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

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Ditinjau oleh tim riset GetInfoUsPeninjauan terakhir: October 2026Kebijakan editorial
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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

Qwen3-Next 80B-A3B Instruct dari Alibaba dan Llama 4 Scout dari Meta adalah dua model AI unggulan. Qwen3-Next 80B-A3B Instruct menawarkan jendela konteks 131.1K token dengan harga $0.00/$0.00 per 1 juta token, sedangkan Llama 4 Scout menyediakan konteks 3.5M token seharga $0.00/$0.00 per 1 juta token. Berikut perbandingan lengkap benchmark, latensi, dan biayanya.

Perbandingan Langsung (VS)

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Popular Matchups:
Alibaba

Qwen3-Next 80B-A3B Instruct

Model Dasar Bobot Terbuka

Qwen3-Next 80B-A3B Instruct dari Alibaba adalah model AI yang dirancang untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, menghadirkan jendela konteks 131K, kecepatan 135 token/detik, indeks kecerdasan 78/100, se

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

Model Dasar Bobot Terbuka

Llama 4 Scout dari Meta adalah model AI yang dirancang untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, menghadirkan jendela konteks 3.5M, kecepatan 95 token/detik, indeks kecerdasan 76/100, serta akses gratis o

Intelligence Index76 / 100
Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

3.5M tokens

SWE-bench Verified:

N/A

Perbedaan Utama Sekilas

  • 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

Siapa Unggul di Mana?

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

Skor Benchmark Terverifikasi

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

Latensi & Waktu Respons

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

Simulator Biaya Token Interaktif

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

  • Jendela konteks 131K dengan kapasitas output maksimum hingga 8K token (GPQA Diamond 60% · MMLU-Pro 78%)
  • Kecepatan generasi tinggi 135 token/detik dengan latensi token pertama (TTFT) hanya 310 ms
  • Bobot terbuka (80B total (3B MoE)) yang siap di-hosting mandiri di GPU lokal atau cloud privat melalui vLLM dan Ollama
Considerations & Trade-offs
  • Membutuhkan infrastruktur GPU dengan kapasitas VRAM yang memadai untuk eksekusi lokal presisi penuh
  • Pada tugas pembuktian ilmiah tingkat ekstrem, model penalaran berat kelas atas memberikan kedalaman lebih tinggi

Llama 4 Scout · Strengths & Key Advantages

  • Jendela konteks 3.5M dengan kapasitas output maksimum hingga 33K token (MMLU-Pro 78% · Arena Elo 1370)
  • Kecepatan generasi tinggi 95 token/detik dengan latensi token pertama (TTFT) hanya 650 ms
  • Bobot terbuka (17B (109B total MoE)) yang siap di-hosting mandiri di GPU lokal atau cloud privat melalui vLLM dan Ollama
Considerations & Trade-offs
  • Membutuhkan infrastruktur GPU dengan kapasitas VRAM yang memadai untuk eksekusi lokal presisi penuh
  • Pada tugas pembuktian ilmiah tingkat ekstrem, model penalaran berat kelas atas memberikan kedalaman lebih tinggi

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

Spesifikasi Teknis

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: Jawaban Cepat (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.

Kesimpulan

Baik Qwen3-Next 80B-A3B Instruct (Sangat cocok untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, sistem RAG perusahaan, dan integrasi API skala produksi.) maupun Llama 4 Scout (Sangat cocok untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, sistem RAG perusahaan, dan integrasi API skala produksi.) sangat tangguh: Qwen3-Next 80B-A3B Instruct unggul dalam penalaran dan pemrograman, Qwen3-Next 80B-A3B Instruct memberikan kecepatan generasi token lebih tinggi, dan Qwen3-Next 80B-A3B Instruct lebih hemat biaya untuk penggunaan API skala besar.

Pertanyaan Umum (FAQ)

Mana yang lebih baik untuk coding dan penalaran, Qwen3-Next 80B-A3B Instruct atau Llama 4 Scout?+

Dalam indeks kecerdasan gabungan dan benchmark coding, Qwen3-Next 80B-A3B Instruct memimpin. Lihat skor SWE-bench dan GPQA di atas untuk detailnya.

Berapa perbandingan harga API Qwen3-Next 80B-A3B Instruct vs Llama 4 Scout?+

Qwen3-Next 80B-A3B Instruct mengenakan biaya $0.00 / $0.00 per 1M token input/output, sedangkan Llama 4 Scout sebesar $0.00 / $0.00. Secara keseluruhan, Qwen3-Next 80B-A3B Instruct lebih hemat biaya.

Model mana yang memiliki jendela konteks lebih besar dan kecepatan lebih tinggi?+

Qwen3-Next 80B-A3B Instruct mendukung konteks 131.1K token dibandingkan 3.5M token pada Llama 4 Scout, sementara Qwen3-Next 80B-A3B Instruct unggul dalam kecepatan output token per detik.

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.

Sumber resmi: Dokumentasi API resmi Alibaba & Meta serta evaluasi benchmark independen (2026).