

没有其他模型的公开成绩

N/A

72.6%

DeepSWE 1.1 DSH PTC 据 DeepSeek 公布

没有其他模型的公开成绩

N/A

67.6%

DeepSWE 1.1 DSH Standard 据 DeepSeek 公布

没有其他模型的公开成绩

N/A

70.5%

DeepSWE 1.1 mini-SWE 据 DeepSeek 公布

没有其他模型的公开成绩

N/A

74.2%

DeepSWE 1.1 OpenCode 据 DeepSeek 公布

没有其他模型的公开成绩

N/A

65.5%

DeepSWE 1.1 Pi 据 DeepSeek 公布

没有其他模型的公开成绩

N/A

66.2%

Terminal-Bench 2.1 Claude Code 据 DeepSeek 公布

没有其他模型的公开成绩

N/A

88%

Terminal-Bench 2.1 Codex 据 DeepSeek 公布

没有其他模型的公开成绩

N/A

84.1%

Terminal-Bench 2.1 DSH Minimal 据 DeepSeek 公布

没有其他模型的公开成绩

N/A

90.6%

Terminal-Bench 2.1 DSH PTC 据 DeepSeek 公布

没有其他模型的公开成绩

N/A

85.8%

Terminal-Bench 2.1 DSH Standard 据 DeepSeek 公布

没有其他模型的公开成绩

N/A

85.8%

Terminal-Bench 2.1 mini-SWE 据 DeepSeek 公布

没有其他模型的公开成绩

N/A

90.3%

Terminal-Bench 2.1 OpenCode 据 DeepSeek 公布

没有其他模型的公开成绩

N/A

85%

Terminal-Bench 2.1 Pi 据 DeepSeek 公布

没有其他模型的公开成绩

N/A

86.1%

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

定价

	DeepSeek V4 Flash	DeepSeek V4.1 Flash	Δ
输入 / 1M tokens	$0.138	$0.3	0.46×
输出 / 1M tokens	$0.275	$1.2	0.23×
缓存读取 / 1M tokens	$0.0028	$0.03	0.093×
缓存写入	不单独收费	不单独收费	-

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 69 个聊天模型的每 1M token 的输入价格（对数刻度）

DeepSeek V4 Flash · $0.138 DeepSeek V4.1 Flash · $0.3

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

能力

	DeepSeek V4 Flash	DeepSeek V4.1 Flash
工具使用	是	是
思考控制	可配置	是 —— 厂商未公布调节参数
结构化输出	是	-
提示词缓存	隐式（自动）	隐式（自动）
缓存生存时间	no fixed TTL (evicted when unused)	no fixed TTL (evicted when unused)
最小缓存前缀	未公开	未公开

规格

	DeepSeek V4 Flash	DeepSeek V4.1 Flash
输入模态	文本	文本图像
输出模态	文本	文本
发布日期	2026-04-24	2026-09-10
上下文窗口	1M	1M
最大输出	393K	393K
思考参数	thinking.type reasoning_effort	-
允许的值	thinking.type enabled disabled reasoning_effort high max (low and medium map to high, xhigh maps to max)	-
默认值	enabled, with reasoning_effort high some complex agent requests are automatically set to max	-

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： DeepSeek V4 Flash · DeepSeek V4.1 Flash

单个 Prompt，两个模型 —— 通过网关实测

面向 CFO 的推理修复含 bug 的函数提取至 schema 按约束条件写作

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

DeepSeek V4 Flash 通过 · 3 sentences

When reasoning is enabled, the model generates additional "chain-of-thought" tokens internally before producing the final answer, effectively doubling the total token count per request. Since our billing is based on tokens processed, this directly doubled your usage. Additionally, reasoning often requires more expensive model tiers or higher compute resources, further amplifying the cost increase.

输出 154 tok (+85 思考) 延迟 3.1 s

DeepSeek V4.1 Flash 通过 · 3 sentences

Enabling reasoning changed the unit economics: the model now spends extra billable tokens internally to “think” before producing the answer, and those reasoning tokens are charged like output tokens even though users never see them. That added token generation—plus longer contexts and occasional retries on harder prompts—roughly doubled our cost per request. We can control this by capping reasoning depth, routing only complex queries to reasoning models, and caching common prompts to bring the bill back down.

输出 423 tok (+327 思考) 延迟 14.6 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

DeepSeek V4 Flash 通过 · 8/8 cases

The bug is that the function counts the number of consecutive equal **pairs** (i.e., `cur` increments for each `i` where `xs[i] == xs[i-1]`), but it should return the **length** of the longest run. For a run of length `L`, the number of pairs is `L-1`, so the result is always one less than the correct value. This causes the function to return 0 for a single‑element list (expected 1) and to undercount runs that end at the last index (e.g., `[1,1]` returns 1 instead of 2). Additionally, runs of length 1 that are not part of any pair (such as a single element at the end of a list) are never considered because `best` is only updated when a pair is found. […]

输出 2494 tok (+2227 思考) 延迟 19.4 s

DeepSeek V4.1 Flash 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` is counting equal adjacent pairs, not elements in the current run: every time `xs[i] == xs[i - 1]`, the run length increases by one element, but the first element of the run was never counted, so all runs are undercounted by one. Also, `best` starts at `0`, so single-element inputs return `0` instead of `1`, and a one-element run ending at the last index is missed entirely.

