DeepSeek
DeepSeek V4.1 Flash开源
DeepSeek V4.1 Flash is an open-weights MoE reasoning model with 552B total (16B active) parameters, 1.0M context, and chain-of-thought problem solving.
智商指数
54.5 #379
AAQI 40.0
输出速度
214 t/s
TTFT 1.21 s · ITL 4.7 ms/tok
混合价格 (3:1)
$0.18 /1M
输入 $0.3/M · 输出 $1.2/M
上下文
1.0M
≈ 2,500 页 A4 · 最大输出 32,768
Quality Domain Radar
6-Axis Frontier Evaluation vs Category Average & Flagship
DeepSeek V4.1 Flash
Claude Mythos Preview (#1)
Average (68)
缺少直接基准测量时,雷达图各轴由类别得分近似得出 — 详见 评分方法。
优势与取舍
优势所在
- Open weights — self-host, fine-tune and audit with no vendor lock-in
- 1.0M context — swallows whole repos, codebases or corpora in one pass
- 214 tok/s output — fast enough for real-time agents
- Very low blended price — $0.18/1M mixed tokens
杀手级用例
Deep research
Synthesis across broad knowledge domains with citation-grade recall.
Math & proof work
Step-verified reasoning for olympiad math, theorem proving and formal verification.
SWE-bench agents
Issue-to-PR resolution across multi-file repos — refactor, test and verify autonomously.
开发者快速上手与 API
请先设置
DEEPSEEK_API_KEY — 获取 API 密钥curl https://api.deepseek.com/v1/chat/completions \
-H "Authorization: Bearer $DEEPSEEK_API_KEY" \
-H "content-type: application/json" \
-d '{
"model": "deepseek-v4-1-flash",
"messages": [{"role": "user", "content": "Refactor this function for O(n) time and explain the change."}]
}'import os
from openai import OpenAI
client = OpenAI(
base_url="https://api.deepseek.com/v1",
api_key=os.environ["DEEPSEEK_API_KEY"],
)
resp = client.chat.completions.create(
model="deepseek-v4-1-flash",
messages=[{"role": "user", "content": "Refactor this function for O(n) time and explain the change."}],
)
print(resp.choices[0].message.content)const res = await fetch("https://api.deepseek.com/v1/chat/completions", {
method: "POST",
headers: {
Authorization: `Bearer ${process.env.DEEPSEEK_API_KEY}`,
"content-type": "application/json",
},
body: JSON.stringify({
model: "deepseek-v4-1-flash",
messages: [{ role: "user", content: "Refactor this function for O(n) time and explain the change." }],
}),
});
console.log((await res.json()).choices[0].message.content);官方资源与链接
API 部署
对比托管商与延迟
Groq、Cerebras、Together、DeepInfra、Fireworks、Bedrock 与 Azure 的吞吐量、TTFT 和缓存定价。
GroqCerebrasTogether AIDeepInfraFireworks AIAWS BedrockAzure AI
各榜单得分
分类能力得分
基准测试分项
实时规格与架构
底层解析
Dense · 552B (16B active)
- Switchable reasoning effort — trading latency and tokens for accuracy on hard problems.
- Prompt caching supported — cached input reads at $0.03/1M tokens.
发布日期
2026年9月10日
架构
Dense · 552B (16B active)
参数量
552B (16B active)
开源协议
Open Weights
上下文窗口
1,000,000 tokens
最大输出
32,768 tokens
模态
text + image
缓存价格
读 $0.03 · 写 $0.35 /1M
推理
思考力度档位
输入价格
$0.3/M
输出价格
$1.2/M
输出速度
214 t/s
首Token延迟
1.21 s
数据更新
2026年9月12日
每月账单预估
每月预估支出
$0.00
正在计算相对 GPT-4o 的节省额…
引用与嵌入
GitHub Markdown 徽章
Loading badge markdown...BibTeX
@misc{llmpodium2026deepseekv41flash,
title = {Benchmark Analysis and Intelligence Evaluation of DeepSeek V4.1 Flash},
author = {LLMPodium Research},
year = {2026},
url = {https://llmpodium.com/models/deepseek-v4-1-flash}
}嵌入评分徽章
将实时 Podium 评分徽章添加到 GitHub 或 Hugging Face README —— 链接回实时排名。
预览
Markdown
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替代模型推荐
基于能力、定价和基准测试得分与 DeepSeek V4.1 Flash 最接近的替代模型。