Head-to-Head Model Comparison

Llama 5 vs Muse Spark 1.1

Benchmarks, API Token Pricing, Context Window & Speed Comparison

GetInfoUs 연구팀 검토 완료최종 검토일: October 2026편집 정책

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Llama 5Profile

Muse Spark 1.1Profile

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Llama 5

오픈 웨이트 파운데이션 모델

Meta의 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.

Intelligence Index94 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

5M tokens

SWE-bench Verified:

74%

Muse Spark 1.1

네이티브 멀티모달 AI

Meta의 Muse Spark 1.1 모델은 텍스트와 이미지의 네이티브 멀티모달 이해 및 복합 문서·차트 분석에 최적화된 AI 모델로, 1M 컨텍스트 윈도우, 초당 105 토큰의 생성 속도, 종합 지능 지수 90/100점(1M 토큰당 입력 $3 / 출력 $15)을 제공합니다.

Intelligence Index90 / 100

Input Token:

$3.00 / 1M

Output Token:

$15.00 / 1M

Context Window:

1M tokens

SWE-bench Verified:

76.5%

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 1M tokens).
Muse Spark 1.1 is faster at 105 tok/s vs 65 tok/s.
Muse Spark 1.1 leads on SWE-bench coding (76.5% vs 74.0%).

Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →

Feature	Llama 5	Muse Spark 1.1
Intelligence Index Composite reasoning & knowledge score (0-100)	94	90
Provider	Meta	Meta
Input Price Cost per million input tokens	$0.00 / 1M	$3.00 / 1M
Output Price Cost per million output tokens	$0.00 / 1M	$15.00 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.00 / 1M	$2.31 / 1M
Context Window Maximum input + output tokens	5M tokens~7,500 A4 pages of text	1M tokens~1,500 A4 pages of text
Max Output Tokens Per single request	128K	64K
Throughput Speed Tokens generated per second	65 tok/s	105 tok/s
SWE-bench Verified Real-world software engineering	74%	76.5%
GPQA Diamond Graduate-level reasoning	84%	83.5%
Cached Input Price Prompt caching discount per 1M tokens	$0.00 / 1M	$0.30 / 1M

부문별 우위 모델

At-a-glance decision framework

Coding

Muse Spark 1.1

+2.5% SWE-bench lead

Cost

Llama 5

1.0x cheaper monthly runtime

Speed

Muse Spark 1.1

+62% faster generation

Context

Llama 5

5M tokens window

검증된 벤치마크 점수 비교

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

Llama 5Muse Spark 1.1

SWE-bench Verified (Autonomous Coding)

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

74%vs76.5%

GPQA Diamond (Doctorate-Level Science & Math)

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

84%vs83.5%

MMLU-Pro (Multi-task Language Understanding)

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

87%vs88%

LMSYS Chatbot Arena Elo

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

1485ptsvs1495pts

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

68%vsN/A

AIME (Advanced Math Competition)

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

88%vsN/A

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

65 tok/s vs105 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

Llama 50.85s

Muse Spark 1.10.60s

Est. End-to-End Response (500 tokens)TTFT + generation time

Llama 58.54s

Muse Spark 1.15.36s

실시간 API 토큰 비용 시뮬레이터

Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.

Daily Input Tokens100K / day

10K500K1M

Daily Output Tokens30K / day

5K150K300K

Prompt Cache Hit Ratio50% cached

0% (Cold)50%90% (Warm)

Llama 5 Monthly API Bill

$0.00 / month

$0/1M in · $0/1M out

Muse Spark 1.1 Monthly API Bill

$18.45 / month

$3/1M in · $15/1M out

Llama 5 saves approximately $18.45 / month (100%) under this workload.1.0x Savings

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

Muse Spark 1.1 · Strengths & Key Advantages

1M 컨텍스트 윈도우 및 최대 64K 출력 토큰 생성 지원 (SWE-bench Verified 76.5% · GPQA Diamond 83.5%)
초당 105 토큰의 빠른 처리 속도와 600ms에 불과한 첫 토큰 지연 시간(TTFT)
합리적인 API 요금 체계(1M 토큰당 입력 $3 / 출력 $15)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

Token Use & Task Economics

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

Metric	Llama 5	Muse Spark 1.1
Cost per Task Estimated USD to finish one task	$0.00	$2.20
Output Tokens / Task Tokens generated per task	40K	45K
Reasoning Tokens / Task Thinking tokens burned per task	20K	22K

기술 사양 (Specifications)

Architecture, release timeline, capabilities, and modalities.

