Head-to-Head Model Comparison

Llama 5 vs Qwen3.8 Max Preview

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Revisado pela equipe de pesquisa do GetInfoUsÚltima revisão: October 2026Política editorial

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Llama 5Profile

Qwen3.8 Max PreviewProfile

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5-Model Matrix Workbench

Popular Matchups:

Llama 5

Modelo Base de Pesos Abertos

O Llama 5 da Meta é um modelo de IA projetado para auto-hospedagem em infraestrutura própria, fine-tuning e soberania de dados, oferecendo janela de contexto de 5M, velocidade de 65 tokens/s, índice de inteligência de 94/100 e tarifa de ace

Intelligence Index94 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

5M tokens

SWE-bench Verified:

74%

Alibaba

Qwen3.8 Max Preview

Modelo Base de Pesos Abertos

O Qwen3.8 Max Preview da Alibaba é um modelo de IA projetado para auto-hospedagem em infraestrutura própria, fine-tuning e soberania de dados, oferecendo janela de contexto de 1M, velocidade de 60 tokens/s, índice de inteligência de 93/100

Intelligence Index93 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

1M tokens

SWE-bench Verified:

75.2%

Principais Diferenças em Resumo

Both models cost the same under this workload — choose on capability, not price.
Llama 5 has a larger context window (5M vs 1M tokens).
Llama 5 is faster at 65 tok/s vs 60 tok/s.
Qwen3.8 Max Preview leads on SWE-bench coding (75.2% vs 74.0%).

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

Feature	Llama 5	Qwen3.8 Max Preview
Intelligence Index Composite reasoning & knowledge score (0-100)	94	93
Provider	Meta	Alibaba
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	5M tokens~7,500 A4 pages of text	1M tokens~1,500 A4 pages of text
Max Output Tokens Per single request	128K	32K
Throughput Speed Tokens generated per second	65 tok/s	60 tok/s
SWE-bench Verified Real-world software engineering	74%	75.2%
GPQA Diamond Graduate-level reasoning	84%	79.5%

Quem vence em cada categoria?

At-a-glance decision framework

Coding

Qwen3.8 Max Preview

+1.2% SWE-bench lead

Cost

Tied

Identical monthly runtime cost

Speed

Llama 5

+8% faster generation

Context

Llama 5

5M tokens window

Pontuações de Benchmarks Verificadas

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

Llama 5Qwen3.8 Max Preview

SWE-bench Verified (Autonomous Coding)

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

74%vs75.2%

GPQA Diamond (Doctorate-Level Science & Math)

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

84%vs79.5%

MMLU-Pro (Multi-task Language Understanding)

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

87%vs89%

LMSYS Chatbot Arena Elo

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

1485ptsvs1485pts

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

68%vs77%

AIME (Advanced Math Competition)

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

88%vsN/A

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

65 tok/s vs60 tok/s

Latência e Tempo de Resposta

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 50.85s

Qwen3.8 Max Preview0.68s

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

Llama 58.54s

Qwen3.8 Max Preview9.01s

Simulador Interativo de Custo de Tokens

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 5 Monthly API Bill

$0.00 / month

$0/1M in · $0/1M out

Qwen3.8 Max Preview 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

Llama 5 · Strengths & Key Advantages

Janela de contexto de 5M com limite máximo de saída de 128K tokens (SWE-bench Verified 74% · GPQA Diamond 84%)
Taxa de transferência rápida de 65 tokens/segundo com latência inicial (TTFT) de 850 ms
Pesos abertos (600B+ total (~40-80B )) para auto-hospedagem local ou em nuvem privada via vLLM, SGLang ou Ollama

Considerations & Trade-offs

Requer servidores GPU com memória VRAM adequada para execução local em precisão total
Em derivações científicas extremas, modelos pesados dedicados exclusivamente ao raciocínio oferecem maior profundidade

Qwen3.8 Max Preview · Strengths & Key Advantages

Janela de contexto de 1M com limite máximo de saída de 32K tokens (SWE-bench Verified 75.2% · GPQA Diamond 79.5%)
Taxa de transferência rápida de 60 tokens/segundo com latência inicial (TTFT) de 680 ms
Pesos abertos (2.4T total (95B MoE)) para auto-hospedagem local ou em nuvem privada via vLLM, SGLang ou Ollama

