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Head-to-Head Model Comparison

Claude 3.7 Sonnet vs DeepSeek R1

Benchmarks, API Token Pricing, Context Window & Speed Comparison

GetInfoUs 연구팀 검토 완료최종 검토일: October 2026편집 정책
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AI CompareAI BenchmarkToken PricingModel CostModel SpecsSWE-bench CodingModel VersusWhich AI ModelLLM Speed & LatencyReasoning ModelsExtended ThinkingPrompt Caching ROI
Claude 3.7 Sonnet vs DeepSeek R1: Benchmarks, Pricing & Speed Comparison

Anthropic의 Claude 3.7 Sonnet와 DeepSeek의 DeepSeek R1는 현재 가장 주목받는 AI 모델입니다. Claude 3.7 Sonnet는 200K 토큰 컨텍스트 윈도우와 100만 토큰당 $3.00/$15.00(입력/출력)의 요금을 제공하며, DeepSeek R1는 128K 토큰 컨텍스트와 $0.55/$2.19의 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.

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Popular Matchups:Claude 3.7 vs DeepSeek R1
Anthropic

Claude 3.7 Sonnet

최고 수준의 코딩 및 소프트웨어 개발

Anthropic의 Claude 3.7 Sonnet 모델은 자율 소프트웨어 엔지니어링, 다중 파일 리팩토링 및 고난도 디버깅에 최적화된 AI 모델로, 200K 컨텍스트 윈도우, 초당 72 토큰의 생성 속도, 종합 지능 지수 48/100점(1M 토큰당 입력 $3 / 출력 $15)을 제공합니다.

Intelligence Index48 / 100
Input Token:

$3.00 / 1M

Output Token:

$15.00 / 1M

Context Window:

200K tokens

SWE-bench Verified:

70.3%

DeepSeek

DeepSeek R1

오픈 웨이트 추론 모델

DeepSeek의 DeepSeek R1 모델은 강화학습 기반의 고효율 오픈 웨이트 사고 사슬(Chain-of-Thought) 추론에 최적화된 AI 모델로, 128K 컨텍스트 윈도우, 초당 45 토큰의 생성 속도, 종합 지능 지수 49/100점(1M 토큰당 입력 $0.55 / 출력 $2.19)을 제공합니다.

Intelligence Index49 / 100
Input Token:

$0.55 / 1M

Output Token:

$2.19 / 1M

Context Window:

128K tokens

SWE-bench Verified:

49.2%

한눈에 보는 핵심 차이점

  • DeepSeek R1 is 6.1x cheaper for API usage (input + output tokens combined).
  • Claude 3.7 Sonnet has a larger context window (200K vs 128K tokens).
  • Claude 3.7 Sonnet is faster at 72 tok/s vs 45 tok/s.
  • Claude 3.7 Sonnet leads on SWE-bench coding (70.3% vs 49.2%).
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureClaude 3.7 SonnetDeepSeek R1
Intelligence Index
Composite reasoning & knowledge score (0-100)
4849
Provider
AnthropicDeepSeek
Input Price
Cost per million input tokens
$3.00 / 1M$0.55 / 1M
Output Price
Cost per million output tokens
$15.00 / 1M$2.19 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$2.31 / 1M$0.43 / 1M
Context Window
Maximum input + output tokens
200K tokens~300 A4 pages of text128K tokens~192 A4 pages of text
Max Output Tokens
Per single request
64K8K
Throughput Speed
Tokens generated per second
72 tok/s45 tok/s
SWE-bench Verified
Real-world software engineering
70.3%49.2%
GPQA Diamond
Graduate-level reasoning
65%71.5%
Cached Input Price
Prompt caching discount per 1M tokens
$0.30 / 1M$0.14 / 1M

부문별 우위 모델

At-a-glance decision framework
Coding
Claude 3.7 Sonnet
+21.1% SWE-bench lead
Cost
DeepSeek R1
6.1x cheaper monthly runtime
Speed
Claude 3.7 Sonnet
+60% faster generation
Context
Claude 3.7 Sonnet
200K tokens window

검증된 벤치마크 점수 비교

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

Claude 3.7 SonnetDeepSeek R1
SWE-bench Verified (Autonomous Coding)

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

70.3%vs49.2%
GPQA Diamond (Doctorate-Level Science & Math)

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

65%vs71.5%
MMLU-Pro (Multi-task Language Understanding)

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

78.2%vs84%
LMSYS Chatbot Arena Elo

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

1335ptsvs1340pts
Humanity's Last Exam (Frontier Expert Knowledge)

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

8.9%vs11.4%
Terminal-Bench (Agentic Coding & Terminal Use)

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

35.2%vs32.5%
SciCode (Scientific Computing)

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

40.1%vs29.5%
LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

64.8%vs51.7%
AIME (Advanced Math Competition)

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

61.3%vs82.2%
Long-Context Reasoning

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

71.5%vs75.6%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

72 tok/s vs45 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
Claude 3.7 Sonnet0.52s
DeepSeek R10.95s
Est. End-to-End Response (500 tokens)TTFT + generation time
Claude 3.7 Sonnet7.46s
DeepSeek R112.06s

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)
Claude 3.7 Sonnet Monthly API Bill
$18.45 / month
$3/1M in · $15/1M out
DeepSeek R1 Monthly API Bill
$3.01 / month
$0.55/1M in · $2.19/1M out
DeepSeek R1 saves approximately $15.44 / month (84%) under this workload.6.1x Savings

