Claude Sonnet 4 vs Claude 3.7 Sonnet
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Claude Sonnet 4 by Anthropic and Claude 3.7 Sonnet 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, both models offer identical input token rates. On output generation, costs are matched. In data-heavy architectures requiring extensive repository or document analysis, both models provide an identical memory horizon with 200K tokens. In latency-sensitive production environments, Claude Sonnet 4 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.
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Claude Sonnet 4
高度な推論モデルAnthropic が提供する Claude Sonnet 4 は、最大 200K トークンのコンテキストウィンドウを備え、入力 $3/100万トークン・出力 $15/100万トークンで利用可能な、コーディング・推論・業務自動化向け AI モデルです。
$3.00 / 1M
$15.00 / 1M
200K tokens
72.7%
Claude 3.7 Sonnet
高度な推論モデルAnthropic が提供する Claude 3.7 Sonnet は、最大 200K トークンのコンテキストウィンドウを備え、入力 $3/100万トークン・出力 $15/100万トークンで利用可能な、コーディング・推論・業務自動化向け AI モデルです。
$3.00 / 1M
$15.00 / 1M
200K tokens
70.3%
ひと目でわかる主な違い
- Both models cost the same under this workload — choose on capability, not price.
- Claude Sonnet 4 is faster at 85 tok/s vs 72 tok/s.
- Claude Sonnet 4 leads on SWE-bench coding (72.7% vs 70.3%).
| Feature | Claude Sonnet 4 | Claude 3.7 Sonnet |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 58 | 48 |
Provider | Anthropic | Anthropic |
Input Price Cost per million input tokens | $3.00 / 1M | $3.00 / 1M |
Output Price Cost per million output tokens | $15.00 / 1M | $15.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $2.31 / 1M | $2.31 / 1M |
Context Window Maximum input + output tokens | 200K tokens~300 A4 pages of text | 200K tokens~300 A4 pages of text |
Max Output Tokens Per single request | 64K | 64K |
Throughput Speed Tokens generated per second | 85 tok/s | 72 tok/s |
SWE-bench Verified Real-world software engineering | 72.7% | 70.3% |
GPQA Diamond Graduate-level reasoning | 71.2% | 65% |
Cached Input Price Prompt caching discount per 1M tokens | $0.30 / 1M | $0.30 / 1M |
分野別勝者スコアカード
At-a-glance decision framework検証済みベンチマークスコア比較
Official evaluation metrics verified by independent third-party research labs.
Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.
Challenging questions created by domain experts to test complex PhD-level reasoning.
Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.
Crowdsourced human preference evaluation based on blind, head-to-head model battles.
Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.
Autonomous multi-step tasks executed inside a real command-line terminal environment.
Research-grade coding problems drawn from real scientific and numerical workloads.
Contamination-free competitive programming problems collected continuously over time.
American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.
Accuracy when reasoning over very large inputs that fill the model context window.
Tokens emitted per second during active generation streaming.
レイテンシ＆応答時間
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
リアルタイムAPIトークン料金シミュレーター
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Claude Sonnet 4 · Strengths & Key Advantages
- Anthropic の高度な Hybrid Multimodal Reasoning Transformer アーキテクチャと 200K トークンのコンテキスト処理能力
- 優れたコストパフォーマンス（入力 $3/1M、出力 $15/1M）と安定した応答速度
- プログラミング、論理的推論、多言語タスクにおいて高いベンチマーク精度を実現
Considerations & Trade-offs
- 大規模な長文コンテキスト処理時はトークン消費量の最適化が必要
- 専門領域のタスクでは適切なプロンプト設計や RAG の併用が推奨される
Claude 3.7 Sonnet · Strengths & Key Advantages
- Anthropic の高度な Hybrid Reasoning MoE アーキテクチャと 200K トークンのコンテキスト処理能力
- 優れたコストパフォーマンス（入力 $3/1M、出力 $15/1M）と安定した応答速度
- プログラミング、論理的推論、多言語タスクにおいて高いベンチマーク精度を実現
Considerations & Trade-offs
- 大規模な長文コンテキスト処理時はトークン消費量の最適化が必要
- 専門領域のタスクでは適切なプロンプト設計や RAG の併用が推奨される
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Claude Sonnet 4 | Claude 3.7 Sonnet |
|---|---|---|
Cost per Task Estimated USD to finish one task | $3.29 | $1.42 |
Output Tokens / Task Tokens generated per task | 72K | 41K |
Reasoning Tokens / Task Thinking tokens burned per task | 37K | 22K |
技術仕様・スペック
Architecture, release timeline, capabilities, and modalities.
