DeepSeek

DeepSeek V4.1 Flash오픈소스

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.

#317출시일: 2026년 9월 10일·Open Weights·552B (16B active)
textimage1.0M ctxreasoning
API providers순위표
Intelligence
54.5 #317
AAQI 40.0
Output speed
214.4 t/s
TTFT 1.21 s · ITL 4.7 ms/tok
Blended price (3:1)
$0.18 /1M
In $0.3/M · Out $1.2/M
Context
1.0M
≈ 2,500 A4 pages · max out 32768

Quality Domain Radar

6-Axis Frontier Evaluation vs Category Average & Flagship

DeepSeek V4.1 Flash
Claude Mythos Preview (#1)
Average (68)
Coding (52)Mathematics (68)Hard Science (GPQA) (58)Agentic & Tool Use (48)Knowledge (MMLU) (46)Vision / Multimodal (50)

Radar axes are approximated from category scores where direct benchmark measurements are missing — see 평가 방법.

API deployment

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Throughput, TTFT and cache pricing across Groq, Cerebras, Together, DeepInfra, Fireworks, Bedrock and Azure.

GroqCerebrasTogether AIDeepInfraFireworks AIAWS BedrockAzure AI
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출처별 점수

모델 상세 정보

출시일
2026년 9월 10일
파라미터 수
552B (16B active)
라이선스
Open Weights
컨텍스트 창
1,000,000 tokens
입력 가격
$0.3/M
출력 가격
$1.2/M
출력 속도
214 t/s
첫 토큰 생성 시간
1.21 s

Estimated monthly bill

Estimated monthly spend
$0.00
Calculating savings vs GPT-4o...

Cite & embed

GitHub Markdown badge
[![LLMPodium Rank](https://llmpodium.com/badge/deepseek-v4-1-flash/rank.svg)](https://llmpodium.com/models/deepseek-v4-1-flash)
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}
}

More from DeepSeek

대체 모델

성능, 가격 및 벤치마크 점수를 기반으로 한 DeepSeek V4.1 Flash의 주요 대체 모델.

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