Grok-3 vs Claude 3.7 Sonnet
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Grok-3 by xAI 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, Grok-3 provides an expanded memory horizon with 1,000K tokens. In latency-sensitive production environments, Grok-3 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.
원하는 AI 모델 선택 및 실시간 비교
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Grok-3
심층 추론 모델xAI의 Grok-3 모델은 1M 토큰 컨텍스트 윈도우와 입력 $3/1M 토큰, 출력 $15/1M 토큰의 합리적인 API 요금을 제공하며 코딩, 추론 및 엔터프라이즈 자동화에 최적화되어 있습니다.
$3.00 / 1M
$15.00 / 1M
1M tokens
65.2%
Claude 3.7 Sonnet
심층 추론 모델Anthropic의 Claude 3.7 Sonnet 모델은 200K 토큰 컨텍스트 윈도우와 입력 $3/1M 토큰, 출력 $15/1M 토큰의 합리적인 API 요금을 제공하며 코딩, 추론 및 엔터프라이즈 자동화에 최적화되어 있습니다.
$3.00 / 1M
$15.00 / 1M
200K tokens
70.3%
한눈에 보는 핵심 차이점
- Claude 3.7 Sonnet is 1.0x cheaper for API usage (input + output tokens combined).
- Grok-3 has a larger context window (1M vs 200K tokens).
- Grok-3 is faster at 95 tok/s vs 72 tok/s.
- Claude 3.7 Sonnet leads on SWE-bench coding (70.3% vs 65.2%).
| Feature | Grok-3 | Claude 3.7 Sonnet |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 54 | 48 |
Provider | xAI | 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 | $2.63 / 1M | $2.31 / 1M |
Context Window Maximum input + output tokens | 1M tokens~1,500 A4 pages of text | 200K tokens~300 A4 pages of text |
Max Output Tokens Per single request | 33K | 64K |
Throughput Speed Tokens generated per second | 95 tok/s | 72 tok/s |
SWE-bench Verified Real-world software engineering | 65.2% | 70.3% |
GPQA Diamond Graduate-level reasoning | 74% | 65% |
Cached Input Price Prompt caching discount per 1M tokens | $0.75 / 1M | $0.30 / 1M |
부문별 우위 모델
At-a-glance decision framework검증된 벤치마크 점수 비교
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.
지연 시간(Latency) 및 응답 속도
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
실시간 API 토큰 비용 시뮬레이터
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Grok-3 · Strengths & Key Advantages
- xAI의 첨단 Hybrid Reasoning Deep MoE 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 3.7 Sonnet · Strengths & Key Advantages
- Anthropic의 첨단 Hybrid Reasoning MoE 아키텍처와 최대 200K 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
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Grok-3 | Claude 3.7 Sonnet |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.20 | $1.42 |
Output Tokens / Task Tokens generated per task | 24K | 41K |
Reasoning Tokens / Task Thinking tokens burned per task | 15K | 22K |
기술 사양 (Specifications)
Architecture, release timeline, capabilities, and modalities.
| Feature | Grok-3 | Claude 3.7 Sonnet |
|---|---|---|
| Official API Model ID (SDK) | grok-3-latest | claude-3-7-sonnet-20250201 |
| API Protocol | xAI REST API (OpenAI-Compatible) | Anthropic Messages API + AWS Bedrock + Vertex AI |
| Release Date | 2025-02 | 2025-02 |
| Knowledge Cutoff | Late 2024 | November 2024 |
| Architecture | Hybrid Reasoning Deep MoE | Hybrid Reasoning MoE |
| Parameters | Confidential | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 5,000 – 15,000 RPM (Production Tier) · 4,000,000 – 10,000,000 TPM | 4,000 – 10,000 RPM (Tier 4 + Priority Tier) · 800,000 – 2,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($3/1M in) across full 1000K window | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens |
| Prompt Cache Policy & TTL | Cached input at $0.75/1M tokens | $0.3/1M cached read (90% off); 5-min default & 1-hour extended TTL |
| Computer Use / GUI Agent | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) | 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 | Real-time Web & X (Twitter) Search Grounding + Code Execution | Native Web Search Citations + Code Execution Tool + MCP Connectors |
| Official Fine-Tuning Support | API Prompt Engineering & RAG 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 | xAI Production Safety & Alignment Verified | 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 |
Grok-3 vs Claude 3.7 Sonnet: 핵심 요약 문답 (FAQ)
Which is more intelligent, Grok-3 or Claude 3.7 Sonnet?
Grok-3 is more intelligent. It scores 54 compared with 48 on the composite Intelligence Index.
Which model is faster?
Grok-3 is faster, generating 95 tokens per second compared with 72 tokens per second.
Which model is cheaper to run?
Claude 3.7 Sonnet is cheaper at $2.31 per 1M tokens blended, compared with $2.63 (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 1.10s.
Which model has a larger context window?
Grok-3 has a larger context window, supporting 1M tokens compared with 200K tokens.
종합 평가
Verdict & Model Selection Recommendation: Choose Grok-3 if your priority is Frontier algorithmic development, complex technical architecture, and mathematical proofs. 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.
자주 묻는 질문 (FAQ)
How does Grok-3 compare to Claude 3.7 Sonnet in API token pricing and cost per token?+
Both Grok-3 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: Grok-3 or Claude 3.7 Sonnet?+
Grok-3 supports a context window of 1,000K tokens (~750k words), while Claude 3.7 Sonnet supports 200K tokens (~150k words). Therefore, Grok-3 can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: Grok-3 or Claude 3.7 Sonnet?+
Grok-3 scores 65.2% on SWE-bench Verified while Claude 3.7 Sonnet scores 70.3%. For autonomous software engineering and multi-file debugging, Claude 3.7 Sonnet holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between Grok-3 and Claude 3.7 Sonnet?+
Grok-3 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 Grok-3 and Claude 3.7 Sonnet?+
Grok-3 generates approximately 95 tokens per second, while Claude 3.7 Sonnet delivers 72 tokens per second. In latency-sensitive production environments, Grok-3 provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Grok-3 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 Grok-3, and route deep architectural or complex reasoning queries to Grok-3. 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
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- Claude 3.7 Sonnet vs GPT-4o: Benchmarks, Pricing & Speed Comparison비교
- Claude 3.7 Sonnet vs o3-mini: Benchmarks, Pricing & Speed Comparison비교
- Claude Sonnet 4 vs Claude 3.7 Sonnet: Benchmarks, Pricing & Speed Comparison비교
- Gemini 2.0 Pro vs Claude 3.7 Sonnet: Benchmarks, Pricing & Speed Comparison비교
- Grok 3 Mini vs Claude 3.7 Sonnet: Benchmarks, Pricing & Speed Comparison비교
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: GetInfoUs Independent AI Benchmarks & Official API Specs