Llama 5 vs Muse Spark 1.1
Benchmarks, API Token Pricing, Context Window & Speed Comparison
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Llama 5 by Meta and Muse Spark 1.1 by Meta 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, Llama 5 holds a notable pricing advantage for token ingestion. On output generation, Llama 5 is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, Llama 5 provides an expanded memory horizon with 5,000K tokens. In latency-sensitive production environments, Muse Spark 1.1 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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Llama 5
오픈 웨이트 모델Meta의 Llama 5 모델은 5M 토큰 컨텍스트 윈도우와 입력 $0/1M 토큰, 출력 $0/1M 토큰의 합리적인 API 요금을 제공하며 코딩, 추론 및 엔터프라이즈 자동화에 최적화되어 있습니다.
$0.00 / 1M
$0.00 / 1M
5M tokens
74%
Muse Spark 1.1
심층 추론 모델Meta의 Muse Spark 1.1 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
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%).
| 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 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.
Contamination-free competitive programming problems collected continuously over time.
American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.
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.
Llama 5 · Strengths & Key Advantages
- Meta의 첨단 Mixture-of-Experts with recursive self-improvement (~600B total, 40-80B active) 아키텍처와 최대 5M 토큰 컨텍스트 지원
- 경쟁력 있는 API 가격(입력 $0/1M, 출력 $0/1M)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
Muse Spark 1.1 · Strengths & Key Advantages
- Meta의 첨단 Omni-modal native thinking agent architecture 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
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
Verdict & Model Selection Recommendation: Choose Llama 5 if your priority is Autonomous open-source multi-agent systems, deep software refactoring, and frontier research. Its performance in demanding tasks justifies its operational token cost. Choose Muse Spark 1.1 if your workflow prioritizes Enterprise multimodal agent systems, UI analysis, and complex collaborative task execution. 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 Llama 5 compare to Muse Spark 1.1 in API token pricing and cost per token?+
Llama 5 costs $0.00 per 1M input tokens and $0.00 per 1M output tokens, whereas Muse Spark 1.1 costs $3.00 input and $15.00 output. Overall, Llama 5 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: Llama 5 or Muse Spark 1.1?+
Llama 5 supports a context window of 5,000K tokens (~3,750k words), while Muse Spark 1.1 supports 1,000K tokens (~750k words). Therefore, Llama 5 can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: Llama 5 or Muse Spark 1.1?+
Llama 5 scores 74% on SWE-bench Verified while Muse Spark 1.1 scores 76.5%. For autonomous software engineering and multi-file debugging, Muse Spark 1.1 holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between Llama 5 and Muse Spark 1.1?+
Llama 5 features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Muse Spark 1.1 provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between Llama 5 and Muse Spark 1.1?+
Llama 5 generates approximately 65 tokens per second, while Muse Spark 1.1 delivers 105 tokens per second. In latency-sensitive production environments, Muse Spark 1.1 provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Llama 5 and Muse Spark 1.1 to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Llama 5 to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to Muse Spark 1.1. This tiered routing pattern cuts overall API bills by up to 70%.
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: GetInfoUs Independent AI Benchmarks & Official API Specs