Free LLM API: Run 120+ AI Models for $0 in 2026

If you've seen the claims that a free LLM API lets you "stop paying for AI forever," you were right to be sceptical — and right to be curious. The hype is overblown, but underneath it there's something genuinely useful: you really can get a single API key that unlocks more than 120 AI models, with no credit card and no trial timer. The trick is knowing what's real, what the catch is, and how to actually wire it into the tools you already use.

This guide cuts through the noise. You'll see exactly what a free LLM API gives you, which models you get (and which you don't), and three ways to set it up — the easy path in OpenCode, the advanced path of running it inside Claude Code, and the reusable command-and-script approach. We'll be honest about the limits too, because a free tier you don't understand will waste your time.

I run frontier closed models as my daily driver, but I keep these free tiers set up for testing, for teaching, and to make sure my students are never locked out of learning by an API bill. Let's get you set up.

What Is a Free LLM API (and What's the Catch)?

A free LLM API is an endpoint you can send text to and get an AI model's response back — without paying per token, and usually without a credit card. You get an API key, point your code or your editor at a URL, choose a model, and you're running. For learning and prototyping, it removes the single biggest barrier to entry: cost.

But "free" always has a shape, and it's worth naming it up front so nothing surprises you later:

• Rate limits. Free tiers cap how many requests you can send per minute and per day. Fine for a human chatting or coding; a problem for heavy automation.
• Open-weight models only. You'll get excellent open models (DeepSeek, Kimi, Qwen, Llama). You will not get GPT-5, Claude, or Gemini Pro for free — those are closed, and only their owners can serve them.
• Dev and test, not production. Most free tiers are explicitly for prototyping. The moment you have real users, you're expected to pay.
• Hosted ≠ private. Your prompts still leave your machine and hit someone's servers. Free hosting is not the same as privacy.

Keep those four in mind and a free LLM API is one of the best things going for anyone learning to build with AI. Ignore them and you'll hit a wall and blame the wrong thing. Now let's look at the one I'd start with.

NVIDIA NIM: 120+ Free AI Models Through One Key

NVIDIA NIM (NVIDIA Inference Microservices) is NVIDIA's hosted catalogue of AI models, served through an OpenAI-compatible endpoint at https://integrate.api.nvidia.com/v1. You sign up at build.nvidia.com with an email — no card — and get a key that starts with nvapi-. That one key reaches every model in the catalogue.

Why does NVIDIA give this away? Because it's a shop window. NVIDIA wants developers building on its platform, and the free NVIDIA NIM API is the on-ramp to its paid AI Enterprise product. That's a fair trade — and it means the free tier is real, not a bait-and-switch, as long as you stay inside its dev-and-test purpose.

The reason it stands out among free options is the combination: the largest open-weight catalogue of the free providers, an OpenAI-compatible endpoint that drops into almost any tool, no credit card, and Western (NVIDIA-operated) hosting rather than calling a model maker's own servers directly.

Here's the catalogue itself at build.nvidia.com — "free inference with leading models" is right there on the front page:

What You Get — the Open-Weight Models

This is where a free LLM API earns its keep. A free AI API is only as good as the models behind it, and NVIDIA NIM hosts open-weight models — models whose weights are publicly released — which is exactly why they can be offered free. These aren't toys; several are genuinely competitive with closed frontier models for everyday work.

The headline models you'll recognise:

Two honest points worth making to anyone in cyber security, which is where most of my students sit. First, several of the strongest free models come from Chinese labs. The weights themselves carry whatever bias and content guardrails they were trained with — that travels with the model regardless of who hosts it. Second, and reassuringly: because NVIDIA hosts these, your prompts go to NVIDIA's infrastructure, not to the original lab's servers. The model maker is out of the loop. That's a real plus over calling a Chinese provider's API directly — though it still isn't the same as private (more on that below).

What you won't find here: GPT, Claude, or Gemini Pro. Those are closed-weight, so no free open-model service can host them. (Google does give away its smaller Gemini Flash models free through its own API — more on that later — but the frontier closed models stay paid.) If a video tells you it gives "all the top models for free," that's the tell it's overselling. For a deeper look at choosing among open models, see our best local LLM methodology guide.

How to Get Your Free NVIDIA NIM API Key

This takes about two minutes and needs nothing but an email address. No free LLM API key hunting, no card.

