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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 araştırma ekibi tarafından incelendiSon inceleme: October 2026Yayın ilkeleri
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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

Anthropic tarafından geliştirilen Claude 3.7 Sonnet ve DeepSeek tarafından sunulan DeepSeek R1, kendi sınıflarının öne çıkan yapay zeka modelleridir. Claude 3.7 Sonnet, 200K token bağlam penceresi ve 1M token başına $3.00/$15.00 fiyat sunarken; DeepSeek R1, 128K token bağlam penceresi ve $0.55/$2.19 fiyatla gelir. Aşağıda tüm benchmark, hız ve maliyet detaylarını inceleyebilirsiniz.

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Popular Matchups:Claude 3.7 vs DeepSeek R1
Anthropic

Claude 3.7 Sonnet

Kodlama ve Yazılım Lideri

Anthropic tarafından geliştirilen Claude 3.7 Sonnet; otonom yazılım mühendisliği, çoklu dosya kod düzenleme ve ileri düzey hata ayıklama için tasarlanmış olup 200K bağlam penceresi, 72 token/sn hız, 48/100 zeka indeksi ve 1M token başına $3

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

Açık Ağırlıklı Akıl Yürütme

DeepSeek tarafından geliştirilen DeepSeek R1; yüksek verimli açık ağırlıklı düşünce zinciri (Chain-of-Thought) akıl yürütme için tasarlanmış olup 128K bağlam penceresi, 45 token/sn hız, 49/100 zeka indeksi ve 1M token başına $0.55/$2.19 fiy

Intelligence Index49 / 100
Input Token:

$0.55 / 1M

Output Token:

$2.19 / 1M

Context Window:

128K tokens

SWE-bench Verified:

49.2%

Bir Bakışta Temel Farklar

  • 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

Hangi Alanda Kim Kazanıyor?

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

Doğrulanmış Benchmark Puanları

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

Gecikme Süresi ve Yanıt Hızı

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

Gerçek Zamanlı Token Maliyet Simülatörü

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 bağlam penceresi ve yanıt başına maksimum 64K çıktı tokenı kapasitesi (SWE-bench Verified 70.3% · GPQA Diamond 65%)
  • Sadece 520 ms ilk token gecikmesi (TTFT) ile saniyede 72 token yüksek üretim hızı
  • İstem önbellekleme (Prompt Caching), araç çağırma ve yapılandırılmış JSON çıktısını destekleyen rekabetçi API ekonomisi (1M token başına $3/$15 fiyatlandırma)
Considerations & Trade-offs
  • Kapalı ağırlıklı bulut modelidir; çevrimdışı yerel sunuculara indirilerek çalıştırılamaz
  • Yüksek hacimli üretim trafiğinde maliyetleri optimize etmek için Prompt Caching ve Batch API kullanımı önerilir

DeepSeek R1 · Strengths & Key Advantages

  • 128K bağlam penceresi ve yanıt başına maksimum 8K çıktı tokenı kapasitesi (SWE-bench Verified 49.2% · GPQA Diamond 71.5%)
  • Sadece 950 ms ilk token gecikmesi (TTFT) ile saniyede 45 token yüksek üretim hızı
  • vLLM, SGLang veya Ollama üzerinden yerel GPU sunucularında barındırılabilen açık ağırlıklı mimari (671B Total (37B ))
Considerations & Trade-offs
  • Tam hassasiyette yerel GPU çıkarımı için yeterli VRAM kapasitesine sahip donanım altyapısı gerektirir
  • Çok karmaşık akademik ve bilimsel çıkarımlarda üst düzey ağır akıl yürütme modelleri daha derin sonuçlar verebilir

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

Teknik Özellikler

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: Hızlı Yanıtlar (SSS)

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.

Sonuç

Hem Claude 3.7 Sonnet (otonom yazılım mühendisliği, çoklu dosya kod düzenleme ve ileri düzey hata ayıklama, kurumsal RAG mimarileri ve ölçeklenebilir üretim API entegrasyonları için idealdir.) hem de DeepSeek R1 (yüksek verimli açık ağırlıklı düşünce zinciri (Chain-of-Thought) akıl yürütme, kurumsal RAG mimarileri ve ölçeklenebilir üretim API entegrasyonları için idealdir.) güçlü üretim seçenekleridir: DeepSeek R1 karmaşık akıl yürütme ve kodlama testlerinde öne çıkarken, Claude 3.7 Sonnet daha yüksek üretim hızı sağlar; DeepSeek R1 ise yüksek hacimli API kullanımlarında daha ekonomiktir.

Sıkça Sorulan Sorular (SSS)

Kodlama ve akıl yürütme için Claude 3.7 Sonnet mi yoksa DeepSeek R1 mi daha iyi?+

Bileşik zeka endeksi ve yazılım testlerinde DeepSeek R1 önde yer alıyor. Detaylı karşılaştırma için yukarıdaki SWE-bench ve GPQA tablolarına göz atabilirsiniz.

Claude 3.7 Sonnet ve DeepSeek R1 API fiyatları nasıl karşılaştırılır?+

Claude 3.7 Sonnet 1M giriş/çıkış tokeni başına $3.00 / $15.00 ücretlendirilirken, DeepSeek R1 $0.55 / $2.19 seviyesindedir. Karma maliyette DeepSeek R1 daha avantajlıdır.

Hangi modelin bağlam penceresi daha geniş ve hızı daha yüksek?+

Claude 3.7 Sonnet 200K token bağlam penceresi sunarken DeepSeek R1 128K token kapasiteye sahiptir. Saniyedeki token üretim hızında ise Claude 3.7 Sonnet öndedir.

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.

Resmi kaynaklar: Anthropic ve DeepSeek resmi API dokümantasyonu, sistem kartları ve bağımsız benchmark testleri (2026).