Considerations & Trade-offs

Requer servidores GPU com memória VRAM adequada para execução local em precisão total
Em derivações científicas extremas, modelos pesados dedicados exclusivamente ao raciocínio oferecem maior profundidade

Token Use & Task Economics

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

Metric	Llama 5	Qwen3.8 Max Preview
Cost per Task Estimated USD to finish one task	$0.00	$0.00
Output Tokens / Task Tokens generated per task	40K	35K
Reasoning Tokens / Task Thinking tokens burned per task	20K	15K

Especificações Técnicas

Architecture, release timeline, capabilities, and modalities.

Llama 5 vs Qwen3.8 Max Preview: Respostas Rápidas (FAQ)

Which is more intelligent, Llama 5 or Qwen3.8 Max Preview?

Llama 5 is more intelligent. It scores 94 compared with 93 on the composite Intelligence Index.

Which model is faster?

Llama 5 is faster, generating 65 tokens per second compared with 60 tokens per second.

Which model has lower latency?

Qwen3.8 Max Preview has lower latency, with a time to first token of 0.68s compared with 0.85s.

Which model has a larger context window?

Llama 5 has a larger context window, supporting 5M tokens compared with 1M tokens.

Veredito

Tanto Llama 5 (Indicado para auto-hospedagem em infraestrutura própria, fine-tuning e soberania de dados, arquiteturas RAG corporativas e integração escalável via API.) quanto Qwen3.8 Max Preview (Indicado para auto-hospedagem em infraestrutura própria, fine-tuning e soberania de dados, arquiteturas RAG corporativas e integração escalável via API.) entregam excelente desempenho: Llama 5 lidera em inteligência geral e programação, Llama 5 oferece geração mais rápida e Llama 5 apresenta o melhor custo-benefício em produção.

Perguntas Frequentes (FAQ)

Qual é melhor para programação e raciocínio, Llama 5 ou Qwen3.8 Max Preview?+

Nas avaliações de inteligência e código, Llama 5 leva vantagem. Confira os gráficos SWE-bench e GPQA acima para detalhes.

Qual modelo tem a API mais barata, Llama 5 ou Qwen3.8 Max Preview?+

O Llama 5 custa $0.00 / $0.00 por 1M de tokens (entrada/saída) e o Qwen3.8 Max Preview custa $0.00 / $0.00. No custo combinado, Llama 5 é mais econômico.

Qual modelo possui maior janela de contexto e velocidade?+

O Llama 5 suporta 5M tokens de contexto contra 1M tokens do Qwen3.8 Max Preview, enquanto Llama 5 entrega maior velocidade de geração.

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)

Feature	Llama 5	Qwen3.8 Max Preview
Official API Model ID (SDK)	meta-llama/Llama-5	qwen3-8-max-preview-instruct
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date	2026-04	2026-07
Knowledge Cutoff	January 2026	April 2026
Architecture	Mixture-of-Experts with recursive self-improvement (~600B total, 40-80B active)	Colossus-Scale Open Sparse Mixture-of-Experts
Parameters	600B+ total (~40-80B active)	2.4T total (95B active 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	Supported (Screen coordinate grounding, browser & desktop GUI action loops)	Supported (Screen coordinate grounding, browser & desktop GUI action loops)
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) · Audio (~25–32 tokens/sec) · Video (~258–300 tokens/sec)	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) · 372 GB VRAM (4-bit Quantized) · 1290 GB VRAM (Native BF16)	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 1488 GB VRAM (4-bit Quantized) · 5160 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, video, audio	text, vision
Output Modalities	text	text

Llama 5 vs Qwen3.8 Max Preview

Troque e Compare Qualquer Modelo

Llama 5

Qwen3.8 Max Preview

Quem vence em cada categoria?

Pontuações de Benchmarks Verificadas

Latência e Tempo de Resposta

Simulador Interativo de Custo de Tokens

Llama 5 · Strengths & Key Advantages

Considerations & Trade-offs

Qwen3.8 Max Preview · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Especificações Técnicas

Llama 5 vs Qwen3.8 Max Preview: Respostas Rápidas (FAQ)

Veredito

Perguntas Frequentes (FAQ)

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