Does Llama 4 Maverick 17B 128E Instruct support zero data retention?

Yes. Llama 4 Maverick 17B 128E Instruct is available with zero data retention on SCX through the Opper gateway.

Where is Llama 4 Maverick 17B 128E Instruct hosted?

See the per-route table for Llama 4 Maverick 17B 128E Instruct hosting locations and residency details.

Is Llama 4 Maverick 17B 128E Instruct trained on prompts and outputs?

No. Every hosting route for Llama 4 Maverick 17B 128E Instruct is configured so customer prompts and outputs are not used to train models by default.

Is a GDPR DPA available for Llama 4 Maverick 17B 128E Instruct?

Yes. A GDPR Data Processing Agreement is available on at least one route serving Llama 4 Maverick 17B 128E Instruct. Contact hello@opper.ai to receive it.

Llama 4 Maverick 17B 128E Instruct

by Meta

Llama 4 Maverick is Meta's general-purpose multimodal model from the Llama 4 family, released on April 5, 2025. It is a mixture-of-experts design with 128 experts, 17 billion active parameters, and 400 billion total, alternating dense and MoE layers. Text and vision are fused early in the model, so it is natively multimodal: it takes text and images and returns text and code. Meta trained it on a mixture of more than 30 trillion tokens of text, image, and video data, with a knowledge cutoff of August 2024, and officially supports 12 languages including Arabic, English, French, German, Hindi, Spanish, Thai, and Vietnamese. On Meta's numbers the instruct model scores 80.5 on MMLU Pro, 69.8 on GPQA Diamond, 43.4 on LiveCodeBench, and 94.4 on DocVQA, and Meta described it at launch as beating GPT-4o and Gemini 2.0 Flash across a broad range of benchmarks. The weights are open under the Llama 4 Community License. It fits assistant-style chat, visual reasoning, image captioning, and document question answering across multiple languages.

Call Llama 4 Maverick 17B 128E Instruct on Opper with the OpenAI SDK. Sign up without a credit card. Get started

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Key info

Input

Output

Features

Context window

131K

Max output

131K

Input price

$1.13 /1M

Output price

$3.43 /1M

Zero data retention on pay-as-you-go
No training by default
GDPR DPA available

Available routes

Llama 4 Maverick 17B 128E Instruct runs on 1 route through the Opper gateway. Compare residency, zero data retention and training posture at a glance, with full data-handling detail per route below.

Provider	Region	Zero data retention	Training	Input	Output	Cache read
SCX	Australia		No	$1.13	$3.43	Input price

Live routes and prices as JSON: api.opper.ai/v3/models?q=scx%3Aau%2Fllama-4-maverick-17b-128e-instruct

Data handling per route

Each route hosting Llama 4 Maverick 17B 128E Instruct has its own privacy posture, residency, and GDPR terms. Postures are maintained by Opper with a last-verification timestamp.

SCX — Australia🇦🇺

Zero data retention: nothing is logged, held for abuse monitoring or used for training. No training on customer data. AU; unknown; DPA available.

Zero data retention: Yes. Nothing is logged, held for abuse monitoring or used for training.
Training: No training on customer data.
Logging: None
Abuse monitoring: Optional (off by default), keeps nothing
GDPR DPA: DPA available
Transfer mechanism: unknown

Get started

Call Llama 4 Maverick 17B 128E Instruct through the Opper gateway with one API key. Opper is drop-in compatible with the OpenAI, Anthropic and Google AI SDKs.

Llama 4 Maverick 17B 128E Instruct is a premium model on Opper. Sign up needs no credit card: you get an API key straight away and the free models work in the playground and the API. Add a card to use premium models, pay-as-you-go with no minimum.

Set it up with your agent

Copy this and paste it into a coding agent like Claude Code, Cursor or Codex and it'll wire up Opper for you.

Or call it directly

1import OpenAI from "openai";
2
3const client = new OpenAI({
4  apiKey: process.env.OPPER_API_KEY,
5  baseURL: "https://api.opper.ai/v3/compat",
6});
7
8const completion = await client.chat.completions.create({
9  model: "scx:au/llama-4-maverick-17b-128e-instruct",
10  messages: [{ role: "user", content: "Hello" }],
11});
12console.log(completion.choices[0].message.content);