Head-to-Head Model Comparison

Qwen3-Next 80B-A3B Instruct vs Llama 4 Scout

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Geprüft vom GetInfoUs-ForschungsteamZuletzt geprüft: October 2026Redaktionelle Richtlinien

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Qwen3-Next 80B-A3B InstructProfile

Llama 4 ScoutProfile

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5-Model Matrix Workbench

Popular Matchups:

Alibaba

Qwen3-Next 80B-A3B Instruct

Open-Weights-Basismodell

Qwen3-Next 80B-A3B Instruct von Alibaba ist ein KI-Modell für Self-Hosting im eigenen Rechenzentrum, Fine-Tuning und volle Datensouveränität mit 131K Kontextfenster, 135 Token/s Durchsatz, einem Intelligenz-Index von 78/100 und kostenlosem

Intelligence Index78 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

131.1K tokens

SWE-bench Verified:

N/A

Llama 4 Scout

Open-Weights-Basismodell

Llama 4 Scout von Meta ist ein KI-Modell für Self-Hosting im eigenen Rechenzentrum, Fine-Tuning und volle Datensouveränität mit 3.5M Kontextfenster, 95 Token/s Durchsatz, einem Intelligenz-Index von 76/100 und kostenlosem Open-Weights-Zugan

Intelligence Index76 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

3.5M tokens

SWE-bench Verified:

N/A

Wichtigste Unterschiede auf einen Blick

Both models cost the same under this workload — choose on capability, not price.
Llama 4 Scout has a larger context window (4M vs 131K tokens).
Qwen3-Next 80B-A3B Instruct is faster at 135 tok/s vs 95 tok/s.

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

Feature	Qwen3-Next 80B-A3B Instruct	Llama 4 Scout
Intelligence Index Composite reasoning & knowledge score (0-100)	78	76
Provider	Alibaba	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	131.1K tokens~197 A4 pages of text	3.5M tokens~5,250 A4 pages of text
Max Output Tokens Per single request	8K	33K
Throughput Speed Tokens generated per second	135 tok/s	95 tok/s
SWE-bench Verified Real-world software engineering	N/A	N/A
GPQA Diamond Graduate-level reasoning	60%	N/A

Wer gewinnt in welcher Disziplin?

At-a-glance decision framework

Coding

Evaluations ongoing

Cost

Tied

Identical monthly runtime cost

Speed

Qwen3-Next 80B-A3B Instruct

+42% faster generation

Context

Llama 4 Scout

3.5M tokens window

Geprüfte Benchmark-Ergebnisse im Vergleich

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

Qwen3-Next 80B-A3B InstructLlama 4 Scout

GPQA Diamond (Doctorate-Level Science & Math)

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

60%vsN/A

MMLU-Pro (Multi-task Language Understanding)

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

78%vs78%

LMSYS Chatbot Arena Elo

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

1315ptsvs1370pts

Long-Context Reasoning

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

N/Avs92.5%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

135 tok/s vs95 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

Qwen3-Next 80B-A3B Instruct0.31s

Llama 4 Scout0.65s

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

Qwen3-Next 80B-A3B Instruct4.01s

Llama 4 Scout5.91s

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)

Qwen3-Next 80B-A3B Instruct Monthly API Bill

$0.00 / month

$0/1M in · $0/1M out

Llama 4 Scout 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

Qwen3-Next 80B-A3B Instruct · Strengths & Key Advantages

131K Kontextfenster und bis zu 8K maximale Output-Token (GPQA Diamond 60% · MMLU-Pro 78%)
Hohe Generierungsgeschwindigkeit von 135 Token/Sekunde bei nur 310 ms Erstlatenz (TTFT)
Offene Modellgewichte (80B total (3B 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 4 Scout · Strengths & Key Advantages

3.5M Kontextfenster und bis zu 33K maximale Output-Token (MMLU-Pro 78% · Arena Elo 1370)
Hohe Generierungsgeschwindigkeit von 95 Token/Sekunde bei nur 650 ms Erstlatenz (TTFT)
Offene Modellgewichte (17B (109B 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

Token Use & Task Economics

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

Metric	Qwen3-Next 80B-A3B Instruct	Llama 4 Scout
Cost per Task Estimated USD to finish one task	$0.00	$0.00
Output Tokens / Task Tokens generated per task	15K	10K
Reasoning Tokens / Task Thinking tokens burned per task	0K	0K

Technische Spezifikationen

Architecture, release timeline, capabilities, and modalities.

