Meta Llama API
Access Meta Llama instantly with Puter.js, and add AI to any app in a few lines of code without backend or API keys.
// npm install @heyputer/puter.js
import { puter } from '@heyputer/puter.js';
puter.ai.chat("Explain AI like I'm five!", {
model: "meta-llama/llama-4-maverick"
}).then(response => {
console.log(response);
});
<html>
<body>
<script src="https://js.puter.com/v2/"></script>
<script>
puter.ai.chat("Explain AI like I'm five!", {
model: "meta-llama/llama-4-maverick"
}).then(response => {
console.log(response);
});
</script>
</body>
</html>
List of Meta Llama Models
Llama Guard 4 12B
meta-llama/llama-guard-4-12b
Llama Guard 4 12B is Meta's 12 billion parameter multimodal safety model that moderates both text and image inputs across 12 languages. It was built from Llama 4 Scout and detects violations based on the MLCommons hazard taxonomy.
ChatLlama 4 Maverick
meta-llama/llama-4-maverick
Llama 4 Maverick is Meta's 400 billion total parameter MoE model with 17B active parameters and 128 experts, supporting 1M token context. It's natively multimodal with state-of-the-art performance on coding, reasoning, and image understanding tasks.
ChatLlama 4 Scout
meta-llama/llama-4-scout
Llama 4 Scout is Meta's efficient 109 billion parameter MoE model with 17B active parameters and 16 experts, featuring an industry-leading 10M token context window. It fits on a single H100 GPU and handles multimodal text and image inputs.
ChatLlama Guard 3 8B
meta-llama/llama-guard-3-8b
Llama Guard 3 8B is Meta's enhanced safety moderation model providing content classification in 8 languages with support for tool call safety. It detects 14 hazard categories and integrates with Llama 3.1 for comprehensive AI safety.
ChatLlama 3.3 70B Instruct
meta-llama/llama-3.3-70b-instruct
Llama 3.3 70B Instruct is Meta's refined 70 billion parameter multilingual model with improved instruction following and tool use capabilities. It supports 8 languages and offers enhanced reasoning performance over previous versions.
ChatMeta Llama 3.3 70B Instruct Turbo
meta-llama/llama-3.3-70b-instruct-turbo
Llama 3.3 70B Instruct Turbo is a 70-billion-parameter, instruction-tuned text model from Meta, served on Together AI's throughput-optimized Turbo endpoint. It is known for delivering quality close to the much larger Llama 3.1 405B at a fraction of the cost, especially on instruction following and reasoning. Published benchmarks include IFEval 92.1%, MMLU 86.0%, HumanEval 88.4%, MGSM 91.1%, MATH 77.0%, and GPQA Diamond 50.5%. On IFEval it edges out Llama 3.1 405B (88.6) and approaches Claude 3.5 Sonnet (89.3). With a 128K context window and tool/function calling support, it suits developers building chat assistants, multilingual apps, coding helpers, and agentic workflows that want strong open-weight performance at a low price.
ChatLlama 3.2 11B Vision Instruct
meta-llama/llama-3.2-11b-vision-instruct
Llama 3.2 11B Vision Instruct is Meta's multimodal model that processes both text and images with 11 billion parameters. It excels at visual recognition, image reasoning, captioning, and answering questions about images.
ChatLlama 3.2 1B Instruct
meta-llama/llama-3.2-1b-instruct
Llama 3.2 1B Instruct is Meta's ultra-lightweight 1 billion parameter model designed for edge and mobile devices. It supports 128K context and handles summarization, instruction following, and rewriting tasks locally.
ChatLlama 3.2 3B Instruct
meta-llama/llama-3.2-3b-instruct
Llama 3.2 3B Instruct is a compact 3 billion parameter model optimized for on-device use cases with 128K context support. It outperforms comparable models on instruction following, summarization, and tool-use tasks.
ChatLlama 3.1 405B (base)
meta-llama/llama-3.1-405b
Llama 3.1 405B is Meta's flagship open-source large language model with 405 billion parameters, supporting 128K context length and 8 languages. It offers capabilities comparable to leading closed models for advanced reasoning, coding, and multilingual tasks.
ChatLlama 3.1 405B Instruct
meta-llama/llama-3.1-405b-instruct
Llama 3.1 405B Instruct is the instruction-tuned version of Meta's largest open model, optimized for multilingual dialogue, tool use, and complex reasoning. It supports 8 languages with 128K context and serves as a foundation for enterprise-level AI applications.
ChatLlama 3.1 70B Instruct
meta-llama/llama-3.1-70b-instruct
Llama 3.1 70B Instruct is a multilingual 70 billion parameter model with 128K context length, optimized for dialogue, tool use, and coding tasks. It balances strong performance with resource efficiency across 8 supported languages.
ChatLlama 3.1 8B Instruct
meta-llama/llama-3.1-8b-instruct
Llama 3.1 8B Instruct is Meta's efficient 8 billion parameter multilingual model supporting 128K context and 8 languages. It's ideal for resource-constrained deployments requiring summarization, classification, and translation capabilities.
ChatLlama 3 70B Instruct
meta-llama/llama-3-70b-instruct
Llama 3 70B Instruct is a 70 billion parameter instruction-tuned language model from Meta, optimized for dialogue and assistant-like chat in English. It uses an optimized transformer architecture with grouped-query attention and was trained on over 15 trillion tokens.
ChatLlama 3 8B Instruct
meta-llama/llama-3-8b-instruct
Llama 3 8B Instruct is Meta's compact 8 billion parameter instruction-tuned model for dialogue use cases in English. It offers strong performance on common benchmarks while being more efficient to deploy than its larger sibling.
ChatLlamaGuard 2 8B
meta-llama/llama-guard-2-8b
Llama Guard 2 8B is Meta's 8 billion parameter safety classifier built on Llama 3, designed to moderate both user prompts and AI responses. It classifies content across 11 hazard categories based on the MLCommons taxonomy.
ChatMeta Llama 3 8B Instruct Lite
meta-llama/meta-llama-3-8b-instruct-lite
Meta Llama 3 8B Instruct Lite is a cost-optimized serving tier for Meta's Llama 3 8B Instruct, an 8-billion-parameter open-weight chat model pretrained on over 15 trillion tokens. Despite its small size, it delivers strong reasoning, coding, and general-purpose conversation. On published benchmarks it scores around 67.4 on MMLU, 79.6% on GSM8K math, and 62.2% on HumanEval coding, making it highly competitive among models in its tier. The Lite tier offers the same quality at a lower per-token price, making it a great fit for developers who need fast, affordable responses for chat assistants, summarization, classification, and lightweight coding tasks at scale.
Frequently Asked Questions
The Meta Llama API gives you access to models for AI chat. Through Puter.js, you can start using Meta Llama models instantly with zero setup or configuration.
Puter.js supports a variety of Meta Llama models, including Llama Guard 4 12B, Llama 4 Maverick, Llama 4 Scout, and more. Find all AI models supported by Puter.js in the AI model list.
With the User-Pays model, users cover their own AI costs through their Puter account. This means you can build apps without worrying about infrastructure expenses.
Puter.js is a JavaScript library that provides access to AI, storage, and other cloud services directly from a single API. It handles authentication, infrastructure, and scaling so you can focus on building your app.
Yes — the Meta Llama API through Puter.js works with any JavaScript framework, Node.js, or plain HTML. Just include the library and start building. See the documentation for more details.