Feature	Llama 5	Muse Spark 1.1
Official API Model ID (SDK)	meta-llama/Llama-5	meta-llama/Muse-Spark-1.1
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	Meta REST API (OpenAI-Compatible)
Release Date	2026-04	2026-04
Knowledge Cutoff	January 2026	January 2026
Architecture	Mixture-of-Experts with recursive self-improvement (~600B total, 40-80B active)	Omni-modal native thinking agent architecture
Parameters	600B+ total (~40-80B active)	Confidential
Enterprise Rate Limits (RPM / TPM)	Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud	5,000 – 15,000 RPM (Production Tier) · 4,000,000 – 10,000,000 TPM
Long-Context Tier (>128K/200K)	Free across full context window (only KV-cache VRAM scales)	Flat rate ($3/1M in) across full 1000K window
Prompt Cache Policy & TTL	Automatic prefix KV-caching in vLLM / SGLang (0 cost)	Cached input at $0.3/1M tokens
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	Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools	Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools
Official Fine-Tuning Support	Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)	API Prompt Engineering & RAG Distillation
Multimodal Token Billing	Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) · Audio (~25–32 tokens/sec) · Video (~258–300 tokens/sec)	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	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 372 GB VRAM (4-bit Quantized) · 1290 GB VRAM (Native BF16)	Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API)
Safety & Frontier Classification	Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma)	Meta Production Safety & Alignment Verified
Enterprise Compliance (ZDR/HIPAA/SOC2)	100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control	SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier
Input Modalities	text, vision, video, audio	text, vision, video, audio
Output Modalities	text	text

Llama 5 vs Muse Spark 1.1: 핵심 요약 문답 (FAQ)

Which is more intelligent, Llama 5 or Muse Spark 1.1?

Llama 5 is more intelligent. It scores 94 compared with 90 on the composite Intelligence Index.

Which model is faster?

Muse Spark 1.1 is faster, generating 105 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 $2.31 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Muse Spark 1.1 has lower latency, with a time to first token of 0.60s compared with 0.85s.

Which model has a larger context window?

Llama 5 has a larger context window, supporting 5M tokens compared with 1M tokens.

종합 평가

Llama 5(프라이빗 클라우드 자체 호스팅, 파인튜닝 및 완벽한 데이터 주권 확보, 엔터프라이즈 RAG 지식베이스 및 대규모 프로덕션 API 연동에 최적.)와 Muse Spark 1.1(텍스트와 이미지의 네이티브 멀티모달 이해 및 복합 문서·차트 분석, 엔터프라이즈 RAG 지식베이스 및 대규모 프로덕션 API 연동에 최적.) 모두 뛰어난 성능을 자랑합니다. 고난도 추론과 코딩 벤치마크에서는 Llama 5가 앞서며, 토큰 생성 속도에서는 Muse Spark 1.1가 빠르고, 대규모 API 운영 비용 면에서는 Llama 5가 더 경제적입니다.

자주 묻는 질문 (FAQ)

코딩과 복잡한 추론에는 Llama 5와 Muse Spark 1.1 중 어느 모델이 더 나은가요?+

종합 지능 지수 및 코딩 벤치마크에서는 Llama 5가 우위를 보입니다. 상단의 SWE-bench 및 GPQA 점수를 함께 확인해 보세요.

Llama 5와 Muse Spark 1.1의 API 요금 차이는 얼마인가요?+

Llama 5는 100만 토큰당 입력 $0.00 / 출력 $0.00이며, Muse Spark 1.1는 입력 $3.00 / 출력 $15.00입니다. 혼합 비용 기준으로는 Llama 5가 더 저렴합니다.

어느 모델이 더 큰 컨텍스트 윈도우와 빠른 속도를 제공하나요?+

컨텍스트 길이는 Llama 5가 5M 토큰, Muse Spark 1.1가 1M 토큰를 지원하며, 출력 속도 면에서는 Muse Spark 1.1가 더 빠릅니다.

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 & Pricing Context Window AI Benchmarks Reasoning Models Tool Calling Model Context Protocol (MCP)

Llama 5 vs Muse Spark 1.1

원하는 AI 모델 선택 및 실시간 비교

Llama 5

Muse Spark 1.1

부문별 우위 모델

검증된 벤치마크 점수 비교

지연 시간(Latency) 및 응답 속도

실시간 API 토큰 비용 시뮬레이터

Llama 5 · Strengths & Key Advantages

Considerations & Trade-offs

Muse Spark 1.1 · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

기술 사양 (Specifications)

Llama 5 vs Muse Spark 1.1: 핵심 요약 문답 (FAQ)

종합 평가

자주 묻는 질문 (FAQ)

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