Head-to-Head Model Comparison

Llama 5 vs Muse Spark 1.1

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Verificato dal team di ricerca di GetInfoUsUltima verifica: October 2026Politica editoriale

Cambia e Confronta Qualsiasi Modello

Select any AI models from our verified benchmark database to generate live side-by-side matrices.

Llama 5Profile

Muse Spark 1.1Profile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

Llama 5

Modello Base a Pesi Aperti

Llama 5 di Meta è un modello di IA progettato per il self-hosting su infrastruttura privata, il fine-tuning e la piena sovranità dei dati, con finestra di contesto da 5M, velocità di 65 token/s, indice di intelligenza di 94/100 e tariffa di

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 Multimodale Nativa

Muse Spark 1.1 di Meta è un modello di IA progettato per la comprensione multimodale nativa di testo e immagini e l’analisi visiva dei documenti, con finestra di contesto da 1M, velocità di 105 token/s, indice di intelligenza di 90/100 e ta

Intelligence Index90 / 100

Input Token:

$3.00 / 1M

Output Token:

$15.00 / 1M

Context Window:

1M tokens

SWE-bench Verified:

76.5%

Differenze Chiave a Colpo d’Occhio

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

Chi vince in ogni categoria?

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

Punteggi Benchmark Verificati

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

Latenza e Tempo di Risposta

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

Simulatore Interattivo dei Costi Token

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

Finestra di contesto da 5M con capacità massima di output fino a 128K token (SWE-bench Verified 74% · GPQA Diamond 84%)
Throughput elevato di 65 token/secondo con latenza al primo token (TTFT) di soli 850 ms
Pesi aperti (600B+ total (~40-80B )) pronti per il self-hosting locale o su cloud privato tramite vLLM, SGLang o Ollama

Considerations & Trade-offs

Richiede server GPU dedicati con sufficiente memoria VRAM per l’esecuzione locale a piena precisione
Nelle dimostrazioni scientifiche più estreme, i modelli di ragionamento pesante dedicati offrono maggiore profondità

Muse Spark 1.1 · Strengths & Key Advantages

Finestra di contesto da 1M con capacità massima di output fino a 64K token (SWE-bench Verified 76.5% · GPQA Diamond 83.5%)
Throughput elevato di 105 token/secondo con latenza al primo token (TTFT) di soli 600 ms
Economia API competitiva ($3/$15 per 1M di token) con supporto per prompt caching, tool calling e output JSON strutturati

Considerations & Trade-offs

Modello cloud proprietario chiuso; non consente il download locale dei pesi per l’esecuzione offline
Per volumi di traffico elevati in produzione si consiglia di attivare il prompt caching e la Batch API

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

Specifiche Tecniche

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: Risposte Rapide (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.

Verdetto

Sia Llama 5 (Ideale per il self-hosting su infrastruttura privata, il fine-tuning e la piena sovranità dei dati, sistemi RAG aziendali e integrazioni API scalabili.) sia Muse Spark 1.1 (Ideale per la comprensione multimodale nativa di testo e immagini e l’analisi visiva dei documenti, sistemi RAG aziendali e integrazioni API scalabili.) eccellono in produzione: Llama 5 guida nei benchmark di ragionamento e programmazione, Muse Spark 1.1 garantisce una generazione più rapida e Llama 5 offre il miglior rapporto qualità-prezzo per carichi API elevati.

Domande Frequenti (FAQ)

Quale modello è migliore per coding e ragionamento, Llama 5 o Muse Spark 1.1?+

Nell’indice di intelligenza composito e nei benchmark di programmazione prevale Llama 5. Consulta i punteggi SWE-bench e GPQA qui sopra per tutti i dettagli.

Qual è la differenza di prezzo API tra Llama 5 e Muse Spark 1.1?+

Llama 5 costa $0.00 / $0.00 per milione di token di input/output, mentre Muse Spark 1.1 costa $3.00 / $15.00. Nel costo combinato, Llama 5 risulta più economico.

Quale modello ha la finestra di contesto più ampia e la velocità maggiore?+

Llama 5 supporta 5M token di contesto contro 1M token di Muse Spark 1.1, mentre Muse Spark 1.1 offre una velocità di generazione superiore.

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 e Confronta Qualsiasi Modello

Llama 5

Muse Spark 1.1

Chi vince in ogni categoria?

Punteggi Benchmark Verificati

Latenza e Tempo di Risposta

Simulatore Interattivo dei Costi Token

Llama 5 · Strengths & Key Advantages

Considerations & Trade-offs

Muse Spark 1.1 · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Specifiche Tecniche

Llama 5 vs Muse Spark 1.1: Risposte Rapide (FAQ)

Verdetto

Domande Frequenti (FAQ)

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