Utilisation de l'API

Utilisez les exemples de code suivants pour intégrer notre API :

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_API_KEY",
    base_url="https://llm.wavespeed.ai/v1"
)

response = client.chat.completions.create(
    model="google/gemma-3n-e4b-it",
    messages=[
        {"role": "user", "content": "Hello!"}
    ]
)

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

Introduction au modèle

Google gemma-3n-e4b-it

google gemma-3n-e4b-it

Why It Looks Great

Large Language Model architecture for efficient processing
32768 context window for long document handling
Competitive pricing at $0.0/$0.0 per million tokens

Key Features

Context Window: 32768 tokens
Max Output: 4096 tokens
Vision: Supported
Function Calling: Supported

Specifications

Specification	Value
Provider	Google
Model Type	Large Language Model (LLM)
Architecture	N/A
Context Window	32768 tokens
Max Output	4096 tokens
Input	Text
Output	Text
Vision	Supported
Function Calling	Supported

Pricing

Token Type	Cost per Million Tokens
Input	$0.0
Output	$0.0

How to Use

Write your prompt — describe the task, provide context, and specify desired output format.
Submit — the model processes your request and returns the response.

API Integration

Base URL: https://llm.wavespeed.ai/v1 API Endpoint: chat/completions Model ID: google/gemma-3n-e4b-it

API Usage

Python SDK

from openai import OpenAI

client = OpenAI(
    api_key="YOUR_API_KEY",
    base_url="https://llm.wavespeed.ai/v1"
)

response = client.chat.completions.create(
    model="google/gemma-3n-e4b-it",
    messages=[
        {"role": "user", "content": "Hello!"}
    ]
)

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

cURL

curl https://llm.wavespeed.ai/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -d '{
    "model": "google/gemma-3n-e4b-it",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

Notes

Model: google/gemma-3n-e4b-it
Provider: Google

google/gemma-3n-e4b-it

Tarification