Head-to-Head Model Comparison

Llama 4 Maverick vs Llama 3.3 70B

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Geprüft vom GetInfoUs-ForschungsteamZuletzt geprüft: October 2026Redaktionelle Richtlinien

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Llama 4 MaverickProfile

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Llama 4 Maverick

Nativ Multimodale KI

Llama 4 Maverick von Meta ist ein KI-Modell für nativ multimodale Verarbeitung von Text und Bild sowie visuelle Dokumentenanalyse mit 1M Kontextfenster, 85 Token/s Durchsatz, einem Intelligenz-Index von 84/100 und kostenlosem Open-Weights-Z

Intelligence Index84 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

1M tokens

SWE-bench Verified:

62%

Llama 3.3 70B

Open-Weights-Basismodell

Llama 3.3 70B von Meta ist ein KI-Modell für Self-Hosting im eigenen Rechenzentrum, Fine-Tuning und volle Datensouveränität mit 128K Kontextfenster, 280 Token/s Durchsatz, einem Intelligenz-Index von 38/100 und kostenlosem Open-Weights-Zuga

Intelligence Index38 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

128K tokens

SWE-bench Verified:

39.5%

Wichtigste Unterschiede auf einen Blick

Llama 4 Maverick is 1.0x cheaper for API usage (input + output tokens combined).
Llama 4 Maverick has a larger context window (1M vs 128K tokens).
Llama 3.3 70B is faster at 280 tok/s vs 85 tok/s.
Llama 4 Maverick leads on SWE-bench coding (62.0% vs 39.5%).

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

Feature	Llama 4 Maverick	Llama 3.3 70B
Intelligence Index Composite reasoning & knowledge score (0-100)	84	38
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	$0.07 / 1M
Context Window Maximum input + output tokens	1M tokens~1,500 A4 pages of text	128K tokens~192 A4 pages of text
Max Output Tokens Per single request	33K	4K
Throughput Speed Tokens generated per second	85 tok/s	280 tok/s
SWE-bench Verified Real-world software engineering	62%	39.5%
GPQA Diamond Graduate-level reasoning	72.5%	48.9%
Cached Input Price Prompt caching discount per 1M tokens	$0.00 / 1M	$0.10 / 1M

Wer gewinnt in welcher Disziplin?

At-a-glance decision framework

Coding

Llama 4 Maverick

+22.5% SWE-bench lead

Cost

Llama 4 Maverick

1.0x cheaper monthly runtime

Speed

Llama 3.3 70B

+229% faster generation

Context

Llama 4 Maverick

1M tokens window

Geprüfte Benchmark-Ergebnisse im Vergleich

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

Llama 4 MaverickLlama 3.3 70B

SWE-bench Verified (Autonomous Coding)

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

62%vs39.5%

GPQA Diamond (Doctorate-Level Science & Math)

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

72.5%vs48.9%

MMLU-Pro (Multi-task Language Understanding)

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

80.5%vs71%

LMSYS Chatbot Arena Elo

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

1415ptsvs1285pts

Humanity's Last Exam (Frontier Expert Knowledge)

Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.

N/Avs7.8%

Terminal-Bench (Agentic Coding & Terminal Use)

Autonomous multi-step tasks executed inside a real command-line terminal environment.

N/Avs26.1%

SciCode (Scientific Computing)

Research-grade coding problems drawn from real scientific and numerical workloads.

N/Avs23.7%

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

N/Avs42.5%

AIME (Advanced Math Competition)

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

N/Avs56.2%

Long-Context Reasoning

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

N/Avs63.9%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

85 tok/s vs280 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 Maverick0.55s

Llama 3.3 70B0.25s

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

Llama 4 Maverick6.43s

Llama 3.3 70B2.04s

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 Maverick Monthly API Bill

$0.00 / month

$0/1M in · $0/1M out

Llama 3.3 70B Monthly API Bill

$0.15 / month

$0/1M in · $0/1M out

Llama 4 Maverick saves approximately $0.15 / month (100%) under this workload.1.0x Savings

Llama 4 Maverick · Strengths & Key Advantages

1M Kontextfenster und bis zu 33K maximale Output-Token (SWE-bench Verified 62% · GPQA Diamond 72.5%)
Hohe Generierungsgeschwindigkeit von 85 Token/Sekunde bei nur 550 ms Erstlatenz (TTFT)
Offene Modellgewichte (17B (400B total 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 3.3 70B · Strengths & Key Advantages

