Llama 4 Behemoth vs Llama 5
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Llama 4 Behemoth da Meta e Llama 5 da Meta são dois modelos de IA de destaque. O Llama 4 Behemoth oferece uma janela de contexto de 10M tokens a $0.00/$0.00 por milhão de tokens, enquanto o Llama 5 oferece 5M tokens de contexto a $0.00/$0.00 por milhão de tokens. Veja abaixo a comparação completa de benchmarks, latência e custos.
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Llama 4 Behemoth
Modelo Base de Pesos AbertosO Llama 4 Behemoth 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 10M, velocidade de 45 tokens/s, índice de inteligência de 93/100 e tar
$0.00 / 1M
$0.00 / 1M
10M tokens
75%
Llama 5
Modelo Base de Pesos AbertosO 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
$0.00 / 1M
$0.00 / 1M
5M tokens
74%
Principais Diferenças em Resumo
- Both models cost the same under this workload — choose on capability, not price.
- Llama 4 Behemoth has a larger context window (10M vs 5M tokens).
- Llama 5 is faster at 65 tok/s vs 45 tok/s.
- Llama 4 Behemoth leads on SWE-bench coding (75.0% vs 74.0%).
| Feature | Llama 4 Behemoth | Llama 5 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 93 | 94 |
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 | 10M tokens~15,000 A4 pages of text | 5M tokens~7,500 A4 pages of text |
Max Output Tokens Per single request | 64K | 128K |
Throughput Speed Tokens generated per second | 45 tok/s | 65 tok/s |
SWE-bench Verified Real-world software engineering | 75% | 74% |
GPQA Diamond Graduate-level reasoning | 82% | 84% |
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.
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 Behemoth · Strengths & Key Advantages
- Janela de contexto de 10M com limite máximo de saída de 64K tokens (SWE-bench Verified 75% · GPQA Diamond 82%)
- Taxa de transferência rápida de 45 tokens/segundo com latência inicial (TTFT) de 1200 ms
- Pesos abertos (2T total / 288B (16-of-2 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 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
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Llama 4 Behemoth | Llama 5 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.00 | $0.00 |
Output Tokens / Task Tokens generated per task | 35K | 40K |
Reasoning Tokens / Task Thinking tokens burned per task | 15K | 20K |
Especificações Técnicas
Architecture, release timeline, capabilities, and modalities.
| Feature | Llama 4 Behemoth | Llama 5 |
|---|---|---|
| Official API Model ID (SDK) | meta-llama/Llama-4-Behemoth | meta-llama/Llama-5 |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2026-05 | 2026-04 |
| Knowledge Cutoff | February 2026 | January 2026 |
| Architecture | Colossal sparse MoE architecture (288B active / 2T total, 16-of-2 MoE) | Mixture-of-Experts with recursive self-improvement (~600B total, 40-80B active) |
| Parameters | 2T total / 288B active (16-of-2 MoE) | 600B+ total (~40-80B active) |
| 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) | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) · Audio (~25–32 tokens/sec) · Video (~258–300 tokens/sec) |
| Quantization & Min GPU VRAM | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 1240 GB VRAM (4-bit Quantized) · 4300 GB VRAM (Native BF16) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 372 GB VRAM (4-bit Quantized) · 1290 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, vision, video, audio |
| Output Modalities | text | text |
Llama 4 Behemoth vs Llama 5: Respostas Rápidas (FAQ)
Which is more intelligent, Llama 4 Behemoth or Llama 5?
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 45 tokens per second.
Which model has lower latency?
Llama 5 has lower latency, with a time to first token of 0.85s compared with 1.20s.
Which model has a larger context window?
Llama 4 Behemoth has a larger context window, supporting 10M tokens compared with 5M tokens.
Veredito
Tanto Llama 4 Behemoth (Indicado para auto-hospedagem em infraestrutura própria, fine-tuning e soberania de dados, arquiteturas RAG corporativas e integração escalável via API.) quanto Llama 5 (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 4 Behemoth apresenta o melhor custo-benefício em produção.
Perguntas Frequentes (FAQ)
Qual é melhor para programação e raciocínio, Llama 4 Behemoth ou Llama 5?+
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 4 Behemoth ou Llama 5?+
O Llama 4 Behemoth custa $0.00 / $0.00 por 1M de tokens (entrada/saída) e o Llama 5 custa $0.00 / $0.00. No custo combinado, Llama 4 Behemoth é mais econômico.
Qual modelo possui maior janela de contexto e velocidade?+
O Llama 4 Behemoth suporta 10M tokens de contexto contra 5M tokens do Llama 5, enquanto Llama 5 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).