Claude 3.7 Sonnet · Strengths & Key Advantages

  • 200K 컨텍스트 윈도우 및 최대 64K 출력 토큰 생성 지원 (SWE-bench Verified 70.3% · GPQA Diamond 65%)
  • 초당 72 토큰의 빠른 처리 속도와 520ms에 불과한 첫 토큰 지연 시간(TTFT)
  • 합리적인 API 요금 체계(1M 토큰당 입력 $3 / 출력 $15)와 프롬프트 캐싱, 함수 호출 및 정형화된 JSON 출력 완벽 지원
Considerations & Trade-offs
  • 독점 클라우드 전용 모델로 로컬 오프라인 서버에 가중치를 다운로드하여 실행할 수 없습니다
  • 대규모 프로덕션 호출 환경에서는 비용 절감을 위해 프롬프트 캐싱과 배치 API(Batch API) 활용이 권장됩니다

DeepSeek R1 · Strengths & Key Advantages

  • 128K 컨텍스트 윈도우 및 최대 8K 출력 토큰 생성 지원 (SWE-bench Verified 49.2% · GPQA Diamond 71.5%)
  • 초당 45 토큰의 빠른 처리 속도와 950ms에 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.

MetricClaude 3.7 SonnetDeepSeek R1
Cost per Task
Estimated USD to finish one task
$1.42$0.65
Output Tokens / Task
Tokens generated per task
41K45K
Reasoning Tokens / Task
Thinking tokens burned per task
22K25K

기술 사양 (Specifications)

Architecture, release timeline, capabilities, and modalities.

FeatureClaude 3.7 SonnetDeepSeek R1
Official API Model ID (SDK)claude-3-7-sonnet-20250201deepseek-reasoner
API ProtocolAnthropic Messages API + AWS Bedrock + Vertex AIOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date2025-022025-01
Knowledge CutoffNovember 2024July 2024
ArchitectureHybrid Reasoning MoEOpen-Weights Reasoning MoE
ParametersConfidential671B Total (37B Active)
Enterprise Rate Limits (RPM / TPM)4,000 – 10,000 RPM (Tier 4 + Priority Tier) · 800,000 – 2,000,000 TPMUnlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud
Long-Context Tier (>128K/200K)Standard rate up to 200K tokens; 2x input/output tier above 200K tokensFlat rate ($0.55/1M in) across full 128K window
Prompt Cache Policy & TTL$0.3/1M cached read (90% off); 5-min default & 1-hour extended TTLAutomatic prefix caching at $0.14/1M (no extra cache-write fee)
Computer Use / GUI AgentSupported (Screen coordinate grounding, browser & desktop GUI action loops)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 & SandboxNative Web Search Citations + Code Execution Tool + MCP ConnectorsSupported via external Tavily/Exa Search & E2B/Docker Sandbox tools
Official Fine-Tuning SupportCustom Model Tuning via Amazon Bedrock & Prompt DistillationFull Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)
Multimodal Token BillingText BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)Text BPE (~4 chars/token)
Quantization & Min GPU VRAMCloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API)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 ClassificationAnthropic RSP ASL-2 / ASL-3 Constitutional Safety EvaluatedOpen-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma)
Enterprise Compliance (ZDR/HIPAA/SOC2)SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control
Input Modalitiestext, visiontext
Output Modalitiestexttext

Claude 3.7 Sonnet vs DeepSeek R1: 핵심 요약 문답 (FAQ)

Which is more intelligent, Claude 3.7 Sonnet or DeepSeek R1?

DeepSeek R1 is more intelligent. It scores 49 compared with 48 on the composite Intelligence Index.

Which model is faster?

Claude 3.7 Sonnet is faster, generating 72 tokens per second compared with 45 tokens per second.

Which model is cheaper to run?

DeepSeek R1 is cheaper at $0.43 per 1M tokens blended, compared with $2.31 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Claude 3.7 Sonnet has lower latency, with a time to first token of 0.52s compared with 0.95s.

Which model has a larger context window?

Claude 3.7 Sonnet has a larger context window, supporting 200K tokens compared with 128K tokens.

종합 평가

Claude 3.7 Sonnet(자율 소프트웨어 엔지니어링, 다중 파일 리팩토링 및 고난도 디버깅, 엔터프라이즈 RAG 지식베이스 및 대규모 프로덕션 API 연동에 최적.)와 DeepSeek R1(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 R1가 앞서며, 토큰 생성 속도에서는 Claude 3.7 Sonnet가 빠르고, 대규모 API 운영 비용 면에서는 DeepSeek R1가 더 경제적입니다.

자주 묻는 질문 (FAQ)

코딩과 복잡한 추론에는 Claude 3.7 Sonnet와 DeepSeek R1 중 어느 모델이 더 나은가요?+

종합 지능 지수 및 코딩 벤치마크에서는 DeepSeek R1가 우위를 보입니다. 상단의 SWE-bench 및 GPQA 점수를 함께 확인해 보세요.

Claude 3.7 Sonnet와 DeepSeek R1의 API 요금 차이는 얼마인가요?+

Claude 3.7 Sonnet는 100만 토큰당 입력 $3.00 / 출력 $15.00이며, DeepSeek R1는 입력 $0.55 / 출력 $2.19입니다. 혼합 비용 기준으로는 DeepSeek R1가 더 저렴합니다.

어느 모델이 더 큰 컨텍스트 윈도우와 빠른 속도를 제공하나요?+

컨텍스트 길이는 Claude 3.7 Sonnet가 200K 토큰, DeepSeek R1가 128K 토큰를 지원하며, 출력 속도 면에서는 Claude 3.7 Sonnet가 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: Anthropic 및 DeepSeek 공식 API 문서, 시스템 카드 및 독립 벤치마크 평가 (2026).