Meta's gargantuan 2-Trillion parameter open model pushing the frontier of open-weight capability and 10M context.
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
- Unrivaled 2T parameter open capacity
- Staggering ~10 Million token context horizon
- State-of-the-art coding and reasoning scores
Limitations & Trade-offs
- Demands high-end datacenter multi-node GPU clusters (8x H100 minimum)
- Slow inference throughput compared to smaller models
Editorial Assessment
The largest open-weight foundation model in computing history.
Best for: National laboratories, large enterprise AI clusters, and extreme scientific 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, large enterprise AI clusters, and extreme scientific modeling. Assessment: The largest open-weight foundation model in 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:
The basic unit of text that a language model processes, typically representing about 3 to 4 English characters or roughly 0.75 words. For example, the sentence "Artificial intelligence is fast" breaks down into about 5 tokens.
A standardized evaluation suite used to objectively measure and rank an AI model's capabilities across specific tasks — such as SWE-bench Verified for software engineering, AIME for competitive math, or MMLU for general knowledge.
The maximum number of tokens a model can consider at once — both the input it reads and the output it writes. A 1,000,000-token context window can hold roughly 750,000 English words in a single request.
An AI model trained via reinforcement learning to spend dynamic compute thinking through complex logic, math, and architectural problems before outputting a solution. Examples include OpenAI o1, o3, and DeepSeek R1.
A step-by-step reasoning technique where an AI model generates intermediate problem-solving thoughts before arriving at a final answer. Native reasoning models like OpenAI o3 and DeepSeek R1 use CoT to solve complex math and coding challenges.
The ability of a language model to recognize when an external capability is needed and output structured JSON arguments to invoke external APIs, databases, or terminal commands. The runtime executes the tool and passes results back to the model.
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.