| Feature | Claude Sonnet 4 | Claude 3.7 Sonnet |
|---|---|---|
| Official API Model ID (SDK) | claude-sonnet-4-20250501 | claude-3-7-sonnet-20250201 |
| API Protocol | Anthropic Messages API + AWS Bedrock + Vertex AI | Anthropic Messages API + AWS Bedrock + Vertex AI |
| Release Date | 2025-05 | 2025-02 |
| Knowledge Cutoff | March 2025 | November 2024 |
| Architecture | Hybrid Multimodal Reasoning Transformer | Hybrid Reasoning MoE |
| Parameters | Confidential | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 4,000 – 10,000 RPM (Tier 4 + Priority Tier) · 800,000 – 2,000,000 TPM | 4,000 – 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 tokens | Standard 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 | $0.3/1M cached read (90% off); 5-min default & 1-hour extended TTL |
| Computer Use / GUI Agent | Supported (Screen coordinate grounding, browser & desktop GUI action loops) | Supported (Screen coordinate grounding, browser & desktop GUI action loops) |
| MCP & Parallel Tool Calling | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema |
| Web Search Grounding & Sandbox | Native Web Search Citations + Code Execution Tool + MCP Connectors | Native Web Search Citations + Code Execution Tool + MCP Connectors |
| Official Fine-Tuning Support | Custom Model Tuning via Amazon Bedrock & Prompt Distillation | Custom Model Tuning via Amazon Bedrock & Prompt Distillation |
| Multimodal Token Billing | Text 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 VRAM | Cloud-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 Classification | Anthropic RSP ASL-2 / ASL-3 Constitutional Safety Evaluated | Anthropic 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 Tier | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier |
| Input Modalities | text, vision | text, vision |
| Output Modalities | text | text |
Claude Sonnet 4 vs Claude 3.7 Sonnet: よくある質問 (FAQ)
Which is more intelligent, Claude Sonnet 4 or Claude 3.7 Sonnet?
Claude Sonnet 4 is more intelligent. It scores 58 compared with 48 on the composite Intelligence Index.
Which model is faster?
Claude Sonnet 4 is faster, generating 85 tokens per second compared with 72 tokens per second.
総合評価
Verdict & Model Selection Recommendation: Choose Claude Sonnet 4 if your priority is Full-stack software engineering agents, CI/CD pipeline automation, and enterprise production applications. Its performance in demanding tasks justifies its operational token cost. Choose Claude 3.7 Sonnet if your workflow prioritizes Full-stack software architecture, complex refactoring, and agentic workflows. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
よくある質問 (FAQ)
How does Claude Sonnet 4 compare to Claude 3.7 Sonnet in API token pricing and cost per token?+
Both Claude Sonnet 4 and Claude 3.7 Sonnet share matched API token pricing at $3.00 per 1M input tokens and $15.00 per 1M output tokens. Because their token economics are identical, your selection should be guided by benchmark latency, memory horizon, and reasoning capabilities rather than operational token fees. Both models support prompt caching discounts of up to 90% for repeated prompt prefixes.
Which model has a larger context window: Claude Sonnet 4 or Claude 3.7 Sonnet?+
Both Claude Sonnet 4 and Claude 3.7 Sonnet offer an identical context window of 200K tokens (~150k words). Both architectures provide equal capacity for large document ingestion, multi-file codebases, and extended multi-turn conversation histories.
Which AI model is better for software engineering and coding: Claude Sonnet 4 or Claude 3.7 Sonnet?+
Claude Sonnet 4 scores 72.7% on SWE-bench Verified while Claude 3.7 Sonnet scores 70.3%. For autonomous software engineering and multi-file debugging, Claude Sonnet 4 holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between Claude Sonnet 4 and Claude 3.7 Sonnet?+
Claude Sonnet 4 features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Claude 3.7 Sonnet provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between Claude Sonnet 4 and Claude 3.7 Sonnet?+
Claude Sonnet 4 generates approximately 85 tokens per second, while Claude 3.7 Sonnet delivers 72 tokens per second. In latency-sensitive production environments, Claude Sonnet 4 provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Claude Sonnet 4 and Claude 3.7 Sonnet to optimize costs?+
Yes! Tiered multi-model routing can optimize for latency and accuracy: you can route high-frequency conversational turns and latency-sensitive queries to Claude Sonnet 4, and route deep architectural or complex reasoning queries to Claude Sonnet 4. This balanced routing strategy maximizes system throughput and answer quality.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
Related reading
- Claude 3.7 Sonnet vs DeepSeek R1: Benchmarks, Pricing & Speed Comparison比較
- Claude 3.7 Sonnet vs GPT-4o: Benchmarks, Pricing & Speed Comparison比較
- Claude 3.7 Sonnet vs o3-mini: Benchmarks, Pricing & Speed Comparison比較
- Claude Opus 4 vs Claude Sonnet 4: Benchmarks, Pricing & Speed Comparison比較
- Claude Sonnet 4 vs o1-pro: Benchmarks, Pricing & Speed Comparison比較
- Gemini 2.0 Pro vs Claude 3.7 Sonnet: Benchmarks, Pricing & Speed Comparison比較
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.
公的ソース: GetInfoUs Independent AI Benchmarks & Official API Specs