Cerebras Inference API: The Fastest Free AI API You’ve Never Heard Of

What Is Cerebras? The Chip Company That’s Also a Free AI API

Cerebras Systems is best known for building the Wafer-Scale Engine (WSE) — a chip the size of a dinner plate with over 4 trillion transistors, purpose-built for AI. What most developers don’t realize is that Cerebras also offers a free cloud inference API that consistently outpaces Groq on smaller models and rivals it on 70B-class models.

If you’ve only heard of Groq as the “fast free AI API,” it’s time to put Cerebras on your radar. No credit card required, OpenAI-compatible endpoint, and benchmarks that speak for themselves.

In this guide, you’ll learn how to get your free Cerebras API key, make your first call with Python or JavaScript, and connect it to OpenClaw for a fully free, ultra-fast AI agent.

Why Cerebras Is So Fast: The Chip Story in 60 Seconds

To understand why Cerebras is fast, you need to understand why GPUs are slow at inference.

When you run a model on a GPU cluster, the model’s weights live in external HBM (High Bandwidth Memory). Every time the chip generates a token, it has to pull weights from that external memory. This memory bandwidth bottleneck is the core reason GPU inference tops out around 100–150 tokens per second, even on expensive A100s.

The Cerebras WSE-3 is different. At 46,225 mm², it’s 57x larger than the biggest GPU die. The entire Llama 3.1 70B model — all 70 billion parameters — fits directly in the chip’s on-chip SRAM. There’s no external memory fetch, no bandwidth bottleneck. The chip just computes.

The result is inference speeds that most GPU providers can’t touch:

• Llama 3.1 8B: ~2,100 tokens/second
• Llama 3.1 70B: ~450–500 tokens/second
• Llama 3.3 70B: ~450 tokens/second

For context, Groq runs Llama 3.3 70B at around 300–500 tokens/second. OpenAI GPT-4o is closer to 50–100. Cerebras is legitimately the fastest publicly accessible AI inference in 2026.

Available Free Models on Cerebras

Cerebras’ free tier gives you access to several high-quality open-source models:

Model ID	Parameters	Context Window	Speed (approx)	Best For
llama3.1-8b	8B	8K tokens	~2,100 tokens/s	Maximum speed, chat, code
llama3.1-70b	70B	8K tokens	~500 tokens/s	Higher quality, reasoning
llama-3.3-70b	70B	8K tokens	~450 tokens/s	Best quality on Cerebras
qwen-3-32b	32B	32K tokens	~700 tokens/s	Multilingual, coding

Note that model availability may be updated over time. Check the Cerebras Cloud Console for the current model list.

Free Tier Rate Limits

Cerebras’ free tier is genuinely usable for development and side projects:

Limit Type	Free Tier
Requests per minute	30 RPM
Tokens per minute	60,000 TPM
Requests per day	~900 RPD
Credit card required	No

The 60,000 tokens per minute is especially generous. At ~2,100 tokens/second for the 8B model, you could burn through an entire minute’s TPM allowance in about 30 seconds of continuous generation — but in practice, request latency means you won’t hit that ceiling often. For typical interactive workloads, the free tier is more than enough.

How to Get Your Free Cerebras API Key

1. Go to cloud.cerebras.ai and create an account (email, Google, or GitHub)
2. After logging in, click “API Keys” in the left sidebar
3. Click “Create new API key” and give it a name
4. Copy the key — it’s only shown once

No credit card, no billing form, no trial period countdown. You’re making API calls in under two minutes.

Using the Cerebras API with Python

Option 1: Install the Official Cerebras SDK

pip install cerebras-cloud-sdk

Basic Chat Completion

from cerebras.cloud.sdk import Cerebras

client = Cerebras(api_key="YOUR_CEREBRAS_API_KEY")

response = client.chat.completions.create(
    model="llama-3.3-70b",
    messages=[
        {"role": "system", "content": "You are a helpful coding assistant."},
        {"role": "user", "content": "Write a Python function that checks if a number is prime"}
    ]
)

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

Streaming Responses

With Cerebras generating 2,100 tokens/second on the 8B model, streaming feels nearly instantaneous — tokens arrive faster than you can read them:

from cerebras.cloud.sdk import Cerebras

client = Cerebras(api_key="YOUR_CEREBRAS_API_KEY")

stream = client.chat.completions.create(
    model="llama3.1-8b",
    messages=[{"role": "user", "content": "Explain Python decorators with three practical examples"}],
    stream=True
)

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

Option 2: Use the OpenAI SDK (Drop-in Replacement)

Cerebras is fully OpenAI-compatible. If your project already uses the OpenAI Python SDK, you only change the base URL and model name:

from openai import OpenAI

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

response = client.chat.completions.create(
    model="llama-3.3-70b",
    messages=[
        {"role": "user", "content": "What are the main differences between PostgreSQL and SQLite?"}
    ]
)

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

This makes it trivially easy to add Cerebras as a fast fallback or alternative in any project that already supports OpenAI.