Feature	Qwen3-Next 80B-A3B Instruct	Llama 4 Scout
Official API Model ID (SDK)	qwen3-next-80b-a3b-instruct-instruct	meta-llama/Llama-4-Scout
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date	2025-09	2025-04
Knowledge Cutoff	August 2025	January 2025
Architecture	Standard Instruction-Tuned Sparse Mixture-of-Experts	Extreme-context sparse MoE multimodal transformer (17B active / 109B total)
Parameters	80B total (3B active MoE)	17B (109B total MoE)
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	API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)	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)	Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)
Quantization & Min GPU VRAM	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 50 GB VRAM (4-bit Quantized) · 172 GB VRAM (Native BF16)	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 11 GB VRAM (4-bit Quantized) · 37 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	text, vision
Output Modalities	text	text

Qwen3-Next 80B-A3B Instruct vs Llama 4 Scout: Schnelle Antworten (FAQ)

Which is more intelligent, Qwen3-Next 80B-A3B Instruct or Llama 4 Scout?

Qwen3-Next 80B-A3B Instruct is more intelligent. It scores 78 compared with 76 on the composite Intelligence Index.

Which model is faster?

Qwen3-Next 80B-A3B Instruct is faster, generating 135 tokens per second compared with 95 tokens per second.

Which model has lower latency?

Qwen3-Next 80B-A3B Instruct has lower latency, with a time to first token of 0.31s compared with 0.65s.

Which model has a larger context window?

Llama 4 Scout has a larger context window, supporting 3.5M tokens compared with 131.1K tokens.

Fazit

Sowohl Qwen3-Next 80B-A3B Instruct (Optimal für Self-Hosting im eigenen Rechenzentrum, Fine-Tuning und volle Datensouveränität, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) als auch Llama 4 Scout (Optimal für Self-Hosting im eigenen Rechenzentrum, Fine-Tuning und volle Datensouveränität, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) überzeugen im Produktionseinsatz: Qwen3-Next 80B-A3B Instruct führt bei komplexem Reasoning und Coding, Qwen3-Next 80B-A3B Instruct liefert den höheren Token-Durchsatz und Qwen3-Next 80B-A3B Instruct ist die kosteneffizienteste Wahl für große API-Workloads.

Häufig gestellte Fragen (FAQ)

Welches Modell ist besser für Coding und Reasoning – Qwen3-Next 80B-A3B Instruct oder Llama 4 Scout?+

Im Gesamt-Intelligenzindex und bei Software-Benchmarks liegt Qwen3-Next 80B-A3B Instruct vorn. Prüfen Sie oben die SWE-bench- und GPQA-Werte für Ihren Anwendungsfall.

Wie unterscheiden sich die API-Preise von Qwen3-Next 80B-A3B Instruct und Llama 4 Scout?+

Qwen3-Next 80B-A3B Instruct kostet $0.00 / $0.00 pro 1M Input-/Output-Tokens, Llama 4 Scout liegt bei $0.00 / $0.00. Beim gewichteten Gesamtpreis ist Qwen3-Next 80B-A3B Instruct günstiger.

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

Qwen3-Next 80B-A3B Instruct bietet 131.1K Tokens Kontext gegenüber 3.5M Tokens bei Llama 4 Scout. Bei der Generierungsgeschwindigkeit liegt Qwen3-Next 80B-A3B Instruct 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)

Qwen3-Next 80B-A3B Instruct vs Llama 4 Scout

Beliebige Modelle wechseln & vergleichen

Qwen3-Next 80B-A3B Instruct

Llama 4 Scout

Wer gewinnt in welcher Disziplin?

Geprüfte Benchmark-Ergebnisse im Vergleich

Latenz & Antwortzeit

Interaktiver Token-Kosten-Simulator

Qwen3-Next 80B-A3B Instruct · Strengths & Key Advantages

Considerations & Trade-offs

Llama 4 Scout · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Technische Spezifikationen

Qwen3-Next 80B-A3B Instruct vs Llama 4 Scout: Schnelle Antworten (FAQ)

Fazit

Häufig gestellte Fragen (FAQ)

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