How much does Llama 4 Maverick API cost on Routeway?

Llama 4 Maverick costs $0.11/1M input tokens on Routeway, offering competitive pricing compared to meta's direct rates.

Is Llama 4 Maverick compatible with OpenAI's API format?

Yes, Llama 4 Maverick on Routeway uses the same API format as OpenAI, making it easy to switch with zero code changes. Simply update your API endpoint and key.

What is the context window for Llama 4 Maverick?

Llama 4 Maverick supports a context window of 1,048,576 tokens, allowing for extensive conversations and document processing.

How do I get started with Llama 4 Maverick on Routeway?

Getting started is easy: 1) Create a Routeway account, 2) Generate your API key, 3) Use our OpenAI-compatible endpoint with Llama 4 Maverick as the model parameter.

Back to Models

Llama 4 Maverick

Available

Description

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 pass (400B total). It supports multilingual text and image input, and produces multilingual text and code output across 12 supported languages. Optimized for vision-language tasks, Maverick is instruction-tuned for assistant-like behavior, image reasoning, and general-purpose multimodal interaction. Maverick features early fusion for native multimodality and a 1 million token context window. It was trained on a curated mixture of public, licensed, and Meta-platform data, covering ~22 trillion tokens, with a knowledge cutoff in August 2024. Released on April 5, 2025 under the Llama 4 Community License, Maverick is suited for research and commercial applications requiring advanced multimodal understanding and high model throughput.

At a Glance

Key pricing and model details available for this model.

Input price

$0.11

per 1M tokens

Output price

$0.44

per 1M tokens

Context window

1.0M

tokens

Hallucination rate

5.56%

Token Pricing

Token pricing normalized to per-million-token rates.

Input / 1M tokens

$0.11

Output / 1M tokens

$0.44

Cache Read / 1M tokens

$0.02

Token Pricing Details

Rates are shown per 1M tokens for easier comparison.

Input / 1M tokens	$0.11
Input unit	1M tokens
Output / 1M tokens	$0.44
Output unit	1M tokens
Cache Read / 1M tokens	$0.02
Cache Read unit	1M tokens

Feature Availability

Capabilities explicitly listed in the current payload.

LLM

Available

Yes

Vision

Available

Yes

Function calling

Available

Yes

Reasoning

Not listed

Supported Parameters

frequency_penalty

logit_bias

max_completion_tokens

presence_penalty

reasoning_effort

response_format

stop

temperature

tool_choice

tools

top_p

Artificial Analysis

Index scores currently reported for this model.

Intelligence Index

18.4

Coding Index

15.6

Math Index

19.3

Category Radar

Aggregated from the benchmark values present for reasoning, code, math, and accuracy.

Benchmark Breakdown

Detailed benchmark results drawn from the current payload.

Intelligence Index

index

Overall 'how smart' score for an AI, combining reasoning, math, coding, and knowledge.

18.4

Reported score

Coding Index

index

How well the model handles real programming tasks.

15.6

Reported score

Math Index

index

Composite score measuring mathematical reasoning and problem-solving.

19.3

Reported score

MMLU-Pro

reasoning

A broad and difficult knowledge-and-reasoning benchmark across many subjects.

80.9%

Reported score

GPQA

reasoning

Graduate-level science questions designed to be difficult to shortcut.

67.1%

Reported score

HLE

reasoning

A very hard expert-level exam across a wide range of subjects.

4.8%

Reported score

LiveCodeBench

code

Fresh programming tasks meant to test current coding ability.

39.7%

Reported score

SciCode

code

Coding tasks drawn from real scientific workflows.

33.1%

Reported score

MATH-500

math

A set of difficult competition-style math problems.

88.9%

Reported score

AIME

math

Advanced math competition questions.

39%

Reported score

AIME 2025

math

The 2025 AIME benchmark used to reduce data leakage concerns.

19.3%

Reported score

IFBench

accuracy

Measures how precisely the model follows detailed instructions.

43%

Reported score

LCR

accuracy

Tests long-context reasoning over large documents and conversations.

46%

Reported score

TerminalBench Hard

code

A harder coding-agent benchmark for complex multi-step terminal tasks.

6.8%

Reported score

Tau2

accuracy

Evaluates realistic agent behavior in tool-using support workflows.

17.8%

Reported score

Code Samples

Quick start with the Routeway API

import OpenAI from 'openai';

const openai = new OpenAI({
  baseURL: "https://api.routeway.ai/v1",
  apiKey: "<YOUR_API_KEY>",
});

async function main() {
  const completion = await openai.chat.completions.create({
    model: "llama-4-maverick",
    messages: [
      {
        role: "user",
        content: "Explain quantum computing in simple terms"
      }
    ]
  });

  console.log(completion.choices[0].message);
}

main();