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Groq-Llama-4-Maverick-17B-128E-Instruct via Llama

Specifications

Context Window

128,000 tokens

Release Date

2025-04-05

Capabilities

AttachmentsTool callingTemperature

Availability

Open Weights

Model Overview

Meta's Llama platform provides direct access to the LLaMA family of open-source language models, one of the most popular open-weight model families in the AI ecosystem.

Groq-Llama-4-Maverick-17B-128E-Instruct is a llama-family model by Llama with a 128k token context window and up to 4k output tokens. It is priced at $0.00/1M input tokens and $0.00/1M output tokens.

Key capabilities include: attachments, tool calling, temperature. It can call external tools and functions for agentic workflows.

Details

ProviderLlama
Model IDgroq-llama-4-maverick-17b-128e-instruct
Familyllama
Release Date2025-04-05
Last Updated2025-04-05
Knowledge Cutoff2025-01
Context Window128,000 tokens
Max Output4,096 tokens
Input Cost / 1M$0.00
Output Cost / 1M$0.00

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Frequently Asked Questions

How much does Groq-Llama-4-Maverick-17B-128E-Instruct cost to use?

Groq-Llama-4-Maverick-17B-128E-Instruct is priced at $0.00/1M input tokens and $0.00/1M output tokens. Use the cost estimator on this page to calculate your expected spend based on your usage pattern.

What is a token and how does it relate to pricing?

A token is a chunk of text — roughly ¾ of a word in English. For example, "chatbot" is two tokens. LLM API pricing is based on the number of tokens you send (input) and receive (output). Input tokens include your prompts, uploaded documents, and images, while output tokens are the model's generated responses.

Why are input and output tokens priced differently?

LLM providers charge separately for input and output tokens. Output tokens are typically more expensive because generating each token requires more compute — the model must run a full forward pass for every token it produces, while input tokens are processed in parallel.

What is the context window of Groq-Llama-4-Maverick-17B-128E-Instruct?

Groq-Llama-4-Maverick-17B-128E-Instruct supports a context window of 128,000 tokens. This is the maximum number of tokens (input + output combined) the model can process in a single request. Larger context windows let you send longer documents or maintain longer conversation histories.

How accurate is this cost estimation?

This tool provides a ballpark estimate based on per-token pricing. Actual costs may differ due to prompt caching, batched API calls, volume discounts, reasoning token overhead, and provider-specific billing rules. Use it for budgeting and comparison, not as an invoice prediction.

How does Groq-Llama-4-Maverick-17B-128E-Instruct pricing compare to other models?

You can compare Groq-Llama-4-Maverick-17B-128E-Instruct with other models on our LLM API pricing calculator. Use the cost estimator to see side-by-side cost breakdowns across different providers and models to find the best fit for your budget and requirements.

What factors affect my total API cost?

Your total cost depends on several factors: the number of API calls you make, the length of your prompts (input tokens), the length of generated responses (output tokens), whether you use features like image or document uploads (which add input tokens), and any provider-specific charges for caching or batch processing.

How can my team use Groq-Llama-4-Maverick-17B-128E-Instruct via API?

You can connect your own Llama API key and give your entire team access to Groq-Llama-4-Maverick-17B-128E-Instruct through TypingMind Teams. It lets you build a unified AI workspace where team members can use Groq-Llama-4-Maverick-17B-128E-Instruct and other models — without needing their own API keys. You stay in control of usage limits, costs, and permissions, all from a single dashboard.

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