GetInfoUs
Head-to-Head Model Comparison

DeepSeek V4 vs DeepSeek Reasoner

Benchmarks, API Token Pricing, Context Window & Speed Comparison

GetInfoUs 연구팀 검토 완료최종 검토일: October 2026편집 정책
공유
Related topics (12)
AI CompareAI BenchmarkToken PricingModel CostModel SpecsSWE-bench CodingModel VersusWhich AI ModelLLM Speed & LatencyReasoning ModelsExtended ThinkingPrompt Caching ROI
DeepSeek V4 vs DeepSeek Reasoner: Benchmarks, Pricing & Speed Comparison

DeepSeek의 DeepSeek V4와 DeepSeek의 DeepSeek Reasoner는 현재 가장 주목받는 AI 모델입니다. DeepSeek V4는 1M 토큰 컨텍스트 윈도우와 100만 토큰당 $0.30/$1.20(입력/출력)의 요금을 제공하며, DeepSeek Reasoner는 1M 토큰 컨텍스트와 $0.28/$0.42의 요금을 제공합니다. 아래에서 벤치마크, 지연 시간 및 비용을 상세히 비교해 보세요.

1:1 모델 맞대결 비교 (VS)

원하는 AI 모델 선택 및 실시간 비교

Select any AI models from our verified benchmark database to generate live side-by-side matrices.

Profile
Profile
Multi-Model Arena Stack:
5-Model Matrix Workbench
Popular Matchups:
DeepSeek

DeepSeek V4

오픈 웨이트 파운데이션 모델

DeepSeek의 DeepSeek V4 모델은 프라이빗 클라우드 자체 호스팅, 파인튜닝 및 완벽한 데이터 주권 확보에 최적화된 AI 모델로, 1M 컨텍스트 윈도우, 초당 78 토큰의 생성 속도, 종합 지능 지수 56/100점(1M 토큰당 입력 $0.3 / 출력 $1.2)을 제공합니다.

Intelligence Index56 / 100
Input Token:

$0.30 / 1M

Output Token:

$1.20 / 1M

Context Window:

1M tokens

SWE-bench Verified:

62.8%

DeepSeek

DeepSeek Reasoner

오픈 웨이트 추론 모델

DeepSeek의 DeepSeek Reasoner 모델은 강화학습 기반의 고효율 오픈 웨이트 사고 사슬(Chain-of-Thought) 추론에 최적화된 AI 모델로, 1M 컨텍스트 윈도우, 초당 52 토큰의 생성 속도, 종합 지능 지수 54/100점(1M 토큰당 입력 $0.28 / 출력 $0.42)을 제공합니다.

Intelligence Index54 / 100
Input Token:

$0.28 / 1M

Output Token:

$0.42 / 1M

Context Window:

1M tokens

SWE-bench Verified:

56.5%

한눈에 보는 핵심 차이점

  • DeepSeek Reasoner is 1.9x cheaper for API usage (input + output tokens combined).
  • DeepSeek V4 is faster at 78 tok/s vs 52 tok/s.
  • DeepSeek V4 leads on SWE-bench coding (62.8% vs 56.5%).
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureDeepSeek V4DeepSeek Reasoner
Intelligence Index
Composite reasoning & knowledge score (0-100)
5654
Provider
DeepSeekDeepSeek
Input Price
Cost per million input tokens
$0.30 / 1M$0.28 / 1M
Output Price
Cost per million output tokens
$1.20 / 1M$0.42 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$0.20 / 1M$0.12 / 1M
Context Window
Maximum input + output tokens
1M tokens~1,500 A4 pages of text1M tokens~1,500 A4 pages of text
Max Output Tokens
Per single request
66K33K
Throughput Speed
Tokens generated per second
78 tok/s52 tok/s
SWE-bench Verified
Real-world software engineering
62.8%56.5%
GPQA Diamond
Graduate-level reasoning
78.4%76.5%
Cached Input Price
Prompt caching discount per 1M tokens
$0.03 / 1M$0.03 / 1M

부문별 우위 모델

At-a-glance decision framework
Coding
DeepSeek V4
+6.3% SWE-bench lead
Cost
DeepSeek Reasoner
1.9x cheaper monthly runtime
Speed
DeepSeek V4
+50% faster generation
Context
Tied
1M tokens window

검증된 벤치마크 점수 비교

Official evaluation metrics verified by independent third-party research labs.

DeepSeek V4DeepSeek Reasoner
SWE-bench Verified (Autonomous Coding)

Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.