Async Support

The Cerebras SDK also supports async/await for high-throughput applications:

import asyncio
from cerebras.cloud.sdk import AsyncCerebras

async def batch_classify(texts: list[str]) -> list[str]:
    client = AsyncCerebras(api_key="YOUR_CEREBRAS_API_KEY")
    results = []

    for text in texts:
        response = await client.chat.completions.create(
            model="llama3.1-8b",
            messages=[
                {
                    "role": "user",
                    "content": f"Classify this text as positive, negative, or neutral. Reply with one word only.\n\n{text}"
                }
            ]
        )
        results.append(response.choices[0].message.content.strip())

    return results

texts = [
    "This product exceeded my expectations!",
    "Totally disappointed, waste of money.",
    "It arrived on time and works fine."
]

labels = asyncio.run(batch_classify(texts))
print(labels)
# ['positive', 'negative', 'neutral']

JSON Mode

Force structured JSON output — essential for building data pipelines and parsers:

import json
from cerebras.cloud.sdk import Cerebras

client = Cerebras(api_key="YOUR_CEREBRAS_API_KEY")

response = client.chat.completions.create(
    model="llama-3.3-70b",
    messages=[
        {
            "role": "user",
            "content": (
                "Extract the following from this job posting as JSON:\n"
                "- title (string)\n- company (string)\n- salary_range (string or null)\n- remote (boolean)\n\n"
                "Posting: 'Senior Python Engineer at DataCorp, $140k–$170k, fully remote position.'"
            )
        }
    ],
    response_format={"type": "json_object"}
)

data = json.loads(response.choices[0].message.content)
print(data)
# {"title": "Senior Python Engineer", "company": "DataCorp", "salary_range": "$140k–$170k", "remote": true}

Using the Cerebras API with JavaScript / Node.js

npm install @cerebras/cerebras_cloud_sdk

import Cerebras from "@cerebras/cerebras_cloud_sdk";

const client = new Cerebras({ apiKey: process.env.CEREBRAS_API_KEY });

const response = await client.chat.completions.create({
  model: "llama-3.3-70b",
  messages: [
    { role: "user", content: "Write a TypeScript interface for a REST API response with pagination" }
  ]
});

console.log(response.choices[0].message.content);

Streaming in Node.js

import Cerebras from "@cerebras/cerebras_cloud_sdk";

const client = new Cerebras({ apiKey: process.env.CEREBRAS_API_KEY });

const stream = await client.chat.completions.create({
  model: "llama3.1-8b",
  messages: [{ role: "user", content: "Explain event loops in JavaScript" }],
  stream: true
});

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

Using Cerebras API with the OpenAI SDK (Any Language)

Because Cerebras uses an OpenAI-compatible endpoint, you can plug it into any library or framework that supports custom base URLs. Here’s a quick reference:

Field	Value
Base URL	https://api.cerebras.ai/v1
API Key Header	Authorization: Bearer YOUR_KEY
Chat endpoint	POST /v1/chat/completions
Models endpoint	GET /v1/models

This means Cerebras works as a drop-in replacement anywhere you use OpenAI — LangChain, LlamaIndex, LiteLLM, OpenWebUI, and hundreds of other tools.

Using with LiteLLM (One Line of Code)

pip install litellm

from litellm import completion
import os

os.environ["CEREBRAS_API_KEY"] = "YOUR_CEREBRAS_API_KEY"

response = completion(
    model="cerebras/llama-3.3-70b",
    messages=[{"role": "user", "content": "What are the SOLID principles?"}]
)

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

LiteLLM has native Cerebras support, making it effortless to switch between Cerebras, Groq, OpenAI, and other providers in the same codebase.

Connect Cerebras to OpenClaw (Free Ultra-Fast AI Agent)

OpenClaw is an open-source AI agent platform that supports custom API endpoints. Connecting it to Cerebras gives you an AI coding agent with response times that feel nearly instant.

Quick Setup via Onboarding

npm install -g openclaw@latest
openclaw onboard

When prompted for a provider, select Custom OpenAI-compatible, enter the base URL https://api.cerebras.ai/v1, paste your API key, and pick llama-3.3-70b as your default model.

