awesome-free-llm-apis
Installation
SKILL.md
Awesome Free LLM APIs
Skill by ara.so — Daily 2026 Skills collection.
A curated list of LLM providers offering permanent free tiers for text inference — no trial credits, no expiry. All endpoints listed are OpenAI SDK-compatible unless noted.
Provider Overview
Provider APIs (trained/fine-tuned by the company)
| Provider | Notable Models | Rate Limits | Region |
|---|---|---|---|
| Cohere | Command A, Command R+, Aya Expanse 32B | 20 RPM, 1K req/mo | 🇺🇸 |
| Google Gemini | Gemini 2.5 Pro, Flash, Flash-Lite | 5–15 RPM, 100–1K RPD | 🇺🇸 (not EU/UK/CH) |
| Mistral AI | Mistral Large 3, Small 3.1, Ministral 8B | 1 req/s, 1B tok/mo | 🇪🇺 |
| Zhipu AI | GLM-4.7-Flash, GLM-4.5-Flash, GLM-4.6V-Flash | Undocumented | 🇨🇳 |
Inference Providers (host open-weight models)
| Provider | Notable Models | Rate Limits | Region |
|---|---|---|---|
| Cerebras | Llama 3.3 70B, Qwen3 235B, GPT-OSS-120B | 30 RPM, 14,400 RPD | 🇺🇸 |
| Cloudflare Workers AI | Llama 3.3 70B, Qwen QwQ 32B | 10K neurons/day | 🇺🇸 |
| GitHub Models | GPT-4o, Llama 3.3 70B, DeepSeek-R1 | 10–15 RPM, 50–150 RPD | 🇺🇸 |
| Groq | Llama 3.3 70B, Llama 4 Scout, Kimi K2 | 30 RPM, 1K RPD | 🇺🇸 |
| Hugging Face | Llama 3.3 70B, Qwen2.5 72B, Mistral 7B | $0.10/mo free credits | 🇺🇸 |
| Kluster AI | DeepSeek-R1, Llama 4 Maverick, Qwen3-235B | Undocumented | 🇺🇸 |
| LLM7.io | DeepSeek R1, Flash-Lite, Qwen2.5 Coder | 30 RPM (120 with token) | 🇬🇧 |
| NVIDIA NIM | Llama 3.3 70B, Mistral Large, Qwen3 235B | 40 RPM | 🇺🇸 |
| Ollama Cloud | DeepSeek-V3.2, Qwen3.5, Kimi-K2.5 | 1 concurrent, light usage | 🇺🇸 |
| OpenRouter | DeepSeek R1, Llama 3.3 70B, GPT-OSS-120B | 20 RPM, 50 RPD (1K with $10+) | 🇺🇸 |
Getting API Keys
Each provider has its own key management page:
# Store keys as environment variables — never hardcode them
export GROQ_API_KEY="your_groq_key"
export GEMINI_API_KEY="your_gemini_key"
export OPENROUTER_API_KEY="your_openrouter_key"
export MISTRAL_API_KEY="your_mistral_key"
export COHERE_API_KEY="your_cohere_key"
export CEREBRAS_API_KEY="your_cerebras_key"
export GITHUB_TOKEN="your_github_pat"
export HF_TOKEN="your_huggingface_token"
export NVIDIA_API_KEY="your_nvidia_key"
export CLOUDFLARE_API_TOKEN="your_cf_token"
export CLOUDFLARE_ACCOUNT_ID="your_cf_account_id"
OpenAI SDK Integration
All providers (except Ollama Cloud) are OpenAI SDK-compatible — just swap the base_url and api_key.