62.8%vs56.5%
GPQA Diamond (Doctorate-Level Science & Math)

Challenging questions created by domain experts to test complex PhD-level reasoning.

78.4%vs76.5%
MMLU-Pro (Multi-task Language Understanding)

Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.

87.5%vs86.2%
LMSYS Chatbot Arena Elo

Crowdsourced human preference evaluation based on blind, head-to-head model battles.

1415ptsvs1368pts
Humanity's Last Exam (Frontier Expert Knowledge)

Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.

14.5%vs13.5%
Terminal-Bench (Agentic Coding & Terminal Use)

Autonomous multi-step tasks executed inside a real command-line terminal environment.

42%vs37.5%
SciCode (Scientific Computing)

Research-grade coding problems drawn from real scientific and numerical workloads.

39.5%vs35%
LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

65%vs58.5%
AIME (Advanced Math Competition)

American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.

89%vs87.5%
Long-Context Reasoning

Accuracy when reasoning over very large inputs that fill the model context window.

88%vs85%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

78 tok/s vs52 tok/s

지연 시간(Latency) 및 응답 속도

Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.

Time to First Token (TTFT)Lower is better
DeepSeek V40.60s
DeepSeek Reasoner0.82s
Est. End-to-End Response (500 tokens)TTFT + generation time
DeepSeek V47.01s
DeepSeek Reasoner10.44s

실시간 API 토큰 비용 시뮬레이터

Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.

100K / day
10K500K1M
30K / day
5K150K300K
50% cached
0% (Cold)50%90% (Warm)
DeepSeek V4 Monthly API Bill
$1.57 / month
$0.3/1M in · $1.2/1M out
DeepSeek Reasoner Monthly API Bill
$0.84 / month
$0.28/1M in · $0.42/1M out
DeepSeek Reasoner saves approximately $0.73 / month (47%) under this workload.1.9x Savings

DeepSeek V4 · Strengths & Key Advantages

  • 1M 컨텍스트 윈도우 및 최대 66K 출력 토큰 생성 지원 (SWE-bench Verified 62.8% · GPQA Diamond 78.4%)
  • 초당 78 토큰의 빠른 처리 속도와 600ms에 불과한 첫 토큰 지연 시간(TTFT)
  • 오픈 웨이트 아키텍처 (MoE (Colossal))를 통해 vLLM, SGLang, Ollama 기반 자체 GPU 서버 구축 및 파인튜닝 가능
Considerations & Trade-offs
  • 비양자화 전체 정밀도로 로컬 서버에서 구동하려면 충분한 GPU VRAM 용량이 필요합니다
  • 초고난도 학술 연구 및 수학 증명 과제에서는 상위 대형 추론 특화 모델이 더 깊은 분석을 제공할 수 있습니다

DeepSeek Reasoner · Strengths & Key Advantages

  • 1M 컨텍스트 윈도우 및 최대 33K 출력 토큰 생성 지원 (SWE-bench Verified 56.5% · GPQA Diamond 76.5%)
  • 초당 52 토큰의 빠른 처리 속도와 820ms에 불과한 첫 토큰 지연 시간(TTFT)
  • 오픈 웨이트 아키텍처 (671B Total (37B ))를 통해 vLLM, SGLang, Ollama 기반 자체 GPU 서버 구축 및 파인튜닝 가능
Considerations & Trade-offs
  • 비양자화 전체 정밀도로 로컬 서버에서 구동하려면 충분한 GPU VRAM 용량이 필요합니다
  • 초고난도 학술 연구 및 수학 증명 과제에서는 상위 대형 추론 특화 모델이 더 깊은 분석을 제공할 수 있습니다

Token Use & Task Economics

Estimated cost and token consumption to complete one representative agentic task.

MetricDeepSeek V4DeepSeek Reasoner
Cost per Task
Estimated USD to finish one task
$0.38$0.22
Output Tokens / Task
Tokens generated per task
48K45K
Reasoning Tokens / Task
Thinking tokens burned per task
22K26K

기술 사양 (Specifications)

Architecture, release timeline, capabilities, and modalities.