Manual Configuration

Edit ~/.openclaw/openclaw.json to add Cerebras as a provider:

{
  "models": {
    "mode": "merge",
    "providers": {
      "cerebras": {
        "baseUrl": "https://api.cerebras.ai/v1",
        "apiKey": "YOUR_CEREBRAS_API_KEY",
        "api": "openai-completions",
        "models": [
          {
            "id": "llama-3.3-70b",
            "name": "Llama 3.3 70B (Cerebras)",
            "reasoning": false,
            "input": ["text"],
            "contextWindow": 8192,
            "maxTokens": 4096
          },
          {
            "id": "llama3.1-8b",
            "name": "Llama 3.1 8B (Cerebras)",
            "reasoning": false,
            "input": ["text"],
            "contextWindow": 8192,
            "maxTokens": 4096
          }
        ]
      }
    }
  },
  "agents": {
    "defaults": {
      "model": {
        "primary": "cerebras/llama-3.3-70b"
      },
      "models": {
        "cerebras/llama-3.3-70b": {}
      }
    }
  }
}

Once configured, OpenClaw uses Cerebras for completions. Ask it to write code, review a file, or explain a function — and watch how fast it responds. For quick tasks like generating boilerplate or explaining an error message, the 8B model at 2,100 tokens/second means you get the full answer before you’d even see the first sentence from most other providers.

Cerebras vs Groq: Which Free AI API Is Faster?

Both Cerebras and Groq market themselves as the fastest AI inference available. Here’s an honest comparison:

Feature	Cerebras	Groq
8B model speed	~2,100 tokens/s	~1,500–2,000 tokens/s
70B model speed	~450–500 tokens/s	~300–500 tokens/s
Best free model quality	Llama 3.3 70B	Llama 3.3 70B
Context window	8K tokens	128K tokens
Free RPD	~900	14,400
Free TPM	60,000	6,000–20,000
OpenAI compatible	Yes	Yes
Credit card required	No	No
Models available	4–6	16+
Vision support	No	Limited (preview)

The honest verdict:

• If you need raw speed and your prompt fits in 8K tokens: Cerebras wins (or ties) on throughput
• If you need long context (documents, large codebases): Groq wins by a large margin (128K vs 8K)
• If you need higher daily request volume: Groq wins at 14,400 RPD vs ~900
• If you need more model variety: Groq wins with 16+ models
• If you need higher tokens per minute: Cerebras wins (60K TPM vs 6K–20K)

The practical recommendation: keep both keys. Use Cerebras for short, frequent completions where speed is paramount (tool calls, classifier chains, real-time chat). Use Groq when you need long context or higher daily limits.

Cerebras vs Other Free AI APIs

Feature	Cerebras	Groq	Google Gemini	DeepSeek
Speed	~2,100 tokens/s (8B)	~1,500 tokens/s (8B)	~100 tokens/s	~50–80 tokens/s
Best free model	Llama 3.3 70B	Llama 3.3 70B	Gemini 2.5 Pro	DeepSeek V3
Context window	8K	128K	1M	128K
Multimodal	No	Limited	Yes	No
Best for	Ultra-fast text	Fast + high volume	Complex tasks	Coding, reasoning

Real-World Use Cases Where Cerebras Shines

1. Real-Time AI Chat Applications

When you’re building a customer-facing chat product, the difference between 80 tokens/second and 2,100 tokens/second is the difference between a chat that feels broken and one that feels alive. Cerebras makes even the 8B Llama model feel snappier than GPT-4 Turbo on a good day.

2. Agentic Tool Calls (Many Short Completions)

AI agents often make dozens of small LLM calls — classifying an intent, extracting a field, choosing between branches, summarizing a step. When each call takes 500ms instead of 3 seconds, your agent loop runs 6x faster. Cerebras at 2,100 tokens/second on the 8B model means a 200-token tool-call response completes in under 100ms.

3. Voice AI Pipelines

In a speech-to-text → LLM → text-to-speech pipeline, LLM latency is the bottleneck. Cerebras dramatically cuts time-to-first-token. With streaming, you can pipe the first few tokens to TTS before the full response is complete — achieving near-human response latency in voice applications.

4. Batch Annotation and Labeling

If you’re labeling data for fine-tuning, classifying thousands of records, or running structured extraction over a dataset, Cerebras’ 60,000 TPM free limit combined with its raw throughput means you can process significantly more data per hour than with GPU-based providers.

5. Developer Tools and CI Integrations

Adding AI to your git hooks, code review bots, or documentation generators? Speed matters when it’s blocking a developer’s workflow. A Cerebras-powered code reviewer that responds in 2 seconds doesn’t disrupt the development loop the way a 15-second GPU call would.

