Head-to-Head Model Comparison

Claude Sonnet 4.6 vs Claude Opus 4.5

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 Sonnet 4.6 vs Claude Opus 4.5: Benchmarks, Pricing & Speed Comparison

Claude Sonnet 4.6 by Anthropic and Claude Opus 4.5 by Anthropic represent two of the frontier AI models analyzed in our comprehensive 2026 AI benchmark and LLM comparison. In this head-to-head evaluation, we examine API token pricing, context window specifications, inference speed (tokens per second), and empirical reasoning performance. From a pure economic perspective, Claude Sonnet 4.6 holds a notable pricing advantage for token ingestion. On output generation, Claude Sonnet 4.6 is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, Claude Sonnet 4.6 provides an expanded memory horizon with 1,000K tokens. In latency-sensitive production environments, Claude Sonnet 4.6 leads in tokens-per-second throughput, making it ideal for real-time conversational agents and high-concurrency workflows. Below is the complete empirical head-to-head breakdown across cost, speed, context, and capability matrices.

1:1 모델 맞대결 비교 (VS)

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Anthropic

Claude Sonnet 4.6

심층 추론 모델

Anthropic의 Claude Sonnet 4.6 모델은 1M 토큰 컨텍스트 윈도우와 입력 $3/1M 토큰, 출력 $15/1M 토큰의 합리적인 API 요금을 제공하며 코딩, 추론 및 엔터프라이즈 자동화에 최적화되어 있습니다.

Intelligence Index60 / 100
Input Token:

$3.00 / 1M

Output Token:

$15.00 / 1M

Context Window:

1M tokens

SWE-bench Verified:

76.1%

Anthropic

Claude Opus 4.5

심층 추론 모델

Anthropic의 Claude Opus 4.5 모델은 200K 토큰 컨텍스트 윈도우와 입력 $5/1M 토큰, 출력 $25/1M 토큰의 합리적인 API 요금을 제공하며 코딩, 추론 및 엔터프라이즈 자동화에 최적화되어 있습니다.

Intelligence Index63 / 100
Input Token:

$5.00 / 1M

Output Token:

$25.00 / 1M

Context Window:

200K tokens

SWE-bench Verified:

77.2%

한눈에 보는 핵심 차이점

  • Claude Sonnet 4.6 is 1.7x cheaper for API usage (input + output tokens combined).
  • Claude Sonnet 4.6 has a larger context window (1M vs 200K tokens).
  • Claude Sonnet 4.6 is faster at 92 tok/s vs 62 tok/s.
  • Claude Opus 4.5 leads on SWE-bench coding (77.2% vs 76.1%).
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureClaude Sonnet 4.6Claude Opus 4.5
Intelligence Index
Composite reasoning & knowledge score (0-100)
6063
Provider
AnthropicAnthropic
Input Price
Cost per million input tokens
$3.00 / 1M$5.00 / 1M
Output Price
Cost per million output tokens
$15.00 / 1M$25.00 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$2.31 / 1M$3.85 / 1M
Context Window
Maximum input + output tokens
1M tokens~1,500 A4 pages of text200K tokens~300 A4 pages of text
Max Output Tokens
Per single request
128K64K
Throughput Speed
Tokens generated per second
92 tok/s62 tok/s
SWE-bench Verified
Real-world software engineering
76.1%77.2%
GPQA Diamond
Graduate-level reasoning
74%74.8%
Cached Input Price
Prompt caching discount per 1M tokens
$0.30 / 1M$0.50 / 1M

부문별 우위 모델

At-a-glance decision framework
Coding
Claude Opus 4.5
+1.1% SWE-bench lead
Cost
Claude Sonnet 4.6
1.7x cheaper monthly runtime
Speed
Claude Sonnet 4.6
+48% faster generation
Context
Claude Sonnet 4.6
1M tokens window

검증된 벤치마크 점수 비교

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

Claude Sonnet 4.6Claude Opus 4.5
SWE-bench Verified (Autonomous Coding)

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

76.1%vs77.2%
GPQA Diamond (Doctorate-Level Science & Math)

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

74%vs74.8%
MMLU-Pro (Multi-task Language Understanding)

