Qwen3-Next 80B-A3B Instruct vs Llama 4 Scout
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Qwen3-Next 80B-A3B Instruct by Alibaba and Llama 4 Scout by Meta represent two of the frontier AI models analyzed in our comprehensive 2026 AI benchmark and LLM comparison. In this head-to-head evaluation, we examine API token pricing, context window specifications, inference speed (tokens per second), and empirical reasoning performance. From a pure economic perspective, both models offer identical input token rates. On output generation, costs are matched. In data-heavy architectures requiring extensive repository or document analysis, Llama 4 Scout provides an expanded memory horizon with 3,500K tokens. In latency-sensitive production environments, Qwen3-Next 80B-A3B Instruct leads in tokens-per-second throughput, making it ideal for real-time conversational agents and high-concurrency workflows. Below is the complete empirical head-to-head breakdown across cost, speed, context, and capability matrices.
بدّل وقارن بين أي نموذجين
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Qwen3-Next 80B-A3B Instruct
نموذج مفتوح الأوزاننموذج Qwen3-Next 80B-A3B Instruct المقدم من Alibaba يوفر نافذة سياق تصل إلى 131K رمز، بتكلفة $0 لكل مليون رمز إدخال و$0 لكل مليون رمز إخراج، ومصمم للبرمجة والاستدلال وتطبيقات الأعمال.
$0.00 / 1M
$0.00 / 1M
131.1K tokens
N/A
Llama 4 Scout
نموذج مفتوح الأوزاننموذج Llama 4 Scout المقدم من Meta يوفر نافذة سياق تصل إلى 3.5M رمز، بتكلفة $0 لكل مليون رمز إدخال و$0 لكل مليون رمز إخراج، ومصمم للبرمجة والاستدلال وتطبيقات الأعمال.
$0.00 / 1M
$0.00 / 1M
3.5M tokens
N/A
أهم الفروقات في لمحة
- 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.
| 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 |
من يتفوق في كل مجال؟
At-a-glance decision frameworkEvaluations ongoing
نتائج الاختبارات المعيارية الموثقة
Official evaluation metrics verified by independent third-party research labs.
Challenging questions created by domain experts to test complex PhD-level reasoning.
Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.
Crowdsourced human preference evaluation based on blind, head-to-head model battles.
Accuracy when reasoning over very large inputs that fill the model context window.
Tokens emitted per second during active generation streaming.
زمن الاستجابة والسرعة
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
محاكي تكلفة الرموز التفاعلي
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Qwen3-Next 80B-A3B Instruct · Strengths & Key Advantages
- بنية Standard Instruction-Tuned Sparse Mixture-of-Experts متطورة من Alibaba مع سعة سياق تبلغ 131K رمز
- كفاءة عالية في التكلفة ($0/1M إدخال و$0/1M إخراج) وسرعة استجابة ممتازة
- أداء قوي في اختبارات البرمجة، الاستدلال المنطقي، ودعم اللغات المتعددة
Considerations & Trade-offs
- يتطلب إدارة دقيقة لاستهلاك الرموز عند استخدام نافذة السياق الكاملة
- قد يحتاج إلى ضبط دقيق للتعليمات (Prompt Engineering) في المهام التخصصية المعقدة
Llama 4 Scout · Strengths & Key Advantages
- بنية Extreme-context sparse MoE multimodal transformer (17B active / 109B total) متطورة من Meta مع سعة سياق تبلغ 3.5M رمز
- كفاءة عالية في التكلفة ($0/1M إدخال و$0/1M إخراج) وسرعة استجابة ممتازة
- أداء قوي في اختبارات البرمجة، الاستدلال المنطقي، ودعم اللغات المتعددة
Considerations & Trade-offs
- يتطلب إدارة دقيقة لاستهلاك الرموز عند استخدام نافذة السياق الكاملة
- قد يحتاج إلى ضبط دقيق للتعليمات (Prompt Engineering) في المهام التخصصية المعقدة
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 |
المواصفات التقنية
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: إجابات سريعة (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.
الحكم النهائي
Verdict & Model Selection Recommendation: Choose Qwen3-Next 80B-A3B Instruct if your priority is General conversational agents, customer care, and low-latency API wrappers. Its performance in demanding tasks justifies its operational token cost. Choose Llama 4 Scout if your workflow prioritizes Whole-repository code analysis, multi-hour video understanding, and legal book ingestion. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
الأسئلة الشائعة (FAQ)
How does Qwen3-Next 80B-A3B Instruct compare to Llama 4 Scout in API token pricing and cost per token?+
Both Qwen3-Next 80B-A3B Instruct and Llama 4 Scout are open-weights foundation models available at $0.00 direct API cost when self-hosted, or offered at identical baseline rates across major inference providers. Model selection depends on GPU deployment sizing and specialized benchmark performance rather than raw token price.
Which model has a larger context window: Qwen3-Next 80B-A3B Instruct or Llama 4 Scout?+
Qwen3-Next 80B-A3B Instruct supports a context window of 131K tokens (~98k words), while Llama 4 Scout supports 3,500K tokens (~2,625k words). Therefore, Llama 4 Scout can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: Qwen3-Next 80B-A3B Instruct or Llama 4 Scout?+
Qwen3-Next 80B-A3B Instruct scores at the frontier of coding benchmarks, while Llama 4 Scout scores competitively for software generation. For complex autonomous debugging and agentic engineering, Qwen3-Next 80B-A3B Instruct provides robust code generation and tool calling.
What is the difference in reasoning capability and extended thinking between Qwen3-Next 80B-A3B Instruct and Llama 4 Scout?+
Qwen3-Next 80B-A3B Instruct is optimized for low-latency direct generation. In contrast, Llama 4 Scout focuses on standard fast inference.
How does inference speed (tokens per second) compare between Qwen3-Next 80B-A3B Instruct and Llama 4 Scout?+
Qwen3-Next 80B-A3B Instruct generates approximately 135 tokens per second, while Llama 4 Scout delivers 95 tokens per second. In latency-sensitive production environments, Qwen3-Next 80B-A3B Instruct provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Qwen3-Next 80B-A3B Instruct and Llama 4 Scout to optimize costs?+
Yes! Tiered multi-model routing can optimize for latency and accuracy: you can route high-frequency conversational turns and latency-sensitive queries to Qwen3-Next 80B-A3B Instruct, and route deep architectural or complex reasoning queries to Qwen3-Next 80B-A3B Instruct. This balanced routing strategy maximizes system throughput and answer quality.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
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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.
المصادر الرسمية: GetInfoUs Independent AI Benchmarks & Official API Specs