Grok 4.7 vs Llama 5
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Grok 4.7 by xAI against Llama 5 by Meta across real-world coding, reasoning, token economics, and latency.
원하는 AI 모델 선택 및 실시간 비교
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Live comparison: no in-depth editorial review has been published for this pair yet.
Grok 4.7
플래그십 프론티어 모델xAI의 Grok 4.7 모델은 최첨단 엔터프라이즈 추론, 소프트웨어 개발 및 핵심 프로덕션 워크로드에 최적화된 AI 모델로, 500K 컨텍스트 윈도우, 초당 110 토큰의 생성 속도, 종합 지능 지수 46/100점(1M 토큰당 입력 $2 / 출력 $6)을 제공합니다.
$2.00 / 1M
$6.00 / 1M
500K tokens
77.2%
Llama 5
오픈 웨이트 파운데이션 모델Meta의 Llama 5 모델은 프라이빗 클라우드 자체 호스팅, 파인튜닝 및 완벽한 데이터 주권 확보에 최적화된 AI 모델로, 5M 컨텍스트 윈도우, 초당 65 토큰의 생성 속도, 종합 지능 지수 94/100점(무료 오픈 웨이트 제공)을 제공합니다.
$0.00 / 1M
$0.00 / 1M
5M tokens
74%
한눈에 보는 핵심 차이점
- Llama 5 is 1.0x cheaper for API usage (input + output tokens combined).
- Llama 5 has a larger context window (5M vs 500K tokens).
- Grok 4.7 is faster at 110 tok/s vs 65 tok/s.
- Grok 4.7 leads on SWE-bench coding (77.2% vs 74.0%).
| Feature | Grok 4.7 | Llama 5 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 46 | 94 |
Provider | xAI | Meta |
Input Price Cost per million input tokens | $2.00 / 1M | $0.00 / 1M |
Output Price Cost per million output tokens | $6.00 / 1M | $0.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $1.35 / 1M | $0.00 / 1M |
Context Window Maximum input + output tokens | 500K tokens~750 A4 pages of text | 5M tokens~7,500 A4 pages of text |
Max Output Tokens Per single request | 66K | 128K |
Throughput Speed Tokens generated per second | 110 tok/s | 65 tok/s |
SWE-bench Verified Real-world software engineering | 77.2% | 74% |
GPQA Diamond Graduate-level reasoning | 78.5% | 84% |
Cached Input Price Prompt caching discount per 1M tokens | $0.50 / 1M | $0.00 / 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 4.7 · Strengths & Key Advantages
- 500K 컨텍스트 윈도우 및 최대 66K 출력 토큰 생성 지원 (SWE-bench Verified 77.2% · GPQA Diamond 78.5%)
- 초당 110 토큰의 빠른 처리 속도와 620ms에 불과한 첫 토큰 지연 시간(TTFT)
- 합리적인 API 요금 체계(1M 토큰당 입력 $2 / 출력 $6)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
Llama 5 · Strengths & Key Advantages
- 5M 컨텍스트 윈도우 및 최대 128K 출력 토큰 생성 지원 (SWE-bench Verified 74% · GPQA Diamond 84%)
- 초당 65 토큰의 빠른 처리 속도와 850ms에 불과한 첫 토큰 지연 시간(TTFT)
- 오픈 웨이트 아키텍처 (600B+ total (~40-80B ))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.
| Metric | Grok 4.7 | Llama 5 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.68 | $0.00 |
Output Tokens / Task Tokens generated per task | 46K | 40K |
Reasoning Tokens / Task Thinking tokens burned per task | 23K | 20K |
기술 사양 (Specifications)
Architecture, release timeline, capabilities, and modalities.
| Feature | Grok 4.7 | Llama 5 |
|---|---|---|
| Official API Model ID (SDK) | grok-4-7-latest | meta-llama/Llama-5 |
| API Protocol | xAI REST API (OpenAI-Compatible) | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2026-09 | 2026-04 |
| Knowledge Cutoff | April 2026 | January 2026 |
| Architecture | 2.1T Multimodal Sparse MoE | Mixture-of-Experts with recursive self-improvement (~600B total, 40-80B active) |
| Parameters | 2.1T (MoE) | 600B+ total (~40-80B active) |
| Enterprise Rate Limits (RPM / TPM) | 5,000 – 15,000 RPM (Production Tier) · 800,000 – 2,000,000 TPM | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud |
| Long-Context Tier (>128K/200K) | Flat rate ($2/1M in) across full 500K window | Free across full context window (only KV-cache VRAM scales) |
| Prompt Cache Policy & TTL | Cached input at $0.5/1M tokens | Automatic prefix KV-caching in vLLM / SGLang (0 cost) |
| 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 | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools |
| Official Fine-Tuning Support | API Prompt Engineering & RAG Distillation | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) |
| 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) · Audio (~25–32 tokens/sec) · Video (~258–300 tokens/sec) |
| Quantization & Min GPU VRAM | Cloud-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) · 372 GB VRAM (4-bit Quantized) · 1290 GB VRAM (Native BF16) |
| Safety & Frontier Classification | xAI Production Safety & Alignment Verified | Open-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 Tier | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control |
| Input Modalities | text, vision | text, vision, video, audio |
| Output Modalities | text | text |
Grok 4.7 vs Llama 5: 핵심 요약 문답 (FAQ)
Which is more intelligent, Grok 4.7 or Llama 5?
Llama 5 is more intelligent. It scores 94 compared with 46 on the composite Intelligence Index.
Which model is faster?
Grok 4.7 is faster, generating 110 tokens per second compared with 65 tokens per second.
Which model is cheaper to run?
Llama 5 is cheaper at $0.00 per 1M tokens blended, compared with $1.35 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Grok 4.7 has lower latency, with a time to first token of 0.62s compared with 0.85s.
Which model has a larger context window?
Llama 5 has a larger context window, supporting 5M tokens compared with 500K tokens.
종합 평가
Choose Grok 4.7 for 최첨단 엔터프라이즈 추론, 소프트웨어 개발 및 핵심 프로덕션 워크로드, 엔터프라이즈 rag 지식베이스 및 대규모 프로덕션 api 연동에 최적., or choose Llama 5 for 프라이빗 클라우드 자체 호스팅, 파인튜닝 및 완벽한 데이터 주권 확보, 엔터프라이즈 rag 지식베이스 및 대규모 프로덕션 api 연동에 최적..
자주 묻는 질문 (FAQ)
Which is cheaper: Grok 4.7 or Llama 5?+
Grok 4.7 costs $2/1M input and $6/1M output tokens, while Llama 5 costs $0/1M input and $0/1M output tokens.
Which model has a larger context window: Grok 4.7 or Llama 5?+
Grok 4.7 supports 500K tokens whereas Llama 5 supports 5M tokens.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
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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.
공식 출처: xAI & Meta Official Documentation