API - Cerebras

Cerebras Inference API - Ultra-Fast AI Inference API Service

📋 Service Overview

Service Name: Cerebras Inference API
Provider: Cerebras Systems
API Endpoint: https://api.cerebras.ai/v1
Service Type: Free tier (1 million tokens daily)
Registration Requirements: Email only, no credit card required

✅ Service Description

Cerebras Inference API is an ultra-high-performance AI inference service provided by Cerebras Systems, built on revolutionary Wafer-Scale Engine (WSE) technology, offering 20x faster inference speed than traditional GPUs.

Key Features

🚀 Ultra-Fast Inference: Llama 4 Scout achieves 2,600+ tokens/s, 20x faster than GPUs
🎁 Free Tier: 1 million tokens free daily
🔌 OpenAI Compatible: Fully compatible with OpenAI API format for seamless migration
🤖 Mainstream Models: Supports Llama 3.1/4, Qwen 3, and other open-source large models

🎁 Available Models

Free/Trial Model List

Model Name	Context Length	Output Length	Features	Use Cases
llama-4-scout	8K	2K	🏆 Ultra-fast (2,600+ tokens/s)	Real-time apps, chatbots
llama-3.1-70b	128K	4K	High performance, large context	Long document processing, complex tasks
llama-3.1-8b	128K	4K	Fast, lightweight	Quick response, edge deployment
qwen-3-32b	128K	4K	Chinese optimized	Chinese applications

Detailed Model Information

Llama 4 Scout

Context Window: 8K tokens
Primary Use: Real-time conversation, quick Q&A
Advantage: Industry-leading inference speed at 2,600+ tokens/s

Llama 3.1 70B

Context Window: 128K tokens
Primary Use: Complex tasks, long document processing
Advantage: Balances high performance with ultra-long context

Llama 3.1 8B

Context Window: 128K tokens
Primary Use: Quick response scenarios
Advantage: Lightweight and fast, more cost-effective

Qwen 3-32B

Context Window: 128K tokens
Primary Use: Chinese dialogue and tasks
Advantage: Excellent Chinese language performance

🔢 Quotas and Limits

Free Tier Limits

Limit Item	Quota	Notes
Daily Tokens	1M tokens/day	Most mainstream models, daily reset
Rate Limits	Varies by model	Retry mechanism recommended
Max Context Length	Up to 128K tokens	Depends on specific model
Max Output Length	Up to 4K tokens	Depends on specific model
Concurrent Requests	Within reason	See official docs for specifics
Credit Card Required	❌	Completely free, no card needed

Note: Specific limits may vary by model. Please refer to the official Rate Limits documentation for the latest information.

⚠️ Important Limits

Daily Quota: 1 million tokens/day, resets at UTC 00:00
Model Availability: Model list may be updated anytime, refer to official documentation
Commercial Use: Free tier for development/testing only, contact sales for production

Quota Reset Time

Daily Quota: UTC 00:00 (Beijing time 08:00)
Usage Monitoring: Check remaining quota via API response headers or console

💰 Pricing

Free/Trial

Free Quota: 1 million tokens daily
How to Get: Simply register an account
Duration: Continuously free (policy subject to change)

Paid Pricing

For paid pricing, please contact Cerebras sales team: [email protected]

🚀 Getting Started

Prerequisites

1. Register Account

Please follow the Cerebras Registration Guide to complete account registration.

2. Get API Key

Log in to Developer Platform

Visit Cerebras Cloud and log in to your account.

Create API Key

Find “API Keys” in the left menu
Click “Create API Key”
Name your key (optional)
Click “Create”

Save API Key

Important: Copy the displayed API key
API key is shown only once, save it immediately to a secure location
Recommended to store key in environment variables

💻 Code Examples

Python Example

Method 1: Using OpenAI Client (Compatible Mode)

Install Dependencies:

Bash

pip install openai

Basic Usage:

Python

from openai import OpenAI

# Initialize client (using Cerebras API)
client = OpenAI(
    base_url="https://api.cerebras.ai/v1",
    api_key="YOUR_CEREBRAS_API_KEY"  # Replace with your API key
)

# Send request
response = client.chat.completions.create(
    model="llama-4-scout",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Explain what a Wafer-Scale Engine is"}
    ],
    max_tokens=1000,
    temperature=0.7
)

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

# Check token usage
print(f"\nTokens Used: {response.usage.total_tokens}")
print(f"Prompt Tokens: {response.usage.prompt_tokens}")
print(f"Completion Tokens: {response.usage.completion_tokens}")

Method 2: Using Official SDK (Recommended)

Install Dependencies:

Bash

pip install cerebras_cloud_sdk

Basic Usage:

Python

from cerebras.cloud.sdk import Cerebras
import os

# Initialize client
client = Cerebras(
    api_key=os.environ.get("CEREBRAS_API_KEY")
)

# Send request
response = client.chat.completions.create(
    model="llama-3.3-70b",
    messages=[
        {"role": "user", "content": "Explain what a Wafer-Scale Engine is"}
    ],
)

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

Streaming Output Example:

Python

# Streaming output (real-time display)
stream = client.chat.completions.create(
    model="llama-4-scout",
    messages=[
        {"role": "user", "content": "Write a poem about artificial intelligence"}
    ],
    stream=True
)

for chunk in stream:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="", flush=True)

Using Environment Variables:

Python

import os
from openai import OpenAI

# Read API key from environment variable
client = OpenAI(
    base_url="https://api.cerebras.ai/v1",
    api_key=os.getenv("CEREBRAS_API_KEY")
)

response = client.chat.completions.create(
    model="llama-3.1-70b",
    messages=[
        {"role": "user", "content": "What is machine learning?"}
    ]
)

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

cURL Example

Basic Request:

Bash

curl https://api.cerebras.ai/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_CEREBRAS_API_KEY" \
  -d '{
    "model": "llama-4-scout",
    "messages": [
      {
        "role": "system",
        "content": "You are a helpful assistant."
      },
      {
        "role": "user",
        "content": "Hello, tell me about Cerebras"
      }
    ],
    "max_tokens": 1000,
    "temperature": 0.7
  }'

Streaming Output:

Bash

curl https://api.cerebras.ai/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_CEREBRAS_API_KEY" \
  -d '{
    "model": "llama-4-scout",
    "messages": [
      {"role": "user", "content": "Hello"}
    ],
    "stream": true
  }'

Node.js Example

Install Dependencies:

Bash

npm install openai

Basic Usage:

JavaScript

import OpenAI from 'openai';

const client = new OpenAI({
  baseURL: 'https://api.cerebras.ai/v1',
  apiKey: process.env.CEREBRAS_API_KEY,
});

async function main() {
  const completion = await client.chat.completions.create({
    model: 'llama-4-scout',
    messages: [
      { role: 'system', content: 'You are a helpful assistant.' },
      { role: 'user', content: 'What is artificial intelligence?' }
    ],
    max_tokens: 1000,
    temperature: 0.7,
  });

  console.log(completion.choices[0].message.content);
  console.log(`\nTokens Used: ${completion.usage.total_tokens}`);
}

main();

Streaming Output:

JavaScript

async function streamExample() {
  const stream = await client.chat.completions.create({
    model: 'llama-4-scout',
    messages: [
      { role: 'user', content: 'Write a short story' }
    ],
    stream: true,
  });

  for await (const chunk of stream) {
    const content = chunk.choices[0]?.delta?.content || '';
    process.stdout.write(content);
  }
}

streamExample();

🌟 Core Advantages

Technical Advantages

Ultra-Fast Inference:
- Llama 4 Scout: 2,600+ tokens/s
- 20x faster than GPUs
- Millisecond-level response latency
Wafer-Scale Engine (WSE):
- 900,000 AI cores
- 40 Pbits/s on-chip bandwidth
- 44 GB high-speed SRAM
OpenAI Compatible:
- Seamless migration of existing code
- Supports streaming output
- Standard API format

Comparison with Other APIs

Feature	Cerebras	Groq	DeepSeek	Google AI Studio
Free Quota	1M tokens/day	~14,400 req/day	¥5 trial	Free usage
Inference Speed	🏆 2,600+ tokens/s	800+ tokens/s	Fast	Fast
OpenAI Compatible	✅	✅	✅	❌
Context Length	128K	128K	128K	2M
Credit Card Required	❌	✅	❌	❌

💡 Practical Recommendations

✅ Best Practices

Choose the Right Model:

# Real-time apps - ultimate speed
model = "llama-4-scout"

# Complex tasks - balanced performance and quality
model = "llama-3.1-70b"

# Chinese apps - Chinese optimized
model = "qwen-3-32b"

Use Streaming to Enhance UX:

# Streaming lets users see content in real-time
stream = client.chat.completions.create(
    model="llama-4-scout",
    messages=messages,
    stream=True
)

for chunk in stream:
    if chunk.choices[0].delta.content:
        print(chunk.choices[0].delta.content, end="", flush=True)

Implement Request Caching:

import hashlib
import json

cache = {}

def cached_request(model, messages):
    # Generate cache key
    key = hashlib.md5(
        json.dumps({"model": model, "messages": messages}).encode()
    ).hexdigest()

    # Check cache
    if key in cache:
        return cache[key]

    # Call API
    response = client.chat.completions.create(
        model=model,
        messages=messages
    )

    # Store in cache
    cache[key] = response
    return response

🎯 Best Practices

Maximize Free Quota:

1 million tokens daily is sufficient for development and testing
Monitor daily usage to avoid exceeding quota
Use free tier for dev and test environments

Optimize Token Usage:

Streamline system prompts
Control conversation history length
Set max_tokens appropriately

Monitor Quota Usage:

Python

# You can check usage in the Cerebras Cloud console
# Visit: https://cloud.cerebras.ai

Error Handling:

Python

import time
from openai import OpenAI, RateLimitError, APIError

client = OpenAI(
    base_url="https://api.cerebras.ai/v1",
    api_key="YOUR_CEREBRAS_API_KEY"
)

def call_api_with_retry(messages, max_retries=3):
    """API call with retry mechanism"""
    for attempt in range(max_retries):
        try:
            response = client.chat.completions.create(
                model="llama-4-scout",
                messages=messages
            )
            return response
        except RateLimitError:
            print(f"Rate limit reached, retrying... ({attempt + 1}/{max_retries})")
            time.sleep(2 ** attempt)  # Exponential backoff
        except APIError as e:
            print(f"API error: {e}")
            if attempt == max_retries - 1:
                raise
            time.sleep(1)
    
    return None

⚠️ Important Notes

Quota Management: 1 million tokens daily, resets at UTC 00:00, plan usage accordingly
Rate Limits: Despite being very fast, rate limits exist, implement retry mechanism
Model Selection: Choose appropriate model based on use case, balance speed and quality

🎯 Real-World Use Cases

Case 1: Real-Time Chatbot

Scenario: Building a real-time responsive chatbot

Python

from openai import OpenAI

client = OpenAI(
    base_url="https://api.cerebras.ai/v1",
    api_key="YOUR_CEREBRAS_API_KEY"
)

def chatbot():
    """Real-time chatbot"""
    messages = [
        {"role": "system", "content": "You are a friendly AI assistant"}
    ]
    
    print("Chatbot started! Type 'quit' to exit.\n")
    
    while True:
        user_input = input("You: ")
        if user_input.lower() == 'quit':
            break
        
        messages.append({"role": "user", "content": user_input})
        
        # Use streaming for real-time response
        print("AI: ", end="", flush=True)
        stream = client.chat.completions.create(
            model="llama-4-scout",
            messages=messages,
            stream=True
        )
        
        assistant_response = ""
        for chunk in stream:
            content = chunk.choices[0].delta.content or ""
            print(content, end="", flush=True)
            assistant_response += content
        
        print("\n")
        messages.append({"role": "assistant", "content": assistant_response})

# Run chatbot
chatbot()

Case 2: Batch Text Processing

Scenario: Process multiple text tasks in batch

Python

import asyncio
from openai import AsyncOpenAI

client = AsyncOpenAI(
    base_url="https://api.cerebras.ai/v1",
    api_key="YOUR_CEREBRAS_API_KEY"
)

async def process_text(text):
    """Process single text"""
    response = await client.chat.completions.create(
        model="llama-4-scout",
        messages=[
            {"role": "system", "content": "You are a text summarization expert"},
            {"role": "user", "content": f"Please summarize the following text:\n\n{text}"}
        ]
    )
    return response.choices[0].message.content

async def batch_process(texts):
    """Batch process texts"""
    tasks = [process_text(text) for text in texts]
    results = await asyncio.gather(*tasks)
    return results

# Usage example
texts = [
    "This is the first text...",
    "This is the second text...",
    "This is the third text..."
]

results = asyncio.run(batch_process(texts))
for i, summary in enumerate(results, 1):
    print(f"Summary {i}: {summary}\n")

Case 3: Code Generation Assistant

Scenario: Use AI to assist with code generation

Python

from openai import OpenAI

client = OpenAI(
    base_url="https://api.cerebras.ai/v1",
    api_key="YOUR_CEREBRAS_API_KEY"
)

def code_assistant(task_description):
    """Code generation assistant"""
    response = client.chat.completions.create(
        model="llama-3.1-70b",
        messages=[
            {
                "role": "system",
                "content": "You are a professional programming assistant skilled at generating high-quality code"
            },
            {
                "role": "user",
                "content": f"Please help me write code: {task_description}"
            }
        ],
        temperature=0.3  # Lower temperature for better code quality
    )
    
    return response.choices[0].message.content

# Usage example
task = "Implement binary tree preorder traversal in Python"
code = code_assistant(task)
print("Generated code:")
print(code)

🔧 Common Questions

Q: How do I check remaining quota?
A: Log in to Cerebras Cloud console to view API usage and remaining quota. It’s recommended to check usage regularly during development.

Q: What happens if I exceed daily quota?
A: API will return 429 error, need to wait until UTC 00:00 for quota reset, or upgrade to paid plan.

Q: Which programming languages are supported?
A: Since it’s OpenAI API compatible, all languages supporting OpenAI SDK can be used, including Python, Node.js, Go, Java, etc.

Q: Can I use it in production?
A: Free tier is mainly for development/testing, contact Cerebras sales team for enterprise solutions for production.

Q: Why is it so fast?
A: Cerebras uses Wafer-Scale Engine (WSE) with 900,000 cores and 40 Pbits/s bandwidth in a single chip, eliminating traditional GPU memory bottlenecks.

🔗 Related Resources

API Endpoint: https://api.cerebras.ai/v1
Developer Platform: https://cloud.cerebras.ai
API Documentation: https://inference-docs.cerebras.ai
Provider Homepage: Cerebras Systems
SDK Documentation: OpenAI Python SDK
GitHub: https://github.com/Cerebras

📝 Update Log

January 2024: Cerebras Inference API publicly launched with 1 million free tokens daily
2024: Added support for Llama 3 series models
2025: Added Llama 4 Scout, Qwen 3-32B, and other models

Service Provider: Cerebras Systems

Last updated on January 16, 2026