Head-to-Head Model Comparison

Llama 4 Behemoth vs Llama 5

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Geprüft vom GetInfoUs-ForschungsteamZuletzt geprüft: October 2026Redaktionelle Richtlinien

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Llama 4 BehemothProfile

Llama 5Profile

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Llama 4 Behemoth

Open-Weights-Basismodell

Llama 4 Behemoth von Meta ist ein KI-Modell für Self-Hosting im eigenen Rechenzentrum, Fine-Tuning und volle Datensouveränität mit 10M Kontextfenster, 45 Token/s Durchsatz, einem Intelligenz-Index von 93/100 und kostenlosem Open-Weights-Zug

Intelligence Index93 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

10M tokens

SWE-bench Verified:

75%

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%

Wichtigste Unterschiede auf einen Blick

Both models cost the same under this workload — choose on capability, not price.
Llama 4 Behemoth has a larger context window (10M vs 5M tokens).
Llama 5 is faster at 65 tok/s vs 45 tok/s.
Llama 4 Behemoth leads on SWE-bench coding (75.0% vs 74.0%).

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

Feature	Llama 4 Behemoth	Llama 5
Intelligence Index Composite reasoning & knowledge score (0-100)	93	94
Provider	Meta	Meta
Input Price Cost per million input tokens	$0.00 / 1M	$0.00 / 1M
Output Price Cost per million output tokens	$0.00 / 1M	$0.00 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.00 / 1M	$0.00 / 1M
Context Window Maximum input + output tokens	10M tokens~15,000 A4 pages of text	5M tokens~7,500 A4 pages of text
Max Output Tokens Per single request	64K	128K
Throughput Speed Tokens generated per second	45 tok/s	65 tok/s
SWE-bench Verified Real-world software engineering	75%	74%
GPQA Diamond Graduate-level reasoning	82%	84%

Wer gewinnt in welcher Disziplin?

At-a-glance decision framework

Coding

Llama 4 Behemoth

+1.0% SWE-bench lead

Cost

Tied

Identical monthly runtime cost

Speed

Llama 5

+44% faster generation

Context

Llama 4 Behemoth

10M tokens window

Geprüfte Benchmark-Ergebnisse im Vergleich

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

Llama 4 BehemothLlama 5

SWE-bench Verified (Autonomous Coding)

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

75%vs74%

GPQA Diamond (Doctorate-Level Science & Math)

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

82%vs84%

MMLU-Pro (Multi-task Language Understanding)

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

88.5%vs87%

LMSYS Chatbot Arena Elo

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

1490ptsvs1485pts

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

N/Avs68%

AIME (Advanced Math Competition)

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

N/Avs88%

Long-Context Reasoning

Accuracy when reasoning over very large inputs that fill the model context window.

95%vsN/A

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

45 tok/s vs65 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 4 Behemoth1.20s

Llama 50.85s

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

Llama 4 Behemoth12.31s

Llama 58.54s

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 4 Behemoth Monthly API Bill

$0.00 / month

$0/1M in · $0/1M out

Llama 5 Monthly API Bill

$0.00 / month

$0/1M in · $0/1M out

These models cost the same under this workload — no savings either way.Break-even

Llama 4 Behemoth · Strengths & Key Advantages

10M Kontextfenster und bis zu 64K maximale Output-Token (SWE-bench Verified 75% · GPQA Diamond 82%)
Hohe Generierungsgeschwindigkeit von 45 Token/Sekunde bei nur 1200 ms Erstlatenz (TTFT)
Offene Modellgewichte (2T total / 288B (16-of-2 MoE)) 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

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

Token Use & Task Economics

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

Metric	Llama 4 Behemoth	Llama 5
Cost per Task Estimated USD to finish one task	$0.00	$0.00
Output Tokens / Task Tokens generated per task	35K	40K
Reasoning Tokens / Task Thinking tokens burned per task	15K	20K

Technische Spezifikationen

Architecture, release timeline, capabilities, and modalities.

