Head-to-Head Model Comparison

Llama 3.3 70B vs GPT-4o

Benchmarks, API Token Pricing, Context Window & Speed Comparison

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

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Llama 3.3 70BProfile

GPT-4oProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

Llama 3.3 70B

Modelo Base de Pesos Abertos

O Llama 3.3 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 280 tokens/s, índice de inteligência de 38/100 e tari

Intelligence Index38 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

128K tokens

SWE-bench Verified:

39.5%

OpenAI

GPT-4o

Modelo Nativo de Voz e Áudio

O GPT-4o da OpenAI é um modelo de IA projetado para síntese de voz natural, transcrição de áudio e interação falada em tempo real, oferecendo janela de contexto de 128K, velocidade de 105 tokens/s, índice de inteligência de 41/100 e tarifa

Intelligence Index41 / 100

Input Token:

$2.50 / 1M

Output Token:

$10.00 / 1M

Context Window:

128K tokens

SWE-bench Verified:

38.8%

Principais Diferenças em Resumo

Llama 3.3 70B is 97.5x cheaper for API usage (input + output tokens combined).
Llama 3.3 70B is faster at 280 tok/s vs 105 tok/s.
Llama 3.3 70B leads on SWE-bench coding (39.5% vs 38.8%).

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

Feature	Llama 3.3 70B	GPT-4o
Intelligence Index Composite reasoning & knowledge score (0-100)	38	41
Provider	Meta	OpenAI
Input Price Cost per million input tokens	$0.00 / 1M	$2.50 / 1M
Output Price Cost per million output tokens	$0.00 / 1M	$10.00 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.07 / 1M	$2.38 / 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	4K	16K
Throughput Speed Tokens generated per second	280 tok/s	105 tok/s
SWE-bench Verified Real-world software engineering	39.5%	38.8%
GPQA Diamond Graduate-level reasoning	48.9%	53.6%
Cached Input Price Prompt caching discount per 1M tokens	$0.10 / 1M	$1.25 / 1M

Quem vence em cada categoria?

At-a-glance decision framework

Coding

Llama 3.3 70B

+0.7% SWE-bench lead

Cost

Llama 3.3 70B

97.5x cheaper monthly runtime

Speed

Llama 3.3 70B

+167% faster generation

Context

Tied

128K tokens window

Pontuações de Benchmarks Verificadas

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

Llama 3.3 70BGPT-4o

SWE-bench Verified (Autonomous Coding)

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

39.5%vs38.8%

GPQA Diamond (Doctorate-Level Science & Math)

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

48.9%vs53.6%

MMLU-Pro (Multi-task Language Understanding)

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

71%vs72.6%

LMSYS Chatbot Arena Elo

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

1285ptsvs1286pts

Humanity's Last Exam (Frontier Expert Knowledge)

Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.

7.8%vs4.1%

Terminal-Bench (Agentic Coding & Terminal Use)

Autonomous multi-step tasks executed inside a real command-line terminal environment.

26.1%vs18.5%

SciCode (Scientific Computing)

Research-grade coding problems drawn from real scientific and numerical workloads.

23.7%vs27.3%

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

42.5%vs43.9%

AIME (Advanced Math Competition)

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

56.2%vs29.7%

Long-Context Reasoning

Accuracy when reasoning over very large inputs that fill the model context window.

63.9%vs58.2%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

280 tok/s vs105 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 3.3 70B0.25s

GPT-4o0.41s

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

Llama 3.3 70B2.04s

GPT-4o5.17s

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 3.3 70B Monthly API Bill

$0.15 / month

$0/1M in · $0/1M out

GPT-4o Monthly API Bill

$14.63 / month

$2.5/1M in · $10/1M out

Llama 3.3 70B saves approximately $14.47 / month (99%) under this workload.97.5x Savings

Llama 3.3 70B · Strengths & Key Advantages

Janela de contexto de 128K com limite máximo de saída de 4K tokens (SWE-bench Verified 39.5% · GPQA Diamond 48.9%)
Taxa de transferência rápida de 280 tokens/segundo com latência inicial (TTFT) de 250 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

GPT-4o · Strengths & Key Advantages

Janela de contexto de 128K com limite máximo de saída de 16K tokens (SWE-bench Verified 38.8% · GPQA Diamond 53.6%)
Taxa de transferência rápida de 105 tokens/segundo com latência inicial (TTFT) de 410 ms
Economia de API competitiva ($2.5/$10 por 1M de tokens) com suporte a cache de prompts e saídas JSON estruturadas

Considerations & Trade-offs

Modelo proprietário em nuvem; não permite download dos pesos para execução local offline
Para alto volume de requisições, recomenda-se ativar o cache de prompts e a Batch API para reduzir custos

Token Use & Task Economics

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

Metric	Llama 3.3 70B	GPT-4o
Cost per Task Estimated USD to finish one task	$0.21	$0.68
Output Tokens / Task Tokens generated per task	45K	24K
Reasoning Tokens / Task Thinking tokens burned per task	0K	0K

Especificações Técnicas

Architecture, release timeline, capabilities, and modalities.