Limitations to Know

• Small context window (8K tokens): This is the biggest practical limitation. You can’t feed Cerebras a large codebase, a long document, or an extended conversation history. For long-context work, use Gemini Free (1M tokens) or Groq (128K).
• Text only: No image input, no vision support, no multimodal capabilities as of 2026. Cerebras is purely for text completions.
• Fewer models: Groq offers 16+ models; Cerebras has 4–6. If you need a specific architecture (Gemma, Qwen with vision, Mistral), you may not find it here.
• Lower daily request limit: ~900 RPD is limiting compared to Groq’s 14,400. High-volume production workloads will hit this quickly.
• No fine-tuning: The free tier is inference-only. No custom model training.
• US-based inference: Cerebras’ infrastructure is US-centric. If your users are in Asia/Europe, you may see higher latency on the network round-trip even if the inference itself is blazing fast.

How to Check Your Current Limits and Usage

You can see your current rate limits and usage directly in the Cerebras Cloud Console:

1. Log in at cloud.cerebras.ai
2. Navigate to “Usage” in the left sidebar to see tokens consumed and request counts
3. Navigate to “API Keys” to manage and rotate your keys

You can also query the limits programmatically by checking response headers after each API call. The X-RateLimit-Remaining-Requests and X-RateLimit-Remaining-Tokens headers tell you how much headroom you have left in the current window.

import httpx

headers = {
    "Authorization": "Bearer YOUR_CEREBRAS_API_KEY",
    "Content-Type": "application/json"
}

payload = {
    "model": "llama3.1-8b",
    "messages": [{"role": "user", "content": "Hello!"}]
}

with httpx.Client() as client:
    response = client.post(
        "https://api.cerebras.ai/v1/chat/completions",
        headers=headers,
        json=payload
    )
    print("Remaining requests:", response.headers.get("X-RateLimit-Remaining-Requests"))
    print("Remaining tokens:", response.headers.get("X-RateLimit-Remaining-Tokens"))
    print(response.json()["choices"][0]["message"]["content"])

Combining Cerebras and Groq: A Practical Multi-Provider Strategy

Here’s a pattern used in production: use Cerebras for short, latency-sensitive calls and Groq as the fallback when context is longer or daily limits are exhausted.

from openai import OpenAI
import os

cerebras = OpenAI(
    api_key=os.environ["CEREBRAS_API_KEY"],
    base_url="https://api.cerebras.ai/v1"
)

groq = OpenAI(
    api_key=os.environ["GROQ_API_KEY"],
    base_url="https://api.groq.com/openai/v1"
)

def smart_complete(prompt: str, max_context_tokens: int = 4000) -> str:
    """Use Cerebras for short prompts, Groq for long ones."""
    estimated_tokens = len(prompt.split()) * 1.3

    if estimated_tokens < max_context_tokens:
        try:
            response = cerebras.chat.completions.create(
                model="llama-3.3-70b",
                messages=[{"role": "user", "content": prompt}]
            )
            return response.choices[0].message.content
        except Exception:
            pass  # Fall through to Groq on rate limit or error

    response = groq.chat.completions.create(
        model="llama-3.3-70b-versatile",
        messages=[{"role": "user", "content": prompt}]
    )
    return response.choices[0].message.content

This strategy gives you the best of both worlds: Cerebras' speed for short completions, Groq's long context and higher daily limits for heavier workloads — all completely free.

Related Reads

• Cohere Free API: The Best Free Embedding and Rerank API for RAG in 2026
• Groq vs Cerebras vs Gemini: Which Free AI API Is Actually Fastest in 2026?
• Mistral AI Free API: Call Nemo and Mixtral for Free with Any OpenAI SDK
• GitHub Models: Free GPT-4o and Llama API for Every Developer
• Cloudflare Workers AI: Free Edge AI Inference with 47+ Models

Final Thoughts

Cerebras is the best-kept secret in free AI APIs. While everyone talks about Groq, Cerebras has been quietly delivering the fastest raw inference speeds on the market — powered by hardware that's genuinely unlike anything else in the industry.

The 8K context window is a real limitation, and it means Cerebras isn't the right tool for every job. But for short, latency-critical completions — real-time chat, agentic tool calls, voice pipelines, developer tools — it's hard to beat 2,100 tokens per second with zero dollars spent.

Get your free API key at cloud.cerebras.ai, pair it with OpenClaw, and experience what AI inference feels like when the hardware bottleneck is gone.

And if you want to compare all the best free AI APIs side-by-side, check out our guide: 10 Best Free AI APIs in 2026: The Ultimate Comparison.

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Originally published at toolfreebie.com.