Can you call Llama-4-Maverick-17B-128E-Instruct-FP8 with the OpenAI SDK you already use?

Yes. Llama-4-Maverick-17B-128E-Instruct-FP8 is reachable through an OpenAI-compatible endpoint, so an existing OpenAI SDK client works after pointing the base URL at MixRoute and setting the model parameter to llama-4-maverick-17b-128e-instruct-fp8 . Keep your current request and response handling.

How does Llama-4-Maverick-17B-128E-Instruct-FP8 compare with hy3, and what should you compare?

Llama-4-Maverick-17B-128E-Instruct-FP8 sits alongside hy3 , gemini-3.5-flash , qwen3.6-plus in the catalog. Compare them on the dimensions that change your bill and your users’ experience: task quality on your own prompts, latency at your concurrency, context and output limits, and cost per completed task rather than per token.

What is the rate limit for Llama-4-Maverick-17B-128E-Instruct-FP8?

The current model record does not publish a numeric rate limit for Llama-4-Maverick-17B-128E-Instruct-FP8. Confirm the quota for your plan in the console, then run a short load test at your expected concurrency to establish the effective ceiling before production.

When was Llama-4-Maverick-17B-128E-Instruct-FP8 released, and is it still maintained?

Llama-4-Maverick-17B-128E-Instruct-FP8 was released on 2025-04-05. Its catalog status is stable. Check the model page for deprecation notices before you build a long-lived integration on it.

How should you set the output budget for Llama-4-Maverick-17B-128E-Instruct-FP8?

Set max_tokens on Llama-4-Maverick-17B-128E-Instruct-FP8 to the longest response your task actually needs, not the 4,096-token ceiling. Keeping the cap tight reduces worst-case spend and shortens tail latency. Raise it only for tasks that demonstrably need longer output.

Model detail

Llama-4-Maverick-17B-128E-Instruct-FP8

Provided by Meta

Pay-as-you-go

FP8 weights of Meta's multimodal Llama 4 Maverick, using seventeen billion active parameters and 128 experts for language and image understanding.

Model specs

Model ID: llama-4-maverick-17b-128e-instruct-fp8

Context length: 1M
Max output: 4.096K
I/O modalities
Released: 2025-04

API endpoints

One MixRoute gateway, OpenAI-compatible

OpenAI-compatible /v1/chat/completions POST

Pricing

Flat rate, unit: /1M Tokens

Input

$0.2500 /1M Tokens

Completion

$1.0000 /1M Tokens

Selection summary

Quickly judge whether it fits your workload

What is this model good for?

FP8 weights of Meta's multimodal Llama 4 Maverick

FP8 weights of Meta's multimodal Llama 4 Maverick, using seventeen billion active parameters and 128 experts for language and image understanding.

What should you check before using it?

1,000,000 context, 4,096 output tokens

Meta model that supports tool calling. Knowledge cutoff: 2024-08. Released 2025-04-05.

Why use it through MixRoute?

Llama-4-Maverick-17B-128E-Instruct-FP8 takes text and images on one route

Text and image inputs share the same route and the same key. MixRoute serves Llama-4-Maverick-17B-128E-Instruct-FP8 on the OpenAI-compatible endpoint /v1/chat/completions; point your existing client at https://api.mixroute.ai/v1 with the model ID llama-4-maverick-17b-128e-instruct-fp8 and re-run your integration tests.

Capabilities & compatibility

Unconfirmed capabilities stay "Unknown," no guesses

Tool calling

Llama-4-Maverick-17B-128E-Instruct-FP8 supports function and tool calls.

EX Send a tools definition; the response returns tool_calls to execute and feed results back.

Structured output

Llama-4-Maverick-17B-128E-Instruct-FP8 can produce JSON-formatted output on demand.

EX response_format: { type: "json_schema" } forces valid JSON output.

Streaming

Llama-4-Maverick-17B-128E-Instruct-FP8 supports streamed responses.

EX stream: true streams tokens incrementally, so chat UIs render as they arrive.

Prompt caching

Llama-4-Maverick-17B-128E-Instruct-FP8 supports prompt caching under supported routing conditions.

EX Keep the system prompt and tool definitions at the start of the request; repeated prefixes are automatically billed at the cache-read rate.

Vision input

Llama-4-Maverick-17B-128E-Instruct-FP8 can process image inputs alongside text.

EX Pass a base64-encoded image in a image_url content part alongside text messages.

OpenAI SDK

Call Llama-4-Maverick-17B-128E-Instruct-FP8 through MixRoute's OpenAI-compatible endpoint.

EX Change base_url to https://api.mixroute.ai/v1; everything else follows the OpenAI style.

Token cost estimator

Live estimate from this page's pricing, not an actual bill

Input tokens Max per request 1M

Output tokens Max per request 4.096K

Requests / month

Cache hit rate –

This model has no cache-read price; caching is not counted

FAQ

Llama-4-Maverick-17B-128E-Instruct-FP8 is billed per use and the record publishes no free-tier allowance. The current published rates are input $0.2500 per 1M tokens and output $1.0000 per 1M tokens. Trial credit or promotional quota is tied to your account rather than to the model, so check the console before planning around a free tier.

Get started

01

Create an API key

Generate a key in the Console; one key calls every model.
02

Set the base URL

Fully OpenAI SDK-compatible; just swap the URL to connect.

https://api.mixroute.ai/v1
03

Specify the model ID

In the request's model field, enter:

llama-4-maverick-17b-128e-instruct-fp8

Create an API key

New users get $5 in free credits

Test this model and alternate routes with the same request format

Start from a real workload, then let quality, total cost, and failure conditions decide whether to send production traffic.

Create an API key View integration docs