1. Go to build.nvidia.com and sign up (email, then verify it).
2. Open any model in the catalogue.
3. Click Get API Key. You'll get a key beginning with nvapi-.
4. Copy it somewhere safe — treat it like a password.

Every model page looks like this — a ready-to-copy code sample, a Generate API Key button, and the model's availability and context window on the right. This is the DeepSeek V4 Pro page:

That single key works for every model. To switch models later, you change one string — the model name — and nothing else. Store the key as an environment variable so it's never hard-coded into your projects:

# Save your key as an environment variable (macOS / Linux)
$ echo 'export NVIDIA_NIM_API_KEY=nvapi-your-key-here' >> ~/.zshrc
$ source ~/.zshrc

Quick sanity check that the key works — this lists the models your account can reach:

$ curl -s https://integrate.api.nvidia.com/v1/models \
    -H "Authorization: Bearer $NVIDIA_NIM_API_KEY" | head

Setup 1: OpenCode (the Easy Path)

There are three ways to plug the free NVIDIA NIM API into your workflow, and they're not equal. Here's the map before we dig in:

OpenCode is a free, open-source AI coding agent that runs in your terminal. It speaks the OpenAI format natively, so the free NVIDIA NIM API drops straight in — this is the path I'd recommend you try first.

Create or edit ~/.config/opencode/opencode.json and add NVIDIA as a provider:

{
  "$schema": "https://opencode.ai/config.json",
  "provider": {
    "nvidia": {
      "npm": "@ai-sdk/openai-compatible",
      "name": "NVIDIA NIM",
      "options": {
        "baseURL": "https://integrate.api.nvidia.com/v1",
        "apiKey": "{env:NVIDIA_NIM_API_KEY}"
      },
      "models": {
        "deepseek-ai/deepseek-v4-pro": { "name": "DeepSeek V4 Pro" },
        "moonshotai/kimi-k2.6": { "name": "Kimi K2.6" },
        "qwen/qwen3.5-397b-a17b": { "name": "Qwen 3.5" }
      }
    }
  }
}

Then open OpenCode, run /models, pick a NVIDIA NIM model, and start working:

$ opencode
# inside OpenCode, type:
> /models
# choose a NVIDIA NIM model, then chat or code as normal

That's the whole setup. Because OpenCode reads the live model list from the endpoint, you actually get the full catalogue available to pick from, not just the three listed above. One key, every model, in your editor.

Here's mine running DeepSeek V4 Pro through NVIDIA NIM — I asked the model to confirm its own identity, and notice the cost: $0.01 for the session, on the free tier:

Setup 2: Claude Code as a Main Driver (Read the Warning First)

Still want to try it? The wrinkle is that Claude Code only speaks Anthropic's API format, while NVIDIA's endpoint speaks OpenAI's. You need a small translator in between — LiteLLM, a free proxy that converts between the two and routes to NVIDIA.

First, a LiteLLM config that maps the model names Claude Code expects onto NVIDIA models:

model_list:
  - model_name: claude-sonnet-4-5
    litellm_params:
      model: nvidia_nim/deepseek-ai/deepseek-v4-pro
      api_key: os.environ/NVIDIA_NIM_API_KEY
      api_base: https://integrate.api.nvidia.com/v1
  - model_name: claude-3-5-haiku
    litellm_params:
      model: nvidia_nim/moonshotai/kimi-k2.6
      api_key: os.environ/NVIDIA_NIM_API_KEY

Install LiteLLM and start the proxy:

$ pip install 'litellm[proxy]'
$ litellm --config config.yaml
# proxy now serving on http://localhost:4000

Then, in a new terminal, point Claude Code at the proxy and launch it:

$ export ANTHROPIC_BASE_URL=http://localhost:4000
$ export ANTHROPIC_AUTH_TOKEN=anything
$ claude

Claude Code now routes its requests through LiteLLM to NVIDIA's open models. It works — but remember the warning. The moment it gets busy, the free rate limit will bite. Treat this as a way to see open models inside Claude Code, not a way to run on them all day.

Setup 3: Call It From a Command, Script or Skill

Beyond an interactive editor, you'll often want to call a model from a script — to batch-process text, build a small tool, or wire it into an automation. Because the endpoint is OpenAI-compatible, any OpenAI client library works; you just change the base URL and key.

