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Meta · Open weights

Llama 3.1 8B

Meta’s small Llama: quick and very cheap for simple, high-volume text work.

Input$0.065per 1M tokens
Output$0.104per 1M tokens
BillingOne monthly invoiceevery model on one key
Request early accessSee every model’s rate

About Llama 3.1 8B

Llama 3.1 8B is the smallest model in Meta’s Llama 3.1 family and one of the most widely used open models anywhere. It has no reasoning mode and makes no pretence of one: it is fast, very inexpensive, follows instructions well for its size (80.4% on IFEval) and handles a 128K-token context.

Use it where the task is simple and the volume is high — routing messages, tagging, short summaries, pulling out a few fields. For anything that needs real reasoning, a 2026 small model will do much better.

Where it’s strong

Routing, tagging and extraction at very low cost.

Short summaries and simple chat.

Steps in a pipeline where speed matters more than depth.

Where to be careful

Its knowledge stops at December 2023.

Weak at maths and multi-step reasoning next to newer small models.

Meta’s licence carries use restrictions; it is not open source in the OSI sense.

Price

Input$0.065 per 1M tokens
Output$0.104 per 1M tokens
Cached input$0.0325 per 1M tokens

For scale: 1,000 requests of about 3,000 tokens in and 1,000 out cost $0.30 — a long prompt and a page of answer each. Every rate is published and stays the same from one request to the next. See every model.

Specs

Made byMeta
ReleasedJuly 2024
TypeChat
Context window128K tokens
ReadsText
Thinks before answeringNo
WeightsOpen (Llama 3.1 Community License)
Size8B, dense
Model idllama-3.1-8b

Call it

Use the OpenAI SDK you already have — point it at Roar AI and name the model. Read the docs.

from openai import OpenAI

client = OpenAI(base_url="https://api.roar-ai.com/v1", api_key="roar_live_…")

reply = client.chat.completions.create(
    model="llama-3.1-8b",
    messages=[{"role": "user", "content": "Summarise this contract in plain English."}],
)

Questions

How much does Llama 3.1 8B cost?

Input is $0.065 per 1M tokens; output is $0.104 per 1M tokens. For scale, 1,000 requests of about 3,000 tokens in and 1,000 out cost $0.30. The rate is published and does not change from one request to the next.

Can I use Llama 3.1 8B alongside other models?

Yes. Every model on Roar AI is on the same API key and the same monthly invoice, so switching from Llama 3.1 8B to another model is a change to one word in your code.

Is Llama 3.1 8B open source?

Its weights are open under the Llama 3.1 Community License licence, so you could run it yourself. Through Roar AI you call it like any other model, with no servers to manage.