Meta Llama/operational

Meta: Llama 4 Maverick

Llama 4 Maverick 17B Instruct (128E) is a high-capacity multimodal language model from Meta, built on a mixture-of-experts (MoE) architecture with 128 experts and 17 billion active parameters per forward...

Input / output per 1M

$0.19/$0.65

Get an API key

function-callingvisionopen weights

128K

Context window

tokens

16K

Max output

tokens per reply

113ms

Time to first token

p50, EU region

$0.65

Output price

per 1M tokens

Fit

Where this model earns its place

function calling

Tool use and structured output — dependable JSON for agent loops, RAG pipelines, and workflow automation.

vision

Image input — screenshots, scanned documents, charts, and photographed forms, without a separate OCR step.

Data handling

Requests are processed in the EU. Prompts are not used for training, and logs follow the retention period in your contract — zero-retention is available on request.

Specifications

Model

Model ID: meta-llama/llama-4-maverick
Provider: Meta Llama
Context length: 128K
Max output: 16K
Modality: text + image
Licence: Open Weight

Commercial

Input: $0.19
Output: $0.65
Cached input: $0.05
Deployment: serverless
Availability: operational
Last updated: 2026-10-26

Cost

What a month actually costs

Workload	Input tokens	Output tokens	Monthly cost
Prototype	1.0M	250K	$0.35
Production	50.0M	12.0M	$17.20
At scale	500.0M	120.0M	$172.05

Illustrative volumes at list price, excluding cached-input discounts and any committed-use agreement. Your invoice shows the same rates, itemised per key.

Cheaper by caching

Repeated prompts — system instructions, retrieved context, few-shot examples — are billed at the cached-input rate where the model supports it. On long system prompts this is typically the largest single line-item saving.

API

Same SDK, different base URL

cURL

curl https://api.itciphtech.com/v1/chat/completions \
  -H "Authorization: Bearer $CIPHER_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "meta-llama/llama-4-maverick",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

TypeScript

import OpenAI from "openai";

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

const res = await client.chat.completions.create({
  model: "meta-llama/llama-4-maverick",
  messages: [{ role: "user", content: "Hello!" }],
});

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