输出 918 tok (+733 思考) 延迟 11.4 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

DeepSeek V4 Flash 通过 · 5/5 fields, guidance null

{ "company": "Meridian Robotics", "quarter": "Q3", "revenue_usd_m": 412, "growth_pct": null, "guidance": null }

输出 1101 tok (+1054 思考) 延迟 10.2 s

DeepSeek V4.1 Flash 通过 · 5/5 fields, guidance "withheld pending the Q4 close"

{"company":"Meridian Robotics","quarter":"September quarter","revenue_usd_m":412,"growth_pct":11.0,"guidance":"withheld pending the Q4 close"}

输出 1707 tok (+1667 思考) 延迟 15.1 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

DeepSeek V4 Flash 通过 · 119 words, 0 banned, 1 question

We're thrilled to introduce Prompt Cache, a new feature for our API Gateway that stores prompt responses from any provider. When a user submits a prompt you've already processed, the system returns the cached result instantly—eliminating redundant API calls and reducing latency. This works across OpenAI, Anthropic, Google, and more, with automatic cache invalidation based on your rules. What does this mean for your budget? Fewer API calls directly lower your monthly spend. Additionally, response times drop by up to 80% for cached prompts, improving user experience. Developers can configure cache duration per provider, set TTLs, and bypass cache when needed. The feature is available now in your gateway dashboard. Start saving time and money with Prompt Cache.

输出 2039 tok (+1887 思考) 延迟 15.6 s

DeepSeek V4.1 Flash 通过 · 116 words, 0 banned, 1 question

Today we're launching Cross-Provider Prompt Cache for our API gateway. It stores identical prompt requests and their responses across supported model providers, then serves cached results when a match is found. Teams can cut duplicate inference costs, reduce latency, and keep behavior consistent during provider failover. The cache works with configurable TTLs, per-route rules, and cache-key controls, so you decide what is reusable and what must stay fresh. Does your application send the same prompts to multiple providers? Now your gateway can answer many of those calls without another upstream request. Existing observability dashboards show hit rates, saved tokens, and estimated spend reduction. Enable it in the gateway console, set your policy, and start caching today.

输出 917 tok (+770 思考) 延迟 9.0 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="deepseek-v4-flash",
    # model="deepseek-v4.1-flash",  # 取消注释此行，注释上一行
    messages=[{"role": "user", "content": "Summarize this diff"}],
)
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: "deepseek-v4-flash",
  // model: "deepseek-v4.1-flash",  // 取消注释此行，注释上一行
  messages: [{ role: "user", content: "Summarize this diff" }],
});
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": "deepseek-v4-flash",
    # "model": "deepseek-v4.1-flash",  # 取消注释此行，注释上一行
    "messages": [{"role": "user", "content": "Hello"}]
  }'

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: "deepseek-v4-flash",
		// Model: "deepseek-v4.1-flash",  // 取消注释此行，注释上一行
		Messages: []openai.ChatCompletionMessageParamUnion{
			openai.UserMessage("Summarize this diff"),
		},
	})
	fmt.Println(resp.Choices[0].Message.Content)
}

import com.openai.client.OpenAIClient;
import com.openai.client.okhttp.OpenAIOkHttpClient;
import com.openai.models.chat.completions.*;

OpenAIClient client = OpenAIOkHttpClient.builder()
    .baseUrl("https://synthorai.io/v1")
    .apiKey("sk-syn-...")
    .build();

ChatCompletion resp = client.chat().completions().create(
    ChatCompletionCreateParams.builder()
        .model("deepseek-v4-flash")
        // .model("deepseek-v4.1-flash")  // 取消注释此行，注释上一行
        .addUserMessage("Summarize this diff")
        .build());
System.out.println(resp.choices().get(0).message().content().orElse(""));

获取 API 密钥 →

常见问题

DeepSeek V4 Flash 和 DeepSeek V4.1 Flash 哪个更便宜？

在输入 / 1m tokens 方面，DeepSeek V4 Flash 更便宜（$0.138 对比 $0.3，相差 2.2×）。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 DeepSeek V4 Flash 和 DeepSeek V4.1 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。

DeepSeek V4 Flash 和 DeepSeek V4.1 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。

来自我们的实测研究

DeepSeek V4.1 Flash API 成本：准确率持平 V4 Pro，便宜 3-6 倍 LLM API 限流对比：13 家服务商，2 个 SDK 从不重试 429 DeepSeek V4 Flash API 成本：思考模式破坏严格 JSON

DeepSeek V4 Flash vs DeepSeek V4.1 Flash

Benchmark 成绩

定价

能力

规格

单个 Prompt，两个模型 —— 通过网关实测

只需一行代码即可在它们之间切换

常见问题

相关对比

来自我们的实测研究