Python
from openai import OpenAI
# ── Groq ──────────────────────────────────────────────────────────────────────
client = OpenAI(
base_url="https://api.groq.com/openai/v1",
api_key=os.environ["GROQ_API_KEY"],
)
response = client.chat.completions.create(
model="llama-3.3-70b-versatile",
messages=[{"role": "user", "content": "Hello!"}],
)
print(response.choices[0].message.content)
# ── Google Gemini ─────────────────────────────────────────────────────────────
client = OpenAI(
base_url="https://generativelanguage.googleapis.com/v1beta/openai/",
api_key=os.environ["GEMINI_API_KEY"],
)
response = client.chat.completions.create(
model="gemini-2.0-flash",
messages=[{"role": "user", "content": "Explain quantum entanglement."}],
)
# ── Mistral AI ────────────────────────────────────────────────────────────────
client = OpenAI(
base_url="https://api.mistral.ai/v1",
api_key=os.environ["MISTRAL_API_KEY"],
)
response = client.chat.completions.create(
model="mistral-small-latest",
messages=[{"role": "user", "content": "Write a haiku about code."}],
)
# ── OpenRouter ────────────────────────────────────────────────────────────────
client = OpenAI(
base_url="https://openrouter.ai/api/v1",
api_key=os.environ["OPENROUTER_API_KEY"],
)
response = client.chat.completions.create(
model="deepseek/deepseek-r1", # free model on OpenRouter
messages=[{"role": "user", "content": "What is 2+2?"}],
extra_headers={
"HTTP-Referer": "https://yourapp.com", # optional but recommended
"X-Title": "My App",
},
)
# ── Cerebras ──────────────────────────────────────────────────────────────────
client = OpenAI(
base_url="https://api.cerebras.ai/v1",
api_key=os.environ["CEREBRAS_API_KEY"],
)
response = client.chat.completions.create(
model="llama-3.3-70b",
messages=[{"role": "user", "content": "Tell me a joke."}],
)
# ── NVIDIA NIM ────────────────────────────────────────────────────────────────
client = OpenAI(
base_url="https://integrate.api.nvidia.com/v1",
api_key=os.environ["NVIDIA_API_KEY"],
)
response = client.chat.completions.create(
model="meta/llama-3.3-70b-instruct",
messages=[{"role": "user", "content": "Summarize this text."}],
)
# ── GitHub Models ─────────────────────────────────────────────────────────────
client = OpenAI(
base_url="https://models.inference.ai.azure.com",
api_key=os.environ["GITHUB_TOKEN"],
)
response = client.chat.completions.create(
model="gpt-4o",
messages=[{"role": "user", "content": "Draft an email."}],
)
# ── Cohere (OpenAI-compatible endpoint) ───────────────────────────────────────
client = OpenAI(
base_url="https://api.cohere.com/compatibility/v1",
api_key=os.environ["COHERE_API_KEY"],
)
response = client.chat.completions.create(
model="command-a-03-2025",
messages=[{"role": "user", "content": "Translate to French: Hello world"}],
)
JavaScript / TypeScript
import OpenAI from "openai";
// ── Groq ──────────────────────────────────────────────────────────────────────
const groq = new OpenAI({
baseURL: "https://api.groq.com/openai/v1",
apiKey: process.env.GROQ_API_KEY,
});
const completion = await groq.chat.completions.create({
model: "llama-3.3-70b-versatile",
messages: [{ role: "user", content: "Hello!" }],
});
console.log(completion.choices[0].message.content);
// ── OpenRouter with free model router ────────────────────────────────────────
const openrouter = new OpenAI({
baseURL: "https://openrouter.ai/api/v1",
apiKey: process.env.OPENROUTER_API_KEY,
defaultHeaders: {
"HTTP-Referer": "https://yourapp.com",
"X-Title": "My App",
},
});
// Use the free models router — automatically picks an available free model
const freeCompletion = await openrouter.chat.completions.create({
model: "openrouter/free",
messages: [{ role: "user", content: "What is the capital of France?" }],
});
// ── Mistral ───────────────────────────────────────────────────────────────────
const mistral = new OpenAI({
baseURL: "https://api.mistral.ai/v1",
apiKey: process.env.MISTRAL_API_KEY,
});
const mistralCompletion = await mistral.chat.completions.create({
model: "mistral-small-latest",
