Llama 4 Maverick vs Llama 3.3 70B
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Llama 4 Maverick da Meta e Llama 3.3 70B da Meta são dois modelos de IA de destaque. O Llama 4 Maverick oferece uma janela de contexto de 1M tokens a $0.00/$0.00 por milhão de tokens, enquanto o Llama 3.3 70B oferece 128K tokens de contexto a $0.00/$0.00 por milhão de tokens. Veja abaixo a comparação completa de benchmarks, latência e custos.
Troque e Compare Qualquer Modelo
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Llama 4 Maverick
IA Multimodal NativaO Llama 4 Maverick da Meta é um modelo de IA projetado para compreensão multimodal nativa de texto e imagem e leitura visual de documentos, oferecendo janela de contexto de 1M, velocidade de 85 tokens/s, índice de inteligência de 84/100 e t
$0.00 / 1M
$0.00 / 1M
1M tokens
62%
Llama 3.3 70B
Modelo Base de Pesos AbertosO 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
$0.00 / 1M
$0.00 / 1M
128K tokens
39.5%
Principais Diferenças em Resumo
- Llama 4 Maverick is 1.0x cheaper for API usage (input + output tokens combined).
- Llama 4 Maverick has a larger context window (1M vs 128K tokens).
- Llama 3.3 70B is faster at 280 tok/s vs 85 tok/s.
- Llama 4 Maverick leads on SWE-bench coding (62.0% vs 39.5%).
| Feature | Llama 4 Maverick | Llama 3.3 70B |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 84 | 38 |
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 | $0.07 / 1M |
Context Window Maximum input + output tokens | 1M tokens~1,500 A4 pages of text | 128K tokens~192 A4 pages of text |
Max Output Tokens Per single request | 33K | 4K |
Throughput Speed Tokens generated per second | 85 tok/s | 280 tok/s |
SWE-bench Verified Real-world software engineering | 62% | 39.5% |
GPQA Diamond Graduate-level reasoning | 72.5% | 48.9% |
Cached Input Price Prompt caching discount per 1M tokens | $0.00 / 1M | $0.10 / 1M |
Quem vence em cada categoria?
At-a-glance decision frameworkPontuações de Benchmarks Verificadas
Official evaluation metrics verified by independent third-party research labs.
Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.
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.
Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.
Autonomous multi-step tasks executed inside a real command-line terminal environment.
Research-grade coding problems drawn from real scientific and numerical workloads.
Contamination-free competitive programming problems collected continuously over time.
American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.
Accuracy when reasoning over very large inputs that fill the model context window.
Tokens emitted per second during active generation streaming.
Latência e Tempo de Resposta
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulador Interativo de Custo de Tokens
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Llama 4 Maverick · Strengths & Key Advantages
- Janela de contexto de 1M com limite máximo de saída de 33K tokens (SWE-bench Verified 62% · GPQA Diamond 72.5%)
- Taxa de transferência rápida de 85 tokens/segundo com latência inicial (TTFT) de 550 ms
- Pesos abertos (17B (400B total 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
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
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Llama 4 Maverick | Llama 3.3 70B |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.00 | $0.21 |
Output Tokens / Task Tokens generated per task | 15K | 45K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 0K |
Especificações Técnicas
Architecture, release timeline, capabilities, and modalities.
| Feature | Llama 4 Maverick | Llama 3.3 70B |
|---|---|---|
| Official API Model ID (SDK) | meta-llama/Llama-4-Maverick | meta-llama/Llama-3.3-70B |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2025-04 | 2024-12 |
| Knowledge Cutoff | January 2025 | December 2023 |
| Architecture | High-capacity sparse MoE multimodal transformer (17B active / 400B total) | Dense Autoregressive Transformer |
| Parameters | 17B (400B total MoE) | 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 | Supported (Screen coordinate grounding, browser & desktop GUI action loops) | 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) · Vision (~258–765 tokens per image tile) | Text BPE (~4 chars/token) |
| Quantization & Min GPU VRAM | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 11 GB VRAM (4-bit Quantized) · 37 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, vision | text |
| Output Modalities | text | text |
Llama 4 Maverick vs Llama 3.3 70B: Respostas Rápidas (FAQ)
Which is more intelligent, Llama 4 Maverick or Llama 3.3 70B?
Llama 4 Maverick is more intelligent. It scores 84 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 85 tokens per second.
Which model is cheaper to run?
Llama 4 Maverick is cheaper at $0.00 per 1M tokens blended, compared with $0.07 (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 0.55s.
Which model has a larger context window?
Llama 4 Maverick has a larger context window, supporting 1M tokens compared with 128K tokens.
Veredito
Tanto Llama 4 Maverick (Indicado para compreensão multimodal nativa de texto e imagem e leitura visual de documentos, arquiteturas RAG corporativas e integração escalável via API.) quanto 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.) entregam excelente desempenho: Llama 4 Maverick lidera em inteligência geral e programação, Llama 3.3 70B oferece geração mais rápida e Llama 4 Maverick apresenta o melhor custo-benefício em produção.
Perguntas Frequentes (FAQ)
Qual é melhor para programação e raciocínio, Llama 4 Maverick ou Llama 3.3 70B?+
Nas avaliações de inteligência e código, Llama 4 Maverick leva vantagem. Confira os gráficos SWE-bench e GPQA acima para detalhes.
Qual modelo tem a API mais barata, Llama 4 Maverick ou Llama 3.3 70B?+
O Llama 4 Maverick custa $0.00 / $0.00 por 1M de tokens (entrada/saída) e o Llama 3.3 70B custa $0.00 / $0.00. No custo combinado, Llama 4 Maverick é mais econômico.
Qual modelo possui maior janela de contexto e velocidade?+
O Llama 4 Maverick suporta 1M tokens de contexto contra 128K tokens do Llama 3.3 70B, enquanto Llama 3.3 70B entrega maior velocidade de geração.
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.
Fontes oficiais: Documentação oficial de API da Meta e Meta e benchmarks independentes (2026).