Head-to-Head Model Comparison

Llama 5 vs Muse Spark 1.1

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Geprüft vom GetInfoUs-ForschungsteamZuletzt geprüft: October 2026Redaktionelle Richtlinien

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Llama 5Profile

Muse Spark 1.1Profile

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Popular Matchups:

Llama 5

Open-Weights-Basismodell

Llama 5 von Meta ist ein KI-Modell für Self-Hosting im eigenen Rechenzentrum, Fine-Tuning und volle Datensouveränität mit 5M Kontextfenster, 65 Token/s Durchsatz, einem Intelligenz-Index von 94/100 und kostenlosem Open-Weights-Zugang.

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

Nativ Multimodale KI

Muse Spark 1.1 von Meta ist ein KI-Modell für nativ multimodale Verarbeitung von Text und Bild sowie visuelle Dokumentenanalyse mit 1M Kontextfenster, 105 Token/s Durchsatz, einem Intelligenz-Index von 90/100 und $3/$15 pro 1M Token.

Intelligence Index90 / 100

Input Token:

$3.00 / 1M

Output Token:

$15.00 / 1M

Context Window:

1M tokens

SWE-bench Verified:

76.5%

Wichtigste Unterschiede auf einen Blick

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

Wer gewinnt in welcher Disziplin?

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

Geprüfte Benchmark-Ergebnisse im Vergleich

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

Latenz & Antwortzeit

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

Interaktiver Token-Kosten-Simulator

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 Kontextfenster und bis zu 128K maximale Output-Token (SWE-bench Verified 74% · GPQA Diamond 84%)
Hohe Generierungsgeschwindigkeit von 65 Token/Sekunde bei nur 850 ms Erstlatenz (TTFT)
Offene Modellgewichte (600B+ total (~40-80B )) für lokales Self-Hosting und Fine-Tuning über vLLM, SGLang oder Ollama

Considerations & Trade-offs

Erfordert dedizierte GPU-Hardware mit ausreichend VRAM für den lokalen Betrieb in voller Präzision
Bei extrem komplexen mehrstufigen Forschungsfragen bieten schwere Reasoning-Spezialmodelle noch tiefere Analysen

Muse Spark 1.1 · Strengths & Key Advantages

1M Kontextfenster und bis zu 64K maximale Output-Token (SWE-bench Verified 76.5% · GPQA Diamond 83.5%)
Hohe Generierungsgeschwindigkeit von 105 Token/Sekunde bei nur 600 ms Erstlatenz (TTFT)
Effiziente API-Struktur ($3/$15 pro 1M Token) inklusive Prompt-Caching, Tool-Calling und strikter JSON-Ausgabe

Considerations & Trade-offs

Proprietäres Cloud-Modell ohne Möglichkeit zum lokalen Download der Modellgewichte
Bei hohem Anfragevolumen empfiehlt sich der Einsatz von Prompt-Caching und Batch-API zur Kostenoptimierung

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

Technische Spezifikationen

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: Schnelle Antworten (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.

Fazit

Sowohl Llama 5 (Optimal für Self-Hosting im eigenen Rechenzentrum, Fine-Tuning und volle Datensouveränität, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) als auch Muse Spark 1.1 (Optimal für nativ multimodale Verarbeitung von Text und Bild sowie visuelle Dokumentenanalyse, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) überzeugen im Produktionseinsatz: Llama 5 führt bei komplexem Reasoning und Coding, Muse Spark 1.1 liefert den höheren Token-Durchsatz und Llama 5 ist die kosteneffizienteste Wahl für große API-Workloads.

Häufig gestellte Fragen (FAQ)

Welches Modell ist besser für Coding und Reasoning – Llama 5 oder Muse Spark 1.1?+

Im Gesamt-Intelligenzindex und bei Software-Benchmarks liegt Llama 5 vorn. Prüfen Sie oben die SWE-bench- und GPQA-Werte für Ihren Anwendungsfall.

Wie unterscheiden sich die API-Preise von Llama 5 und Muse Spark 1.1?+

Llama 5 kostet $0.00 / $0.00 pro 1M Input-/Output-Tokens, Muse Spark 1.1 liegt bei $3.00 / $15.00. Beim gewichteten Gesamtpreis ist Llama 5 günstiger.

Welches Modell hat das größere Kontextfenster und die höhere Geschwindigkeit?+

Llama 5 bietet 5M Tokens Kontext gegenüber 1M Tokens bei Muse Spark 1.1. Bei der Generierungsgeschwindigkeit liegt Muse Spark 1.1 vorn.

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

Beliebige Modelle wechseln & vergleichen

Llama 5

Muse Spark 1.1

Wer gewinnt in welcher Disziplin?

Geprüfte Benchmark-Ergebnisse im Vergleich

Latenz & Antwortzeit

Interaktiver Token-Kosten-Simulator

Llama 5 · Strengths & Key Advantages

Considerations & Trade-offs

Muse Spark 1.1 · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Technische Spezifikationen

Llama 5 vs Muse Spark 1.1: Schnelle Antworten (FAQ)

Fazit

Häufig gestellte Fragen (FAQ)

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