messages: [{ role: "user", content: "Explain async/await in JavaScript." }],
});
Cloudflare Workers AI
Cloudflare uses a slightly different auth pattern:
import requests, os
ACCOUNT_ID = os.environ["CLOUDFLARE_ACCOUNT_ID"]
API_TOKEN = os.environ["CLOUDFLARE_API_TOKEN"]
response = requests.post(
f"https://api.cloudflare.com/client/v4/accounts/{ACCOUNT_ID}/ai/run/"
"@cf/meta/llama-3.3-70b-instruct-fp8-fast",
headers={"Authorization": f"Bearer {API_TOKEN}"},
json={"messages": [{"role": "user", "content": "What is Cloudflare Workers?"}]},
)
result = response.json()
print(result["result"]["response"])
// Cloudflare Workers runtime (inside a Worker)
export default {
async fetch(request: Request, env: Env): Promise<Response> {
const ai = new Ai(env.AI);
const response = await ai.run("@cf/meta/llama-3.3-70b-instruct-fp8-fast", {
messages: [{ role: "user", content: "Hello from Workers AI!" }],
});
return Response.json(response);
},
};
Ollama Cloud (Non-OpenAI API)
Ollama Cloud uses the Ollama API format, not the OpenAI format:
import requests, os
response = requests.post(
"https://ollama.com/api/chat",
headers={"Authorization": f"Bearer {os.environ['OLLAMA_API_KEY']}"},
json={
"model": "deepseek-v3.2",
"messages": [{"role": "user", "content": "What is 2 + 2?"}],
"stream": False,
},
)
print(response.json()["message"]["content"])
# Using the ollama Python client
import ollama, os
client = ollama.Client(
host="https://ollama.com",
headers={"Authorization": f"Bearer {os.environ['OLLAMA_API_KEY']}"},
)
response = client.chat(
model="qwen3.5",
messages=[{"role": "user", "content": "Write a poem about the sea."}],
)
print(response["message"]["content"])
Hugging Face Inference API
from openai import OpenAI
import os
client = OpenAI(
base_url="https://router.huggingface.co/novita/v3/openai",
api_key=os.environ["HF_TOKEN"],
)
response = client.chat.completions.create(
model="meta-llama/llama-3.3-70b-instruct",
messages=[{"role": "user", "content": "Summarize the theory of relativity."}],
max_tokens=512,
)
print(response.choices[0].message.content)
Streaming Responses
from openai import OpenAI
import os
client = OpenAI(
base_url="https://api.groq.com/openai/v1",
api_key=os.environ["GROQ_API_KEY"],
)
with client.chat.completions.stream(
model="llama-3.3-70b-versatile",
messages=[{"role": "user", "content": "Write a short story about a robot."}],
) as stream:
for text in stream.text_stream:
print(text, end="", flush=True)
const stream = await groq.chat.completions.create({
model: "llama-3.3-70b-versatile",
messages: [{ role: "user", content: "Write a haiku." }],
stream: true,
});
for await (const chunk of stream) {
process.stdout.write(chunk.choices[0]?.delta?.content ?? "");
}
Provider Fallback Pattern
Cycle through providers when rate limits are hit:
from openai import OpenAI, RateLimitError
import os
PROVIDERS = [
{
"name": "Groq",
"base_url": "https://api.groq.com/openai/v1",
"api_key": os.environ.get("GROQ_API_KEY"),
"model": "llama-3.3-70b-versatile",
},
{
"name": "Cerebras",
"base_url": "https://api.cerebras.ai/v1",
"api_key": os.environ.get("CEREBRAS_API_KEY"),
"model": "llama-3.3-70b",
},
{
"name": "Mistral",
"base_url": "https://api.mistral.ai/v1",
"api_key": os.environ.get("MISTRAL_API_KEY"),
"model": "mistral-small-latest",
},
{
"name": "OpenRouter",
"base_url": "https://openrouter.ai/api/v1",
"api_key": os.environ.get("OPENROUTER_API_KEY"),
"model": "openrouter/free",
},
]
def chat_with_fallback(messages: list[dict], **kwargs) -> str:
for provider in PROVIDERS:
if not provider["api_key"]:
continue
try:
client = OpenAI(
base_url=provider["base_url"],
api_key=provider["api_key"],
)
response = client.chat.completions.create(
model=provider["model"],
messages=messages,
**kwargs,
)
return response.choices[0].message.content
except RateLimitError:
print(f"Rate limited on {provider['name']}, trying next...")
continue
except Exception as e:
print(f"Error on {provider['name']}: {e}, trying next...")
continue
raise RuntimeError("All providers exhausted.")
