Head-to-Head Model Comparison

Llama 5 vs Muse Spark 1.1

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Revisado por el equipo de investigación de GetInfoUsÚltima revisión: October 2026Política editorial

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Llama 5Profile

Muse Spark 1.1Profile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

Llama 5

Modelo Base de Pesos Abiertos

Llama 5 de Meta es un modelo de IA diseñado para autoalojamiento en servidores propios, ajuste fino (fine-tuning) y soberanía de datos, con ventana de contexto de 5M, velocidad de 65 tokens/s, índice de inteligencia de 94/100 y tarifa de ac

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

IA Multimodal Nativa

Muse Spark 1.1 de Meta es un modelo de IA diseñado para comprensión multimodal nativa de texto e imagen y análisis visual de documentos, con ventana de contexto de 1M, velocidad de 105 tokens/s, índice de inteligencia de 90/100 y tarifa de

Intelligence Index90 / 100

Input Token:

$3.00 / 1M

Output Token:

$15.00 / 1M

Context Window:

1M tokens

SWE-bench Verified:

76.5%

Diferencias Clave de un Vistazo

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 →

Característica	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

¿Quién gana en cada área?

At-a-glance decision framework

Código

Muse Spark 1.1

+2.5% SWE-bench lead

Costo

Llama 5

1.0x cheaper monthly runtime

Velocidad

Muse Spark 1.1

+62% faster generation

Contexto

Llama 5

5M tokens window

Puntuaciones de Benchmarks Verificadas

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

Latencia y Tiempo de Respuesta

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

Simulador Interactivo de Costos de Tokens

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

Ventana de contexto de 5M con hasta 128K tokens de salida máxima (SWE-bench Verified 74% · GPQA Diamond 84%)
Rendimiento ágil de 65 tokens/segundo con latencia inicial (TTFT) de solo 850 ms
Pesos abiertos (600B+ total (~40-80B )) listos para autoalojamiento local o en nube privada mediante vLLM y Ollama

Considerations & Trade-offs

Requiere infraestructura GPU dedicada con suficiente memoria VRAM para inferencia local sin cuantización
En demostraciones científicas extremas, los modelos de razonamiento pesado de gama alta ofrecen mayor profundidad

Muse Spark 1.1 · Strengths & Key Advantages

Ventana de contexto de 1M con hasta 64K tokens de salida máxima (SWE-bench Verified 76.5% · GPQA Diamond 83.5%)
Rendimiento ágil de 105 tokens/segundo con latencia inicial (TTFT) de solo 600 ms
Economía de API competitiva ($3/$15 por 1M de tokens) con soporte para caché de prompts y salidas JSON estructuradas

Considerations & Trade-offs

Modelo propietario en la nube; no permite descargar los pesos para ejecución local sin conexión
En volúmenes masivos de tokens, se recomienda activar el caché de prompts y Batch API para optimizar costos

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

Especificaciones Técnicas

Architecture, release timeline, capabilities, and modalities.

Característica	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: Respuestas Rápidas (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.

Veredicto

Tanto Llama 5 (Ideal para autoalojamiento en servidores propios, ajuste fino (fine-tuning) y soberanía de datos, sistemas RAG empresariales y automatización mediante API.) como Muse Spark 1.1 (Ideal para comprensión multimodal nativa de texto e imagen y análisis visual de documentos, sistemas RAG empresariales y automatización mediante API.) destacan en producción: Llama 5 lidera en inteligencia general y programación, Muse Spark 1.1 ofrece mayor velocidad de generación de tokens y Llama 5 resulta más rentable para cargas de trabajo API de alto volumen.

Preguntas frecuentes

¿Cuál es mejor para programación y razonamiento, Llama 5 o Muse Spark 1.1?+

En las pruebas de inteligencia compuesta y resolución de código, Llama 5 obtiene la ventaja. Puedes revisar la tabla SWE-bench y GPQA arriba para comparar cada métrica.

¿Cuánto cuesta la API de Llama 5 frente a Muse Spark 1.1?+

Llama 5 cuesta $0.00 / $0.00 por millón de tokens de entrada/salida, mientras que Muse Spark 1.1 cuesta $3.00 / $15.00. En costo combinado, Llama 5 es la opción más económica.

¿Qué modelo tiene mayor ventana de contexto y menor latencia?+

Llama 5 cuenta con 5M tokens de contexto frente a 1M tokens de Muse Spark 1.1. En velocidad de inferencia, Muse Spark 1.1 ofrece un rendimiento superior.

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

Cambia y Compara Cualquier Modelo

Llama 5

Muse Spark 1.1

¿Quién gana en cada área?

Puntuaciones de Benchmarks Verificadas

Latencia y Tiempo de Respuesta

Simulador Interactivo de Costos de Tokens

Llama 5 · Strengths & Key Advantages

Considerations & Trade-offs

Muse Spark 1.1 · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Especificaciones Técnicas

Llama 5 vs Muse Spark 1.1: Respuestas Rápidas (FAQ)

Veredicto

Preguntas frecuentes

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