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Head-to-Head Model Comparison

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

Benchmarks, API Token Pricing, Context Window & Speed Comparison

GetInfoUs araştırma ekibi tarafından incelendiSon inceleme: October 2026Yayın ilkeleri
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AI CompareAI BenchmarkToken PricingModel CostModel SpecsSWE-bench CodingModel VersusWhich AI ModelLLM Speed & LatencyReasoning ModelsExtended ThinkingPrompt Caching ROI
Qwen3-Next 80B-A3B Instruct vs Llama 4 Scout: Benchmarks, Pricing & Speed Comparison

Alibaba tarafından geliştirilen Qwen3-Next 80B-A3B Instruct ve Meta tarafından sunulan Llama 4 Scout, kendi sınıflarının öne çıkan yapay zeka modelleridir. Qwen3-Next 80B-A3B Instruct, 131.1K token bağlam penceresi ve 1M token başına $0.00/$0.00 fiyat sunarken; Llama 4 Scout, 3.5M token bağlam penceresi ve $0.00/$0.00 fiyatla gelir. Aşağıda tüm benchmark, hız ve maliyet detaylarını inceleyebilirsiniz.

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Alibaba

Qwen3-Next 80B-A3B Instruct

Açık Ağırlıklı Temel Model

Alibaba tarafından geliştirilen Qwen3-Next 80B-A3B Instruct; özel sunucuda kendi kendine barındırma (self-hosting), ince ayar ve tam veri egemenliği için tasarlanmış olup 131K bağlam penceresi, 135 token/sn hız, 78/100 zeka indeksi ve ücret

Intelligence Index78 / 100
Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

131.1K tokens

SWE-bench Verified:

N/A

Meta

Llama 4 Scout

Açık Ağırlıklı Temel Model

Meta tarafından geliştirilen Llama 4 Scout; özel sunucuda kendi kendine barındırma (self-hosting), ince ayar ve tam veri egemenliği için tasarlanmış olup 3.5M bağlam penceresi, 95 token/sn hız, 76/100 zeka indeksi ve ücretsiz açık ağırlık e

Intelligence Index76 / 100
Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

3.5M tokens

SWE-bench Verified:

N/A

Bir Bakışta Temel Farklar

  • 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 →
FeatureQwen3-Next 80B-A3B InstructLlama 4 Scout
Intelligence Index
Composite reasoning & knowledge score (0-100)
7876
Provider
AlibabaMeta
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 text3.5M tokens~5,250 A4 pages of text
Max Output Tokens
Per single request
8K33K
Throughput Speed
Tokens generated per second
135 tok/s95 tok/s
SWE-bench Verified
Real-world software engineering
N/AN/A
GPQA Diamond
Graduate-level reasoning
60%N/A

Hangi Alanda Kim Kazanıyor?

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

Doğrulanmış Benchmark Puanları

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

Gecikme Süresi ve Yanıt Hızı

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

Gerçek Zamanlı Token Maliyet Simülatörü

Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.

100K / day
10K500K1M
30K / day
5K150K300K
50% 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 bağlam penceresi ve yanıt başına maksimum 8K çıktı tokenı kapasitesi (GPQA Diamond 60% · MMLU-Pro 78%)
  • Sadece 310 ms ilk token gecikmesi (TTFT) ile saniyede 135 token yüksek üretim hızı
  • vLLM, SGLang veya Ollama üzerinden yerel GPU sunucularında barındırılabilen açık ağırlıklı mimari (80B total (3B MoE))
Considerations & Trade-offs
  • Tam hassasiyette yerel GPU çıkarımı için yeterli VRAM kapasitesine sahip donanım altyapısı gerektirir
  • Çok karmaşık akademik ve bilimsel çıkarımlarda üst düzey ağır akıl yürütme modelleri daha derin sonuçlar verebilir

Llama 4 Scout · Strengths & Key Advantages

  • 3.5M bağlam penceresi ve yanıt başına maksimum 33K çıktı tokenı kapasitesi (MMLU-Pro 78% · Arena Elo 1370)
  • Sadece 650 ms ilk token gecikmesi (TTFT) ile saniyede 95 token yüksek üretim hızı
  • vLLM, SGLang veya Ollama üzerinden yerel GPU sunucularında barındırılabilen açık ağırlıklı mimari (17B (109B total MoE))
Considerations & Trade-offs
  • Tam hassasiyette yerel GPU çıkarımı için yeterli VRAM kapasitesine sahip donanım altyapısı gerektirir
  • Çok karmaşık akademik ve bilimsel çıkarımlarda üst düzey ağır akıl yürütme modelleri daha derin sonuçlar verebilir

Token Use & Task Economics

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

MetricQwen3-Next 80B-A3B InstructLlama 4 Scout
Cost per Task
Estimated USD to finish one task
$0.00$0.00
Output Tokens / Task
Tokens generated per task
15K10K
Reasoning Tokens / Task
Thinking tokens burned per task
0K0K

Teknik Özellikler

Architecture, release timeline, capabilities, and modalities.