FeatureDeepSeek V4DeepSeek Reasoner
Official API Model ID (SDK)deepseek-reasonerdeepseek-reasoner
API ProtocolOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGIOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date2026-072025-12
Knowledge CutoffApril 2026September 2025
ArchitectureNext-Gen Sparse MoE with MLAReinforcement-Learned Reasoning MoE
ParametersMoE (Colossal)671B Total (37B Active)
Enterprise Rate Limits (RPM / TPM)Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed CloudUnlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud
Long-Context Tier (>128K/200K)Flat rate ($0.3/1M in) across full 1000K windowFlat rate ($0.28/1M in) across full 1000K window
Prompt Cache Policy & TTLAutomatic prefix caching at $0.03/1M (no extra cache-write fee)Automatic prefix caching at $0.028/1M (no extra cache-write fee)
Computer Use / GUI AgentAPI / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)
MCP & Parallel Tool CallingNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON SchemaNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema
Web Search Grounding & SandboxSupported via external Tavily/Exa Search & E2B/Docker Sandbox toolsSupported via external Tavily/Exa Search & E2B/Docker Sandbox tools
Official Fine-Tuning SupportFull Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)
Multimodal Token BillingText BPE (~4 chars/token)Text BPE (~4 chars/token)
Quantization & Min GPU VRAMBF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 43 GB VRAM (4-bit Quantized) · 151 GB VRAM (Native BF16)BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 416 GB VRAM (4-bit Quantized) · 1443 GB VRAM (Native BF16)
Safety & Frontier ClassificationOpen-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma)Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma)
Enterprise Compliance (ZDR/HIPAA/SOC2)100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control
Input Modalitiestexttext
Output Modalitiestexttext

DeepSeek V4 vs DeepSeek Reasoner: 핵심 요약 문답 (FAQ)

Which is more intelligent, DeepSeek V4 or DeepSeek Reasoner?

DeepSeek V4 is more intelligent. It scores 56 compared with 54 on the composite Intelligence Index.

Which model is faster?

DeepSeek V4 is faster, generating 78 tokens per second compared with 52 tokens per second.

Which model is cheaper to run?

DeepSeek Reasoner is cheaper at $0.12 per 1M tokens blended, compared with $0.20 (7:2:1 cache:input:output ratio).

Which model has lower latency?

DeepSeek V4 has lower latency, with a time to first token of 0.60s compared with 0.82s.

종합 평가

DeepSeek V4(프라이빗 클라우드 자체 호스팅, 파인튜닝 및 완벽한 데이터 주권 확보, 엔터프라이즈 RAG 지식베이스 및 대규모 프로덕션 API 연동에 최적.)와 DeepSeek Reasoner(강화학습 기반의 고효율 오픈 웨이트 사고 사슬(Chain-of-Thought) 추론, 엔터프라이즈 RAG 지식베이스 및 대규모 프로덕션 API 연동에 최적.) 모두 뛰어난 성능을 자랑합니다. 고난도 추론과 코딩 벤치마크에서는 DeepSeek V4가 앞서며, 토큰 생성 속도에서는 DeepSeek V4가 빠르고, 대규모 API 운영 비용 면에서는 DeepSeek Reasoner가 더 경제적입니다.

자주 묻는 질문 (FAQ)

코딩과 복잡한 추론에는 DeepSeek V4와 DeepSeek Reasoner 중 어느 모델이 더 나은가요?+

종합 지능 지수 및 코딩 벤치마크에서는 DeepSeek V4가 우위를 보입니다. 상단의 SWE-bench 및 GPQA 점수를 함께 확인해 보세요.

DeepSeek V4와 DeepSeek Reasoner의 API 요금 차이는 얼마인가요?+

DeepSeek V4는 100만 토큰당 입력 $0.30 / 출력 $1.20이며, DeepSeek Reasoner는 입력 $0.28 / 출력 $0.42입니다. 혼합 비용 기준으로는 DeepSeek Reasoner가 더 저렴합니다.

어느 모델이 더 큰 컨텍스트 윈도우와 빠른 속도를 제공하나요?+

컨텍스트 길이는 DeepSeek V4가 1M 토큰, DeepSeek Reasoner가 1M 토큰를 지원하며, 출력 속도 면에서는 DeepSeek V4가 더 빠릅니다.

AI Terminology & Guide
Explore Glossary →

Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.

Tokens & PricingContext WindowAI BenchmarksReasoning ModelsTool CallingModel Context Protocol (MCP)

Related reading

Benchmark scores, token prices, and rate limits evolve rapidly as AI labs release model updates. Always verify current specifications and pricing on the official provider documentation.

공식 출처: DeepSeek 및 DeepSeek 공식 API 문서, 시스템 카드 및 독립 벤치마크 평가 (2026).