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

A lightweight and ultra-fast variant of Llama 3.3 70B, for use when quick response times are needed most.

Try meta-llama/llama-4-maverick-17b-128e-instruct-fp8CompareDocs

Specifications

Context window1.0M tokens
Max output1.0M tokens
API typechat
AddedJan 30, 2025
Model IDnovita/meta-llama/llama-4-maverick-17b-128e-instruct-fp8
Data retentionYes
Used for trainingUnknown
Provider location🇺🇸 US
Privacy policyNovita AI Privacy Policy→

Benchmarks

Benchmarks haven't been published yet for this exact variant.

Some variants (region-specific deployments, highspeed tiers) share benchmarks with their base model. Check the base model page or the Novita AI models overview.

Pricing

Input / 1M
$0.20
Output / 1M
$0.85
Cache write
N/A
Cache read
N/A
Estimated cost
100K input + 10K output$0.0285
1M input + 100K output$0.28
10M input + 1M output$2.85

Requesty charges exactly what the upstream provider charges, no markup, no per-request fees. Prompt caching and smart routing can reduce effective cost by 30-80%.

Quickstart

Drop-in compatible with the OpenAI SDK. Change the base URL, swap in your Requesty API key, and set the model to novita/meta-llama/llama-4-maverick-17b-128e-instruct-fp8.

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from openai import OpenAI

client = OpenAI(
    api_key="YOUR_REQUESTY_API_KEY",
    base_url="https://router.requesty.ai/v1",
)

response = client.chat.completions.create(
    model="novita/meta-llama/llama-4-maverick-17b-128e-instruct-fp8",
    messages=[
        {"role": "user", "content": "Explain quantum computing in one paragraph."},
    ],
)

print(response.choices[0].message.content)

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Frequently asked questions

How much does meta-llama/llama-4-maverick-17b-128e-instruct-fp8 cost?
meta-llama/llama-4-maverick-17b-128e-instruct-fp8 is priced at $0.20 per million input tokens and $0.85 per million output tokens when accessed via Requesty. Requesty charges exactly what the upstream provider charges, we don't add markup.
What is the context window of meta-llama/llama-4-maverick-17b-128e-instruct-fp8?
meta-llama/llama-4-maverick-17b-128e-instruct-fp8 has a context window of 1.0M tokens, with a maximum output of 1.0M tokens per response. That's roughly 1,398 words of input you can fit in a single prompt.
What can meta-llama/llama-4-maverick-17b-128e-instruct-fp8 do?
meta-llama/llama-4-maverick-17b-128e-instruct-fp8 is a text-generation model you can call through any OpenAI-compatible client by pointing base_url to Requesty.
How do I use meta-llama/llama-4-maverick-17b-128e-instruct-fp8 with the OpenAI SDK?
Install the OpenAI SDK, set base_url to "https://router.requesty.ai/v1", set your API key to your Requesty key, and set the model to "novita/meta-llama/llama-4-maverick-17b-128e-instruct-fp8". The Quickstart above shows Python, JavaScript and cURL snippets.
Can I run meta-llama/llama-4-maverick-17b-128e-instruct-fp8 through Requesty?
Yes. meta-llama/llama-4-maverick-17b-128e-instruct-fp8 runs through Requesty's OpenAI-compatible API, served from Novita AI. You do not host the model yourself: point base_url at Requesty, set the model to "novita/meta-llama/llama-4-maverick-17b-128e-instruct-fp8", and requests are routed to the upstream provider with automatic failover. The same key gives you 400+ other models too.

Access meta-llama/llama-4-maverick-17b-128e-instruct-fp8 through Requesty

One API key, 400+ models, OpenAI-compatible. No markup on provider prices, automatic failover, and smart caching built-in.

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