Llama 3.3 70B

Together
Text & Chat

Meta's open-source 70B parameter model. Strong all-around performance with multilingual support.

Examples

See what Llama 3.3 70B can generate

Comparison Essay

Compare and contrast REST and GraphQL APIs in terms of flexibility and performance

REST APIs use fixed endpoints that return predetermined data structures, which is simple but can lead to over-fetching (getting more data than needed) or under-fetching (requiring multiple requests). GraphQL lets clients request exactly the fields they need in a single query, offering superior flexibility. However, REST benefits from better HTTP caching, simpler error handling, and wider tooling support. GraphQL shines in complex applications with varied data needs, while REST remains ideal for straightforward CRUD operations and public APIs.

Explain Like I'm 5

Explain how the internet works to a 5-year-old

Imagine you want to send a drawing to your friend who lives far away. You put your drawing in a magic mailbox, and the mailbox breaks it into tiny puzzle pieces. These pieces zoom through special tunnels underground and even under the ocean! When all the pieces reach your friend's magic mailbox, it puts the puzzle back together. That's basically how the internet works—your computer breaks messages into tiny pieces, sends them through wires, and the other computer puts them back together super fast.

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0.7

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Pricing

Price per Generation
Per generationFree

API Integration

Use our OpenAI-compatible API to integrate Llama 3.3 70B into your application.

Install
npm install railwail
JavaScript / TypeScript
import railwail from "railwail";

const rw = railwail("YOUR_API_KEY");

// Simple — just pass a string
const reply = await rw.run("llama-3-3-70b", "Hello! What can you do?");
console.log(reply);

// With message history
const reply2 = await rw.run("llama-3-3-70b", [
  { role: "system", content: "You are a helpful assistant." },
  { role: "user", content: "Explain quantum computing simply." },
]);
console.log(reply2);

// Full response with usage info
const res = await rw.chat("llama-3-3-70b", [
  { role: "user", content: "Hello!" },
], { temperature: 0.7, max_tokens: 500 });
console.log(res.choices[0].message.content);
console.log(res.usage);
Specifications
Context window
131,072 tokens
Max output
4,096 tokens
Avg. latency
2.5s
Provider
Together AI
Category
Text & Chat
Tags
open-source
popular
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