Llama 3.2 1B Instruct

Tool use

Meta's compact and efficient 1 billion parameter model designed for on-device and edge deployment. Optimized for instruction following with strong performance despite its small size, ideal for resource-constrained environments.

Model id — paste this in your code

meta-llama/llama-3.2-1b-instruct

Pricing · per 1M tokens

Show prices in

Input8.1 DA$0.0324

Output60.3 DA$0.241

Cached input8.1 DA$0.0324

Context window60KMax output: 15K

Capabilities

Tool use — Can call your functions / tools

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Input tokens Output tokens Requests

Estimated cost

Use this model

Create an account, top up in dinars and call it with your key:

from openai import OpenAI

client = OpenAI(
    base_url="https://dzrouter.com/v1",
    api_key="sk-dz-…",
)

reply = client.chat.completions.create(
    model="meta-llama/llama-3.2-1b-instruct",
    messages=[{"role": "user", "content": "Salam!"}],
)
print(reply.choices[0].message.content)

import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://dzrouter.com/v1",
  apiKey: process.env.DZROUTER_API_KEY,
});

const reply = await client.chat.completions.create({
  model: "meta-llama/llama-3.2-1b-instruct",
  messages: [{ role: "user", content: "Salam!" }],
});
console.log(reply.choices[0].message.content);

curl https://dzrouter.com/v1/chat/completions \
  -H "Authorization: Bearer $DZROUTER_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "meta-llama/llama-3.2-1b-instruct",
    "messages": [{"role": "user", "content": "Salam!"}],
    "stream": true
  }'

from anthropic import Anthropic

client = Anthropic(
    base_url="https://dzrouter.com",
    api_key="sk-dz-…",
)

msg = client.messages.create(
    model="meta-llama/llama-3.2-1b-instruct",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Salam!"}],
)
print(msg.content[0].text)

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