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

86.7%vs87.3%
LMSYS Chatbot Arena Elo

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

1380ptsvs1376pts
Humanity's Last Exam (Frontier Expert Knowledge)

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

N/Avs13.7%
Terminal-Bench (Agentic Coding & Terminal Use)

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

47.8%vs49.2%
SciCode (Scientific Computing)

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

N/Avs45.6%
LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

N/Avs74.1%
AIME (Advanced Math Competition)

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

N/Avs88.5%
Long-Context Reasoning

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

N/Avs79.4%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

92 tok/s vs62 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 Sonnet 4.60.65s
Claude Opus 4.52.10s
Est. End-to-End Response (500 tokens)TTFT + generation time
Claude Sonnet 4.66.08s
Claude Opus 4.510.16s

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 Sonnet 4.6 Monthly API Bill
$18.45 / month
$3/1M in · $15/1M out
Claude Opus 4.5 Monthly API Bill
$30.75 / month
$5/1M in · $25/1M out
Claude Sonnet 4.6 saves approximately $12.30 / month (40%) under this workload.1.7x Savings

Claude Sonnet 4.6 · Strengths & Key Advantages

  • Anthropic의 첨단 High-Throughput Multimodal Agent Transformer 아키텍처와 최대 1M 토큰 컨텍스트 지원
  • 경쟁력 있는 API 가격(입력 $3/1M, 출력 $15/1M)과 빠른 응답 속도
  • 코드 생성, 복잡한 논리 추론 및 다국어 벤치마크에서 검증된 성능 발휘
Considerations & Trade-offs
  • 대규모 장문 컨텍스트 처리 시 토큰 비용 및 호출 한도 관리가 필요함
  • 특수 도메인 작업에서는 정교한 프롬프트 엔지니어링과 RAG 연동이 권장됨

Claude Opus 4.5 · Strengths & Key Advantages

  • Anthropic의 첨단 Hybrid Reasoning 아키텍처와 최대 200K 토큰 컨텍스트 지원
  • 경쟁력 있는 API 가격(입력 $5/1M, 출력 $25/1M)과 빠른 응답 속도
  • 코드 생성, 복잡한 논리 추론 및 다국어 벤치마크에서 검증된 성능 발휘
Considerations & Trade-offs
  • 대규모 장문 컨텍스트 처리 시 토큰 비용 및 호출 한도 관리가 필요함
  • 특수 도메인 작업에서는 정교한 프롬프트 엔지니어링과 RAG 연동이 권장됨

Token Use & Task Economics

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

MetricClaude Sonnet 4.6Claude Opus 4.5
Cost per Task
Estimated USD to finish one task
$3.29$5.86
Output Tokens / Task
Tokens generated per task
72K73K
Reasoning Tokens / Task
Thinking tokens burned per task
37K43K

기술 사양 (Specifications)

Architecture, release timeline, capabilities, and modalities.

FeatureClaude Sonnet 4.6Claude Opus 4.5
Official API Model ID (SDK)claude-sonnet-4-6-20260201claude-opus-4-5-20251101
API ProtocolAnthropic Messages API + AWS Bedrock + Vertex AIAnthropic Messages API + AWS Bedrock + Vertex AI
Release Date2026-022025-11
Knowledge CutoffDecember 2025March 2025
ArchitectureHigh-Throughput Multimodal Agent TransformerHybrid Reasoning
ParametersConfidentialConfidential
Enterprise Rate Limits (RPM / TPM)4,000 – 10,000 RPM (Tier 4 + Priority Tier) · 4,000,000 – 10,000,000 TPM4,000 – 10,000 RPM (Tier 4 + Priority Tier) · 800,000 – 2,000,000 TPM
Long-Context Tier (>128K/200K)Standard rate up to 200K tokens; 2x input/output tier above 200K tokensStandard rate up to 200K tokens; 2x input/output tier above 200K tokens
Prompt Cache Policy & TTL$0.3/1M cached read (90% off); 5-min default & 1-hour extended TTL$0.5/1M cached read (90% off); 5-min default & 1-hour extended TTL
Computer Use / GUI AgentSupported (Screen coordinate grounding, browser & desktop GUI action loops)Supported (Screen coordinate grounding, browser & desktop GUI action loops)
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 ConnectorsNative Web Search Citations + Code Execution Tool + MCP Connectors
Official Fine-Tuning SupportCustom Model Tuning via Amazon Bedrock & Prompt DistillationCustom Model Tuning via Amazon Bedrock & Prompt Distillation
Multimodal Token BillingText BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)
Quantization & Min GPU VRAMCloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API)Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API)
Safety & Frontier ClassificationAnthropic RSP ASL-2 / ASL-3 Constitutional Safety EvaluatedAnthropic RSP ASL-2 / ASL-3 Constitutional Safety Evaluated
Enterprise Compliance (ZDR/HIPAA/SOC2)SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise TierSOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier
Input Modalitiestext, visiontext, vision
Output Modalitiestexttext

