Head-to-Head Model Comparison

Llama 3.1 8B vs Llama 3.1 70B

Benchmarks, API Token Pricing, Context Window & Speed Comparison

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

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Llama 3.1 8BProfile

Llama 3.1 70BProfile

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Llama 3.1 8B

Modelo Base de Pesos Abertos

O Llama 3.1 8B 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 128K, velocidade de 160 tokens/s, índice de inteligência de 60/100 e tarif

Intelligence Index60 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

128K tokens

SWE-bench Verified:

22%

Llama 3.1 70B

Modelo Base de Pesos Abertos

O Llama 3.1 70B 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 128K, velocidade de 75 tokens/s, índice de inteligência de 75/100 e tarif

Intelligence Index75 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

128K tokens

SWE-bench Verified:

42%

Principais Diferenças em Resumo

Both models cost the same under this workload — choose on capability, not price.
Llama 3.1 8B is faster at 160 tok/s vs 75 tok/s.
Llama 3.1 70B leads on SWE-bench coding (42.0% vs 22.0%).

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

Feature	Llama 3.1 8B	Llama 3.1 70B
Intelligence Index Composite reasoning & knowledge score (0-100)	60	75
Provider	Meta	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	128K tokens~192 A4 pages of text	128K tokens~192 A4 pages of text
Max Output Tokens Per single request	8K	8K
Throughput Speed Tokens generated per second	160 tok/s	75 tok/s
SWE-bench Verified Real-world software engineering	22%	42%
GPQA Diamond Graduate-level reasoning	38%	52%

Quem vence em cada categoria?

At-a-glance decision framework

Coding

Llama 3.1 70B

+20.0% SWE-bench lead

Cost

Tied

Identical monthly runtime cost

Speed

Llama 3.1 8B

+113% faster generation

Context

Tied

128K tokens window

Pontuações de Benchmarks Verificadas

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

Llama 3.1 8BLlama 3.1 70B

SWE-bench Verified (Autonomous Coding)

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

22%vs42%

GPQA Diamond (Doctorate-Level Science & Math)

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

38%vs52%

MMLU-Pro (Multi-task Language Understanding)

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

65%vs79.5%

LMSYS Chatbot Arena Elo

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

1245ptsvs1330pts

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

160 tok/s vs75 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 3.1 8B0.22s

Llama 3.1 70B0.40s

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

Llama 3.1 8B3.35s

Llama 3.1 70B7.07s

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 3.1 8B Monthly API Bill

$0.00 / month

$0/1M in · $0/1M out

Llama 3.1 70B 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 3.1 8B · Strengths & Key Advantages

Janela de contexto de 128K com limite máximo de saída de 8K tokens (SWE-bench Verified 22% · GPQA Diamond 38%)
Taxa de transferência rápida de 160 tokens/segundo com latência inicial (TTFT) de 220 ms
Pesos abertos (8B) 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

Llama 3.1 70B · Strengths & Key Advantages

Janela de contexto de 128K com limite máximo de saída de 8K tokens (SWE-bench Verified 42% · GPQA Diamond 52%)
Taxa de transferência rápida de 75 tokens/segundo com latência inicial (TTFT) de 400 ms
Pesos abertos (70B) 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 3.1 8B	Llama 3.1 70B
Cost per Task Estimated USD to finish one task	$0.00	$0.00
Output Tokens / Task Tokens generated per task	4K	6K
Reasoning Tokens / Task Thinking tokens burned per task	0K	0K

Especificações Técnicas

Architecture, release timeline, capabilities, and modalities.

Feature	Llama 3.1 8B	Llama 3.1 70B
Official API Model ID (SDK)	meta-llama/Llama-3.1-8B	meta-llama/Llama-3.1-70B
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date	2024-07	2024-07
Knowledge Cutoff	December 2023	December 2023
Architecture	Dense auto-regressive multilingual transformer	High-capacity dense multilingual transformer
Parameters	8B	70B
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)
Quantization & Min GPU VRAM	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 5 GB VRAM (4-bit Quantized) · 17 GB VRAM (Native BF16)	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 43 GB VRAM (4-bit Quantized) · 151 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
Output Modalities	text	text

Llama 3.1 8B vs Llama 3.1 70B: Respostas Rápidas (FAQ)

Which is more intelligent, Llama 3.1 8B or Llama 3.1 70B?

Llama 3.1 70B is more intelligent. It scores 75 compared with 60 on the composite Intelligence Index.

Which model is faster?

Llama 3.1 8B is faster, generating 160 tokens per second compared with 75 tokens per second.

Which model has lower latency?

Llama 3.1 8B has lower latency, with a time to first token of 0.22s compared with 0.40s.

Veredito

Tanto Llama 3.1 8B (Indicado para auto-hospedagem em infraestrutura própria, fine-tuning e soberania de dados, arquiteturas RAG corporativas e integração escalável via API.) quanto Llama 3.1 70B (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 3.1 70B lidera em inteligência geral e programação, Llama 3.1 8B oferece geração mais rápida e Llama 3.1 8B apresenta o melhor custo-benefício em produção.

Perguntas Frequentes (FAQ)

Qual é melhor para programação e raciocínio, Llama 3.1 8B ou Llama 3.1 70B?+

Nas avaliações de inteligência e código, Llama 3.1 70B leva vantagem. Confira os gráficos SWE-bench e GPQA acima para detalhes.

Qual modelo tem a API mais barata, Llama 3.1 8B ou Llama 3.1 70B?+

O Llama 3.1 8B custa $0.00 / $0.00 por 1M de tokens (entrada/saída) e o Llama 3.1 70B custa $0.00 / $0.00. No custo combinado, Llama 3.1 8B é mais econômico.

Qual modelo possui maior janela de contexto e velocidade?+

O Llama 3.1 8B suporta 128K tokens de contexto contra 128K tokens do Llama 3.1 70B, enquanto Llama 3.1 8B 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)

Llama 3.1 8B vs Llama 3.1 70B

Troque e Compare Qualquer Modelo

Llama 3.1 8B

Llama 3.1 70B

Quem vence em cada categoria?

Pontuações de Benchmarks Verificadas

Latência e Tempo de Resposta

Simulador Interativo de Custo de Tokens

Llama 3.1 8B · Strengths & Key Advantages

Considerations & Trade-offs

Llama 3.1 70B · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Especificações Técnicas

Llama 3.1 8B vs Llama 3.1 70B: Respostas Rápidas (FAQ)

Veredito

Perguntas Frequentes (FAQ)

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