128K Kontextfenster und bis zu 4K maximale Output-Token (SWE-bench Verified 39.5% · GPQA Diamond 48.9%)
Hohe Generierungsgeschwindigkeit von 280 Token/Sekunde bei nur 250 ms Erstlatenz (TTFT)
Offene Modellgewichte (70B) 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 Maverick	Llama 3.3 70B
Cost per Task Estimated USD to finish one task	$0.00	$0.21
Output Tokens / Task Tokens generated per task	15K	45K
Reasoning Tokens / Task Thinking tokens burned per task	0K	0K

Technische Spezifikationen

Architecture, release timeline, capabilities, and modalities.

Feature	Llama 4 Maverick	Llama 3.3 70B
Official API Model ID (SDK)	meta-llama/Llama-4-Maverick	meta-llama/Llama-3.3-70B
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date	2025-04	2024-12
Knowledge Cutoff	January 2025	December 2023
Architecture	High-capacity sparse MoE multimodal transformer (17B active / 400B total)	Dense Autoregressive Transformer
Parameters	17B (400B total MoE)	70B
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)	API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)
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)
Quantization & Min GPU VRAM	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 11 GB VRAM (4-bit Quantized) · 37 GB VRAM (Native BF16)	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 43 GB VRAM (4-bit Quantized) · 151 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
Output Modalities	text	text

Llama 4 Maverick vs Llama 3.3 70B: Schnelle Antworten (FAQ)

Which is more intelligent, Llama 4 Maverick or Llama 3.3 70B?

Llama 4 Maverick is more intelligent. It scores 84 compared with 38 on the composite Intelligence Index.

Which model is faster?

Llama 3.3 70B is faster, generating 280 tokens per second compared with 85 tokens per second.

Which model is cheaper to run?

Llama 4 Maverick is cheaper at $0.00 per 1M tokens blended, compared with $0.07 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Llama 3.3 70B has lower latency, with a time to first token of 0.25s compared with 0.55s.

Which model has a larger context window?

Llama 4 Maverick has a larger context window, supporting 1M tokens compared with 128K tokens.

Fazit

Sowohl Llama 4 Maverick (Optimal für nativ multimodale Verarbeitung von Text und Bild sowie visuelle Dokumentenanalyse, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) als auch Llama 3.3 70B (Optimal für Self-Hosting im eigenen Rechenzentrum, Fine-Tuning und volle Datensouveränität, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) überzeugen im Produktionseinsatz: Llama 4 Maverick führt bei komplexem Reasoning und Coding, Llama 3.3 70B liefert den höheren Token-Durchsatz und Llama 4 Maverick ist die kosteneffizienteste Wahl für große API-Workloads.

Häufig gestellte Fragen (FAQ)

Welches Modell ist besser für Coding und Reasoning – Llama 4 Maverick oder Llama 3.3 70B?+

Im Gesamt-Intelligenzindex und bei Software-Benchmarks liegt Llama 4 Maverick vorn. Prüfen Sie oben die SWE-bench- und GPQA-Werte für Ihren Anwendungsfall.

Wie unterscheiden sich die API-Preise von Llama 4 Maverick und Llama 3.3 70B?+

Llama 4 Maverick kostet $0.00 / $0.00 pro 1M Input-/Output-Tokens, Llama 3.3 70B liegt bei $0.00 / $0.00. Beim gewichteten Gesamtpreis ist Llama 4 Maverick günstiger.

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

Llama 4 Maverick bietet 1M Tokens Kontext gegenüber 128K Tokens bei Llama 3.3 70B. Bei der Generierungsgeschwindigkeit liegt Llama 3.3 70B 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 4 Maverick vs Llama 3.3 70B

Beliebige Modelle wechseln & vergleichen

Llama 4 Maverick

Llama 3.3 70B

Wer gewinnt in welcher Disziplin?

Geprüfte Benchmark-Ergebnisse im Vergleich

Latenz & Antwortzeit

Interaktiver Token-Kosten-Simulator

Llama 4 Maverick · Strengths & Key Advantages

Considerations & Trade-offs

Llama 3.3 70B · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Technische Spezifikationen

Llama 4 Maverick vs Llama 3.3 70B: Schnelle Antworten (FAQ)

Fazit

Häufig gestellte Fragen (FAQ)

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