Claude Sonnet 4.6 vs Claude Opus 4.5: 핵심 요약 문답 (FAQ)

Which is more intelligent, Claude Sonnet 4.6 or Claude Opus 4.5?

Claude Opus 4.5 is more intelligent. It scores 63 compared with 60 on the composite Intelligence Index.

Which model is faster?

Claude Sonnet 4.6 is faster, generating 92 tokens per second compared with 62 tokens per second.

Which model is cheaper to run?

Claude Sonnet 4.6 is cheaper at $2.31 per 1M tokens blended, compared with $3.85 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Claude Sonnet 4.6 has lower latency, with a time to first token of 0.65s compared with 2.10s.

Which model has a larger context window?

Claude Sonnet 4.6 has a larger context window, supporting 1M tokens compared with 200K tokens.

종합 평가

Verdict & Model Selection Recommendation: Choose Claude Sonnet 4.6 if your priority is Production coding copilots, full-repo code search, customer agent workflows, and automated workflows. Its performance in demanding tasks justifies its operational token cost. Choose Claude Opus 4.5 if your workflow prioritizes Complex reasoning, creative work, and high-stakes decision making where quality outweighs cost. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.

자주 묻는 질문 (FAQ)

How does Claude Sonnet 4.6 compare to Claude Opus 4.5 in API token pricing and cost per token?+

Claude Sonnet 4.6 costs $3.00 per 1M input tokens and $15.00 per 1M output tokens, whereas Claude Opus 4.5 costs $5.00 input and $25.00 output. Overall, Claude Sonnet 4.6 is the more cost-effective model for high-volume API pipelines. Both models support prompt caching discounts of up to 90% for repeated prompt prefixes.

Which model has a larger context window: Claude Sonnet 4.6 or Claude Opus 4.5?+

Claude Sonnet 4.6 supports a context window of 1,000K tokens (~750k words), while Claude Opus 4.5 supports 200K tokens (~150k words). Therefore, Claude Sonnet 4.6 can process more textual data and document context in a single prompt.

Which AI model is better for software engineering and coding: Claude Sonnet 4.6 or Claude Opus 4.5?+

Claude Sonnet 4.6 scores 76.1% on SWE-bench Verified while Claude Opus 4.5 scores 77.2%. For autonomous software engineering and multi-file debugging, Claude Opus 4.5 holds the verified benchmark lead.

What is the difference in reasoning capability and extended thinking between Claude Sonnet 4.6 and Claude Opus 4.5?+

Claude Sonnet 4.6 features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Claude Opus 4.5 provides chain-of-thought reasoning depth for complex math and science.

How does inference speed (tokens per second) compare between Claude Sonnet 4.6 and Claude Opus 4.5?+

Claude Sonnet 4.6 generates approximately 92 tokens per second, while Claude Opus 4.5 delivers 62 tokens per second. In latency-sensitive production environments, Claude Sonnet 4.6 provides faster time-to-first-token (TTFT) and higher streaming response velocity.

Can you implement a tiered LLM routing setup combining Claude Sonnet 4.6 and Claude Opus 4.5 to optimize costs?+

Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Claude Sonnet 4.6 to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to Claude Opus 4.5. This tiered routing pattern cuts overall API bills by up to 70%.

AI Terminology & Guide
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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.

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