FeatureQwen3-Next 80B-A3B InstructLlama 4 Scout
Official API Model ID (SDK)qwen3-next-80b-a3b-instruct-instructmeta-llama/Llama-4-Scout
API ProtocolOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGIOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date2025-092025-04
Knowledge CutoffAugust 2025January 2025
ArchitectureStandard Instruction-Tuned Sparse Mixture-of-ExpertsExtreme-context sparse MoE multimodal transformer (17B active / 109B total)
Parameters80B 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 CloudUnlimited (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 & TTLAutomatic prefix KV-caching in vLLM / SGLang (0 cost)Automatic prefix KV-caching in vLLM / SGLang (0 cost)
Computer Use / GUI AgentAPI / 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 CallingNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON SchemaNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema
Web Search Grounding & SandboxSupported via external Tavily/Exa Search & E2B/Docker Sandbox toolsSupported via external Tavily/Exa Search & E2B/Docker Sandbox tools
Official Fine-Tuning SupportFull 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 BillingText BPE (~4 chars/token)Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)
Quantization & Min GPU VRAMBF16 / 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 ClassificationOpen-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 Control100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control
Input Modalitiestexttext, vision
Output Modalitiestexttext

Qwen3-Next 80B-A3B Instruct vs Llama 4 Scout: Hızlı Yanıtlar (SSS)

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.

Sonuç

Hem Qwen3-Next 80B-A3B Instruct (özel sunucuda kendi kendine barındırma (self-hosting), ince ayar ve tam veri egemenliği, kurumsal RAG mimarileri ve ölçeklenebilir üretim API entegrasyonları için idealdir.) hem de Llama 4 Scout (özel sunucuda kendi kendine barındırma (self-hosting), ince ayar ve tam veri egemenliği, kurumsal RAG mimarileri ve ölçeklenebilir üretim API entegrasyonları için idealdir.) güçlü üretim seçenekleridir: Qwen3-Next 80B-A3B Instruct karmaşık akıl yürütme ve kodlama testlerinde öne çıkarken, Qwen3-Next 80B-A3B Instruct daha yüksek üretim hızı sağlar; Qwen3-Next 80B-A3B Instruct ise yüksek hacimli API kullanımlarında daha ekonomiktir.

Sıkça Sorulan Sorular (SSS)

Kodlama ve akıl yürütme için Qwen3-Next 80B-A3B Instruct mi yoksa Llama 4 Scout mi daha iyi?+

Bileşik zeka endeksi ve yazılım testlerinde Qwen3-Next 80B-A3B Instruct önde yer alıyor. Detaylı karşılaştırma için yukarıdaki SWE-bench ve GPQA tablolarına göz atabilirsiniz.

Qwen3-Next 80B-A3B Instruct ve Llama 4 Scout API fiyatları nasıl karşılaştırılır?+

Qwen3-Next 80B-A3B Instruct 1M giriş/çıkış tokeni başına $0.00 / $0.00 ücretlendirilirken, Llama 4 Scout $0.00 / $0.00 seviyesindedir. Karma maliyette Qwen3-Next 80B-A3B Instruct daha avantajlıdır.

Hangi modelin bağlam penceresi daha geniş ve hızı daha yüksek?+

Qwen3-Next 80B-A3B Instruct 131.1K token bağlam penceresi sunarken Llama 4 Scout 3.5M token kapasiteye sahiptir. Saniyedeki token üretim hızında ise Qwen3-Next 80B-A3B Instruct öndedir.

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 & PricingContext WindowAI BenchmarksReasoning ModelsTool CallingModel Context Protocol (MCP)

Related reading

Benchmark scores, token prices, and rate limits evolve rapidly as AI labs release model updates. Always verify current specifications and pricing on the official provider documentation.

Resmi kaynaklar: Alibaba ve Meta resmi API dokümantasyonu, sistem kartları ve bağımsız benchmark testleri (2026).