Feature	Llama 3.3 70B	GPT-4o
Official API Model ID (SDK)	meta-llama/Llama-3.3-70B	gpt-4o
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	OpenAI Responses API & Chat Completions v1
Release Date	2024-12	2024-05
Knowledge Cutoff	December 2023	October 2023
Architecture	Dense Autoregressive Transformer	Omni-Multimodal Dense
Parameters	70B	Confidential
Enterprise Rate Limits (RPM / TPM)	Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud	10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM
Long-Context Tier (>128K/200K)	Free across full context window (only KV-cache VRAM scales)	Flat rate ($2.5/1M in) across full 128K window
Prompt Cache Policy & TTL	Automatic prefix KV-caching in vLLM / SGLang (0 cost)	Automatic prefix caching at $1.25/1M (no extra cache-write fee)
Computer Use / GUI Agent	API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)	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	Native Web Search tool + Hosted Code Interpreter & File Search RAG
Official Fine-Tuning Support	Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)	Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT)
Multimodal Token Billing	Text BPE (~4 chars/token)	Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) · Audio (~25–32 tokens/sec)
Quantization & Min GPU VRAM	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 43 GB VRAM (4-bit Quantized) · 151 GB VRAM (Native BF16)	Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API)
Safety & Frontier Classification	Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma)	OpenAI Preparedness Framework Audited & System Card Published
Enterprise Compliance (ZDR/HIPAA/SOC2)	100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control	SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier
Input Modalities	text	text, vision, audio
Output Modalities	text	text, audio

Llama 3.3 70B vs GPT-4o: Respostas Rápidas (FAQ)

Which is more intelligent, Llama 3.3 70B or GPT-4o?

GPT-4o is more intelligent. It scores 41 compared with 38 on the composite Intelligence Index.

Which model is faster?

Llama 3.3 70B is faster, generating 280 tokens per second compared with 105 tokens per second.

Which model is cheaper to run?

Llama 3.3 70B is cheaper at $0.07 per 1M tokens blended, compared with $2.38 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Llama 3.3 70B has lower latency, with a time to first token of 0.25s compared with 0.41s.

Veredito

Tanto Llama 3.3 70B (Indicado para auto-hospedagem em infraestrutura própria, fine-tuning e soberania de dados, arquiteturas RAG corporativas e integração escalável via API.) quanto GPT-4o (Indicado para síntese de voz natural, transcrição de áudio e interação falada em tempo real, arquiteturas RAG corporativas e integração escalável via API.) entregam excelente desempenho: GPT-4o lidera em inteligência geral e programação, Llama 3.3 70B oferece geração mais rápida e Llama 3.3 70B apresenta o melhor custo-benefício em produção.

Perguntas Frequentes (FAQ)

Qual é melhor para programação e raciocínio, Llama 3.3 70B ou GPT-4o?+

Nas avaliações de inteligência e código, GPT-4o leva vantagem. Confira os gráficos SWE-bench e GPQA acima para detalhes.

Qual modelo tem a API mais barata, Llama 3.3 70B ou GPT-4o?+

O Llama 3.3 70B custa $0.00 / $0.00 por 1M de tokens (entrada/saída) e o GPT-4o custa $2.50 / $10.00. No custo combinado, Llama 3.3 70B é mais econômico.

Qual modelo possui maior janela de contexto e velocidade?+

O Llama 3.3 70B suporta 128K tokens de contexto contra 128K tokens do GPT-4o, enquanto Llama 3.3 70B 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 3.3 70B vs GPT-4o

Troque e Compare Qualquer Modelo

Llama 3.3 70B

GPT-4o

Quem vence em cada categoria?

Pontuações de Benchmarks Verificadas

Latência e Tempo de Resposta

Simulador Interativo de Custo de Tokens

Llama 3.3 70B · Strengths & Key Advantages

Considerations & Trade-offs

GPT-4o · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Especificações Técnicas

Llama 3.3 70B vs GPT-4o: Respostas Rápidas (FAQ)

Veredito

Perguntas Frequentes (FAQ)

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