Llama 4 Behemoth vs Llama 5: Schnelle Antworten (FAQ)

Which is more intelligent, Llama 4 Behemoth or Llama 5?

Llama 5 is more intelligent. It scores 94 compared with 93 on the composite Intelligence Index.

Which model is faster?

Llama 5 is faster, generating 65 tokens per second compared with 45 tokens per second.

Which model has lower latency?

Llama 5 has lower latency, with a time to first token of 0.85s compared with 1.20s.

Which model has a larger context window?

Llama 4 Behemoth has a larger context window, supporting 10M tokens compared with 5M tokens.

Fazit

Sowohl Llama 4 Behemoth (Optimal für Self-Hosting im eigenen Rechenzentrum, Fine-Tuning und volle Datensouveränität, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) als auch Llama 5 (Optimal für Self-Hosting im eigenen Rechenzentrum, Fine-Tuning und volle Datensouveränität, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) überzeugen im Produktionseinsatz: Llama 5 führt bei komplexem Reasoning und Coding, Llama 5 liefert den höheren Token-Durchsatz und Llama 4 Behemoth ist die kosteneffizienteste Wahl für große API-Workloads.

Häufig gestellte Fragen (FAQ)

Welches Modell ist besser für Coding und Reasoning – Llama 4 Behemoth oder Llama 5?+

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 4 Behemoth und Llama 5?+

Llama 4 Behemoth kostet $0.00 / $0.00 pro 1M Input-/Output-Tokens, Llama 5 liegt bei $0.00 / $0.00. Beim gewichteten Gesamtpreis ist Llama 4 Behemoth günstiger.

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

Llama 4 Behemoth bietet 10M Tokens Kontext gegenüber 5M Tokens bei Llama 5. Bei der Generierungsgeschwindigkeit liegt Llama 5 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)

Feature	Llama 4 Behemoth	Llama 5
Official API Model ID (SDK)	meta-llama/Llama-4-Behemoth	meta-llama/Llama-5
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date	2026-05	2026-04
Knowledge Cutoff	February 2026	January 2026
Architecture	Colossal sparse MoE architecture (288B active / 2T total, 16-of-2 MoE)	Mixture-of-Experts with recursive self-improvement (~600B total, 40-80B active)
Parameters	2T total / 288B active (16-of-2 MoE)	600B+ total (~40-80B active)
Enterprise Rate Limits (RPM / TPM)	Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud	Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud
Long-Context Tier (>128K/200K)	Free across full context window (only KV-cache VRAM scales)	Free across full context window (only KV-cache VRAM scales)
Prompt Cache Policy & TTL	Automatic prefix KV-caching in vLLM / SGLang (0 cost)	Automatic prefix KV-caching in vLLM / SGLang (0 cost)
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)	Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)
Multimodal Token Billing	Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)	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) · 1240 GB VRAM (4-bit Quantized) · 4300 GB VRAM (Native BF16)	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 372 GB VRAM (4-bit Quantized) · 1290 GB VRAM (Native BF16)
Safety & Frontier Classification	Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma)	Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma)
Enterprise Compliance (ZDR/HIPAA/SOC2)	100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control	100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control
Input Modalities	text, vision	text, vision, video, audio
Output Modalities	text	text

Llama 4 Behemoth vs Llama 5

Beliebige Modelle wechseln & vergleichen

Llama 4 Behemoth

Llama 5

Wer gewinnt in welcher Disziplin?

Geprüfte Benchmark-Ergebnisse im Vergleich

Latenz & Antwortzeit

Interaktiver Token-Kosten-Simulator

Llama 4 Behemoth · Strengths & Key Advantages

Considerations & Trade-offs

Llama 5 · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Technische Spezifikationen

Llama 4 Behemoth vs Llama 5: Schnelle Antworten (FAQ)

Fazit

Häufig gestellte Fragen (FAQ)

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