Llama 3.1 70B Instruct introduced context of 131.1K tokens, eight-language multilingual support, and trained tool-use capability to the open 70B parameter class. This release defined Meta's approach to open frontier models.
import { streamText } from 'ai'
const result = streamText({ model: 'meta/llama-3.1-70b', prompt: 'Why is the sky blue?'})Meta released Llama 3.1 70B Instruct on July 23, 2024. The 70B variant sat in the middle of a three-model release (8B, 70B, 405B) but carried the full 3.1 feature set: a context window of 131.1K tokens (16 times longer than Llama 3's limit), multilingual support across eight languages, and tool-use capability trained directly into the model rather than bolted on through prompting tricks.
The eight-language support (English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai) goes beyond surface-level translation. Llama 3.1 70B Instruct matched or beat peer open and closed models in its parameter class on more than 150 benchmark datasets spanning these languages. A single model can handle cross-language conversations without maintaining separate language-specific deployments.
Tool use was a first for the Llama family at this scale. The 3.1 generation was explicitly trained for agentic workflows: calling APIs, querying databases, and invoking custom functions as part of a reasoning chain. Meta's Llama Stack API, introduced alongside 3.1, standardized the interfaces for connecting the model to external toolchains and retrieval-augmented generation (RAG) systems.
Meta also expanded the open-weight license in 3.1. You can use model outputs to train or improve other models.