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Model profile

Meta Llama 3.2 1B Instruct

Meta chat
meta-llama/Llama-3.2-1B-Instruct

Llama 3.2 1B is a 1-billion-parameter language model focused on efficiently performing natural language tasks, such as summarization, dialogue, and multilingual text analysis. Its smaller size allows it to operate...

Try in Playground API Docs
At a glance

Model specifications

The most important technical and pricing details in one place.

Context window
131.1K tokens
Model type
Chat
Input / 1M
$0.02
Output / 1M
$0.02

Providers and routing order

Gateway-compatible routes are tried from the lowest cost; other sources are shown for catalog reference.

3 sources
1
Novita AICatalog sourcemeta-llama/llama-3.2-1b-instruct · Updated 05.10.2026 20:17
Input$0.0200 / 1M Output$0.0200 / 1M Cache$0.0000 / 1M Media-
2
OpenRouterGateway activemeta-llama/llama-3.2-1b-instruct · Updated 05.10.2026 20:17
Input$0.0270 / 1M Output$0.2010 / 1M Cache$0.0000 / 1M Media-
3
Together AIGateway activemeta-llama/Llama-3.2-1B-Instruct · Updated 05.10.2026 20:17
Input$0.0600 / 1M Output$0.0600 / 1M Cache$0.0000 / 1M Media-
Live telemetry

Usage and performance

Current request, token, and latency data across TamgaStudio.

Usage Example

Call Meta Llama 3.2 1B Instruct with a single request through the OpenAI-compatible TamgaStudio API. Create your API key from the API Keys section in your dashboard.

curl https://api.tamga.studio/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer tl-sk-XXXXXX" \
  -d '{"model": "meta-llama/Llama-3.2-1B-Instruct", "messages": [{"role": "user", "content": "Hello!"}]}'
import requests

resp = requests.post(
    "https://api.tamga.studio/v1/chat/completions",
    headers={"Authorization": "Bearer tl-sk-XXXXXX"},
    json={
        "model": "meta-llama/Llama-3.2-1B-Instruct",
        "messages": [{"role": "user", "content": "Hello!"}],
    },
)
print(resp.json()["choices"][0]["message"]["content"])
<?php
$ch = curl_init('https://api.tamga.studio/v1/chat/completions');
curl_setopt_array($ch, [
    CURLOPT_POST => true,
    CURLOPT_HTTPHEADER => [
        'Authorization: Bearer tl-sk-XXXXXX',
        'Content-Type: application/json',
    ],
    CURLOPT_POSTFIELDS => json_encode([
        'model' => 'meta-llama/Llama-3.2-1B-Instruct',
        'messages' => [['role' => 'user', 'content' => 'Hello!']],
    ]),
    CURLOPT_RETURNTRANSFER => true,
]);
echo curl_exec($ch);

Frequently Asked Questions

Common questions about this model.

What is Meta Llama 3.2 1B Instruct?
Llama 3.2 1B is a 1-billion-parameter language model focused on efficiently performing natural language tasks, such as summarization, dialogue, and multilingual text analysis. Its smaller size allows it to operate... It is accessible through a single OpenAI-compatible API via TamgaStudio.
How much does Meta Llama 3.2 1B Instruct cost?
Pricing is $0.02 per 1M input tokens and $0.02 per 1M output tokens. Billing is credit-based: you only pay for what you use.
What is the context length of Meta Llama 3.2 1B Instruct?
Meta Llama 3.2 1B Instruct supports a context of up to 131,072 tokens in one request.
Can I use Meta Llama 3.2 1B Instruct with the OpenAI SDK?
Yes. TamgaStudio exposes all models through an OpenAI-compatible endpoint: https://api.tamga.studio/v1/chat/completions. Just swap the base URL and API key in your existing OpenAI code.
How do I get an API key?
After signing in, create a key from the "API Keys" section of your dashboard. The key is shown only once and is stored as a SHA-256 hash.