de Meta gargantuan 2-billones parámetro abierto modelo pushing el de frontera de abierto-weight capacidad y 10M contexto.
Technical Specifications
- Provider
- Meta
- Category
- open-source
- Release Date
- 2026-05
- Batch API Discount
- Standard rate
- Consumer Subscription
- Free / Self-Hosted
- Reasoning Effort Control
- Configurable (Low / Med / High)
- Structured Outputs
- Yes
- Output Modalities
- text
- Official Documentation
- Visit Official Docs ↗
Data Privacy, Licensing & Deployment Governance
Official Provider Release Intelligence, API Quotas & Agentic Specs
- Official API Model ID (SDK String)
- meta-llama/Llama-4-Behemoth
- Supported SDK & API Protocol
- OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
- Lifecycle & Deprecation Status
- Active GA (Guaranteed support through 2028+)
- Production Rate Limits (RPM & TPM)
- Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud
- Long-Context Pricing Tier (>128K / >200K)
- Free across full context window (only KV-cache VRAM scales)
- Prompt Cache Write & TTL Policy
- Automatic prefix KV-caching in vLLM / SGLang (0 cost)
- Computer Use & Browser GUI Agent
- 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
- Web Search Grounding & Code Sandbox
- Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools
- Fine-Tuning & Distillation Support
- Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)
- Multimodal Tokenization Billing
- Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)
- Weights Precision & Quantization
- BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0)
- Minimum GPU VRAM Requirement
- 1240 GB VRAM (4-bit Quantized) · 4300 GB VRAM (Native BF16)
- Supported Inference Engines
- vLLM, SGLang, Ollama, llama.cpp, TensorRT-LLM, HuggingFace TGI
- System Card & Safety Framework
- Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma)
- Enterprise Compliance (SOC2 / HIPAA / ZDR)
- 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control
Llama 4 Behemoth Cost, Context, Hardware & API Integration
Open-weights or free-tier model: $0 API license cost when self-hosted.
Same Monthly Workload Cost Across Popular Reference Models
Live Prompt Paste Tokenizer & Cost Counter
Paste real system instructions, JSON schemas, or code to measure BPE token counts across tokenizers.
Hybrid Prompt Routing Cascade Simulator
Route routine queries to a fast lightweight model ($0.15/1M) and escalate only complex reasoning turns to Llama 4 Behemoth:
Benchmark Scores & 6-Axis Capability Radar
- SWE-bench Verified
- 75%
- GPQA Diamond
- 82%
- MMLU-Pro
- 88.5%
- HumanEval
- 96%
- Long-Context Reasoning
- 95%
- Chatbot Arena Elo
- 1490pts
Strengths
- sin rival 2T parámetro abierto capacidad
- Staggering ~10 millones token contexto horizon
- de vanguardia codificación y razonamiento scores
Limitations & Trade-offs
- Demands alto-end datacenter multinodo GPU clústeres (8x H100 minimum)
- Slow inferencia rendimiento en comparación con smaller modelos
Editorial Assessment
el largest abierto-weight modelo fundacional en computing history.
Best for: National laboratories, grande empresarial AI clústeres, y extreme científico modeling
Compare Llama 4 Behemoth
Frequently Asked Questions about Llama 4 Behemoth
How much does the Llama 4 Behemoth API cost per 1M tokens?+
Llama 4 Behemoth by Meta is priced at $0 per 1M input tokens and $0 per 1M output tokens. With prompt caching enabled, cached input tokens cost $0 per 1M tokens, and asynchronous Batch API requests receive a 0% discount.
Should I use a $20/month subscription or the Llama 4 Behemoth pay-as-you-go API?+
A flat $20/month consumer subscription is predictable for daily interactive chat and manual coding. However, if you are building software, running automated workflows, or sending low-to-moderate volume with prompt caching enabled, the pay-as-you-go API is typically far more cost-effective and offers higher rate limits without manual UI caps.
How many words and pages fit in Llama 4 Behemoth's context window?+
Llama 4 Behemoth features a 10M tokens context window, which equals approximately 7,500,000 English words or ~20,000 standard single-spaced pages in a single prompt. Its maximum output generation per turn is 64,000 tokens.
Can I run Llama 4 Behemoth locally on my own GPU (Open Weights)?+
Yes, Llama 4 Behemoth (2T total / 288B active (16-of-2 MoE)) releases open weights that can be self-hosted locally or on private cloud GPUs using vLLM, Ollama, SGLang, or llama.cpp.
Does Meta train on data sent to the Llama 4 Behemoth API, and is commercial use allowed?+
100% Isolated when self-hosted on private GPUs or VPC. License tier: Llama Community License (Commercial Permitted). Full commercial ownership of generated outputs. Supported deployment channels include: Self-Hosted GPU (vLLM / Ollama), Private VPC, or Managed Serverless API.
How does Llama 4 Behemoth perform on coding and reasoning benchmarks?+
Llama 4 Behemoth achieves a composite Intelligence Index of 93/100, scoring 75% on SWE-bench Verified, 82% on GPQA Diamond, 88.5% on MMLU-Pro, and 1490 pts on Chatbot Arena Elo.
What is Llama 4 Behemoth best used for?+
Llama 4 Behemoth is best suited for: National laboratories, grande empresarial AI clústeres, y extreme científico modeling. Assessment: el largest abierto-weight modelo fundacional en computing history.
Meta Family Lineage, Routing Tiers & Upgrade Path
Step down to a lighter sibling for high-volume routing or step up to Meta's flagship reasoning tier:
Llama 5
Muse Spark 1.1
Llama 4 Maverick
Llama 4 Scout
Llama 3.3 70B
Llama 3.2 11B Vision
AI Glossary & Architecture Concepts
View full glossary →Key machine learning concepts and benchmarks relevant to Llama 4 Behemoth:
La unidad básica de texto que procesa un modelo de lenguaje, que normalmente equivale a 3 o 4 caracteres o aproximadamente 0,75 palabras. Por ejemplo, una frase típica se divide en varios tokens.
Un conjunto estandarizado de pruebas para evaluar y comparar objetivamente las capacidades de distintos modelos de IA en tareas como programación (SWE-bench) o matemáticas avanzadas (AIME).
La cantidad máxima de tokens que un modelo puede considerar a la vez, incluyendo tanto la entrada que lee como la salida que genera. Una ventana de 1.000.000 de tokens puede procesar unas 750.000 palabras en una sola petición.
Un modelo de IA entrenado para invertir cómputo adicional en pensar y validar hipótesis lógicas antes de responder, diseñado para problemas complejos de ingeniería y matemáticas.
Una técnica de razonamiento paso a paso donde el modelo genera pensamientos intermedios antes de formular la respuesta final, mejorando radicalmente la resolución de problemas matemáticos y lógicos.
La capacidad de un modelo de detectar la necesidad de una función externa y emitir argumentos JSON estructurados para ejecutar API, búsquedas web o consultas a bases de datos.
Las puntuaciones de benchmarks, precios por token y límites de tasa evolucionan rápidamente. Verifique siempre las especificaciones actuales en la documentación oficial del proveedor.