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...
Specifications
Quick start
Get an API key →import OpenAI from 'openai'
const client = new OpenAI({
apiKey: process.env.HANZO_API_KEY,
baseURL: 'https://api.hanzo.ai/v1'
})
const response = await client.chat.completions.create({
model: 'meta-llama/llama-4-maverick',
messages: [{ role: 'user', content: 'Hello!' }]
})
console.log(response.choices[0].message.content)from openai import OpenAI
client = OpenAI(
api_key=os.environ["HANZO_API_KEY"],
base_url="https://api.hanzo.ai/v1"
)
response = client.chat.completions.create(
model="meta-llama/llama-4-maverick",
messages=[{"role": "user", "content": "Hello!"}]
)
print(response.choices[0].message.content)curl https://api.hanzo.ai/v1/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $HANZO_API_KEY" \
-d '{
"model": "meta-llama/llama-4-maverick",
"messages": [{"role": "user", "content": "Hello!"}]
}'package main
import (
"context"
"fmt"
"os"
"github.com/sashabaranov/go-openai"
)
func main() {
cfg := openai.DefaultConfig(os.Getenv("HANZO_API_KEY"))
cfg.BaseURL = "https://api.hanzo.ai/v1"
client := openai.NewClientWithConfig(cfg)
resp, _ := client.CreateChatCompletion(context.Background(),
openai.ChatCompletionRequest{
Model: "meta-llama/llama-4-maverick",
Messages: []openai.ChatCompletionMessage{
{Role: openai.ChatMessageRoleUser, Content: "Hello!"},
},
},
)
fmt.Println(resp.Choices[0].Message.Content)
}More from Meta
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