# Usage
answer = chat_with_fallback(
messages=[{"role": "user", "content": "What is the speed of light?"}]
)
print(answer)
OpenRouter Free Models Router
OpenRouter provides a special router that automatically selects available free models:
from openai import OpenAI
import os
client = OpenAI(
base_url="https://openrouter.ai/api/v1",
api_key=os.environ["OPENROUTER_API_KEY"],
)
# Use the free router — picks from 29+ free models automatically
response = client.chat.completions.create(
model="openrouter/free",
messages=[{"role": "user", "content": "Explain recursion."}],
)
# Or use model fallbacks for priority ordering
response = client.chat.completions.create(
model="deepseek/deepseek-r1",
messages=[{"role": "user", "content": "Explain recursion."}],
extra_body={
"route": "fallback",
"models": [
"deepseek/deepseek-r1",
"meta-llama/llama-3.3-70b-instruct:free",
"openrouter/free",
],
},
)
LangChain Integration
from langchain_openai import ChatOpenAI
from langchain_core.messages import HumanMessage
import os
# Works with any OpenAI-compatible provider
llm = ChatOpenAI(
model="llama-3.3-70b-versatile",
openai_api_base="https://api.groq.com/openai/v1",
openai_api_key=os.environ["GROQ_API_KEY"],
temperature=0.7,
)
response = llm.invoke([HumanMessage(content="What are the SOLID principles?")])
print(response.content)
# Gemini via LangChain
gemini = ChatOpenAI(
model="gemini-2.0-flash",
openai_api_base="https://generativelanguage.googleapis.com/v1beta/openai/",
openai_api_key=os.environ["GEMINI_API_KEY"],
)
Rate Limit Reference
| Provider | RPM | RPD | Notes |
|---|---|---|---|
| Groq | 30 | 1,000 | 14,400 RPD for Llama 3.1 8B only |
| Cerebras | 30 | 14,400 | — |
| Gemini Flash | 15 | 1,500 | Not in EU/UK/CH |
| Gemini 2.5 Pro | 5 | 25 | Not in EU/UK/CH |
| GitHub Models | 10–15 | 50–150 | Varies by model tier |
| OpenRouter (free) | 20 | 50 | 1K RPD after $10+ purchase |
| Mistral | 1 req/s | — | 1B tokens/month cap |
| NVIDIA NIM | 40 | — | — |
| Cloudflare Workers AI | — | — | 10K neurons/day |
| Cohere | 20 | — | 1K requests/month |
Common Troubleshooting
AuthenticationError
- Double-check the env var is set:
echo $GROQ_API_KEY - Ensure the key is for the correct provider
- Some providers (GitHub Models) require a classic PAT, not a fine-grained token
RateLimitError
- Implement exponential backoff or use the fallback pattern above
- Switch to a provider with higher limits (Cerebras: 14,400 RPD)
- For Groq, use
llama-3.1-8b-instantfor the 14,400 RPD limit
Model not found
- Check the exact model ID on the provider's docs/dashboard
- OpenRouter free models have
:freesuffix:meta-llama/llama-3.3-70b-instruct:free - Cloudflare models use
@cf/prefix:@cf/meta/llama-3.3-70b-instruct-fp8-fast
Gemini free tier unavailable
- The free tier is not available in EU, UK, or Switzerland
- Use a VPN or switch to a different provider like Groq or Mistral
Ollama Cloud not working with OpenAI SDK
- Ollama Cloud uses its own API format — use the
ollamaPython package or raw HTTP
OpenRouter 50 RPD limit
- Make a one-time $10 credit purchase to unlock 1,000 RPD for free models permanently
- Alternatively, use
openrouter/freerouter to distribute across all free models
Choosing the Right Provider
Need highest RPD? → Cerebras (14,400 RPD)
Need smartest free model? → Gemini 2.5 Pro (if not in EU/UK/CH)
Need EU-hosted? → Mistral AI (France)
Need most model variety? → OpenRouter (29+ free models) or Cloudflare (48+ models)
Need fastest inference? → Groq (purpose-built inference chips)
Need reasoning model? → DeepSeek-R1 on Groq/OpenRouter/Kluster AI
Need vision? → Gemini Flash, Llama 4 Scout (Groq), GLM-4.6V-Flash (Zhipu)
No rate limit concern? → Cloudflare (10K neurons/day, compute-based)
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