The Open Models That Made Open Weights the Default
Llama is Meta's family of open-weight models, and its influence is easiest to measure by what was built around it. The tooling most developers use to run, quantize, and fine-tune models locally was written for Llama first. More fine-tuned derivatives exist for this family than for any other. It spans a wider range of sizes than any competing line — from models small enough for a laptop to sparse Mixture-of-Experts systems with context windows in the millions — with native multimodal input in the current generation. On DEVUP AI the family is reachable through the standard OpenAI-compatible endpoint, billed in Algerian Dinar.
The most consequential thing about Llama is not any single model. It is that Meta published the weights when publishing weights was not the norm, and kept doing it across generations and sizes. That decision reset what developers expected from an open model, and the effect compounded: the tooling ecosystem for running models locally — quantization formats, inference runtimes, fine-tuning frameworks — was largely built for Llama first and generalised outward afterwards. If a technique works on any open model, it usually worked here first.
Breadth is the family's other distinguishing property. It spans models small enough to run on a laptop through to sparse Mixture-of-Experts systems at frontier scale. No other open family covers that range, and it matters in practice: the same architecture, prompt format, and tooling carry from a prototype on a developer's machine to a production deployment, without a rewrite in between.
The current generation moved to a Mixture-of-Experts design, activating a fraction of total parameters on any given token, and introduced native multimodality — images and text handled by one model rather than a vision component attached to a language model. One model in this generation carries a context window measured in millions of tokens, among the largest available in open weights.
Because the weights are public, this is also the family with by far the most derivatives. Domain-specific fine-tunes, instruction variants, quantized builds, and language-specific adaptations exist in the thousands. When a project needs a model shaped for a narrow task rather than a general one, a Llama derivative is usually the shortest path to it.
One thing to read rather than assume: Llama models are released under Meta's own community licence rather than a standard permissive one such as MIT or Apache 2.0. It permits commercial use for the overwhelming majority of companies, with conditions that apply at very large scale. Review the terms on the individual model page before committing to a commercial deployment. On DEVUP AI the family is reachable through the same OpenAI-compatible endpoint as the rest of the catalogue, with billing in Algerian Dinar and local payment methods.
Available Llama Models
Deploy and access all published models in this family with unified DZD pricing and zero foreign card requirement.
| Model | Context | DZD in / 1M tokens | DZD out / 1M tokens | Actions |
|---|---|---|---|---|
| Meta-Llama-3.1-70B-Instruct-Turbometa-llama/Meta-Llama-3.1-70B-Instruct-Turbo | 131K Tokens | 140 | — | View model |
| Llama-Guard-4-12Bmeta-llama/Llama-Guard-4-12B | 163k Tokens | 63 | — | View model |
| Llama-4-Scout-17B-16E-Instructmeta-llama/Llama-4-Scout-17B-16E-Instruct | 327K Tokens | 35 | 105 | View model |
| Llama-4-Maverick-17B-128E-Instruct-FP8meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8 | 1024K Tokens | 52.5 | 210 | View model |
| Llama-3.3-70B-Instruct-Turbometa-llama/Llama-3.3-70B-Instruct-Turbo | 128K Tokens | 35 | 112 | View model |
| Meta-Llama-3.1-8B-Instruct-Turbometa-llama/Meta-Llama-3.1-8B-Instruct-Turbo | 131K Tokens | 7 | 14 | View model |
Call any Llama model
Every model on this page uses the same API key and standard OpenAI-compatible endpoint. The example below uses the featured model meta-llama/Meta-Llama-3.1-70B-Instruct-Turbo.
from openai import OpenAI
import os
client = OpenAI(
api_key=os.environ["DEVUP_API_KEY"],
base_url="https://api.devupai.com/v1"
)
response = client.chat.completions.create(
model="Llama-3.3-70B-Instruct-Turbo",
messages=[
{"role": "user", "content": "Hello world!"}
],
max_tokens=1024
)
print(response.choices[0].message.content)import OpenAI from "openai";
const client = new OpenAI({
apiKey: process.env.DEVUP_API_KEY,
baseURL: "https://api.devupai.com/v1",
});
async function main() {
const response = await client.chat.completions.create({
model: "Llama-3.3-70B-Instruct-Turbo",
messages: [
{ role: "user", content: "Hello world!" },
],
max_tokens: 1024,
});
console.log(response.choices[0]?.message?.content);
}
main();curl -X POST "https://api.devupai.com/v1/chat/completions" \
-H "Authorization: Bearer $DEVUP_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "Llama-3.3-70B-Instruct-Turbo",
"messages": [
{ "role": "user", "content": "Hello world!" }
],
"max_tokens": 1024
}'Frequently Asked Questions
Match the size to the task, since this family gives you an unusually wide range to choose from. Compact models handle classification, routing, and extraction at high volume. Mid-size models cover general assistant work and code. The larger Mixture-of-Experts models are for long documents, complex reasoning, and cases where a small model measurably falls short. Check the individual model page for context limits and input types — they vary considerably across the family.
The weights are public, but the licence is Meta's own community licence rather than a standard permissive one like MIT or Apache 2.0. It allows commercial use for the vast majority of companies, with additional conditions that apply at very large user scale. If you are building something commercial, read the licence on the model page rather than assuming it works like MIT.
Some can. The current generation includes natively multimodal models that accept images alongside text in the same request, and earlier generations included dedicated vision variants. Support is per model rather than family-wide, so check the individual model page rather than assuming it.
Because the weights are public and the tooling matured around them first. Fine-tuning frameworks, quantization formats, and local runtimes all target this family well, which makes producing a derivative cheaper here than anywhere else. The practical consequence is that when you need a model shaped for a narrow domain rather than a general one, a Llama derivative often already exists.
No. Every model in the family is called through the same OpenAI-compatible chat completions endpoint, so moving between sizes is a change to the model field. What does vary is context limits, input types, and recommended sampling settings — read the individual model page for those before switching a production path.
Llama's strengths are breadth, ecosystem, and the sheer weight of tooling and derivatives built on it. Newer open families from other developers have moved ahead on specific axes — long-context efficiency, agentic coding, reasoning depth — and often ship under more permissive licences. Choose on the axis that matters for your workload rather than on family reputation, and compare the individual model pages.
Yes, unlike most closed frontier families. Parameter counts, expert configurations, context limits, and training approach are documented, and the weights are downloadable. That transparency is a large part of why the research and tooling ecosystem clustered around this family.
