awesome-free-llm-apis

仓库: aradotso/trending-skills
安装量: 394
排名: #5561

安装

npx skills add https://github.com/aradotso/trending-skills --skill awesome-free-llm-apis

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-instant for the 14,400 RPD limit Model not found Check the exact model ID on the provider's docs/dashboard OpenRouter free models have :free suffix: 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 ollama Python 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/free router 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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