Here's the minimal pattern in TypeScript using the official OpenAI SDK:

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: process.env.NVIDIA_NIM_API_KEY,
  baseURL: "https://integrate.api.nvidia.com/v1",
});

const res = await client.chat.completions.create({
  model: "deepseek-ai/deepseek-v4-pro",
  messages: [{ role: "user", content: "Explain a free LLM API in one line." }],
});

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

The same idea works in Python — pip install openai, set base_url and api_key, done. Once you have a small wrapper like this, you can turn it into a reusable command-line tool, drop it into a coding agent as a skill, or schedule it. The model choice is just a string, so a single script can reach all 120+ models. That's the real power of an OpenAI-compatible free LLM API: write once, swap models freely.

And because it's the OpenAI format, the exact same code works for other free providers — you change two lines. Here's Groq (the fastest free option), which uses the base URL https://api.groq.com/openai/v1:

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: process.env.GROQ_API_KEY,            // get one free at console.groq.com
  baseURL: "https://api.groq.com/openai/v1",
});

const res = await client.chat.completions.create({
  model: "llama-3.3-70b-versatile",
  messages: [{ role: "user", content: "Say hello from Groq." }],
});

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

And here's OpenRouter, which routes to many providers' free models — note the :free suffix on the model name, that's how you stay on the no-cost tier:

import OpenAI from "openai";

const client = new OpenAI({
  apiKey: process.env.OPENROUTER_API_KEY,      // get one free at openrouter.ai/keys
  baseURL: "https://openrouter.ai/api/v1",
});

const res = await client.chat.completions.create({
  model: "deepseek/deepseek-chat:free",         // :free keeps it on the free tier
  messages: [{ role: "user", content: "Say hello from OpenRouter." }],
});

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

That's the whole trick to an OpenAI-compatible free LLM API: same code, swap the base URL and key, and you can fail over between NVIDIA NIM, Groq, and OpenRouter to dodge any one provider's rate limit. Build the wrapper once and you've got three free backends.

The Honest Limits: Rate Caps, Privacy, and When to Pay

Here's the part the hype videos skip — and the part that actually decides whether this fits your project.

Rate limits. The free NVIDIA NIM API allows roughly 40 requests per minute. NVIDIA doesn't publish an exact free-tier figure any more — it shows your current limit in the top-right of build.nvidia.com, and it varies by model — so treat 40 as the ballpark, not a guarantee. Either way, it's comfortable for chatting and steady coding, but heavy automation or agent swarms will hit the ceiling and throw 429 errors. If you see those, you're not broken — you've hit the limit. Wait, or slow your request rate.

Dev and test only. Every free tier LLM API is built for prototyping, learning, and evaluation — not production traffic with real users. Moving to production means an NVIDIA AI Enterprise licence, or self-hosting. That's the business model, and it's a reasonable one.

Hosted is not private. Your prompts go to NVIDIA's (Western) cloud — good news if your concern was data going to a Chinese lab, because it doesn't. But it still leaves your machine, and free-tier data may be logged per NVIDIA's terms. If you need genuine privacy — client data, anything sensitive — the answer is not "NVIDIA instead of China," it's running models locally with Ollama, where nothing leaves your computer at all. See our Claude Code with Ollama guide for that.

Open-weight only. Worth repeating: no GPT, Claude, or Gemini Pro (Google's free Flash models are the one closed-weight exception). For the frontier closed models, you pay the owner.

Other Free LLM APIs Worth Knowing

NVIDIA NIM is my pick for the widest model choice, but it isn't the only free LLM API worth a place in your toolkit. The smart move is to set up two or three of these free LLM API providers and fail over between them. Here's how the main free tiers compare as of 2026:

A few quick takes: Groq is the speed king — if you want fast responses on Llama or Qwen, it's superb, just with a smaller catalogue. OpenRouter gives you one key across many providers' free models, but the 50-requests-a-day ceiling is restrictive (and failed requests still count). OpenRouter doesn't publish these free limits as a fixed promise, so treat them as current-behaviour rather than a guarantee — they can shift. Google AI Studio hands you Gemini Flash free, but uses your prompts for training unless you're in the EU/UK/EEA — a real consideration for sensitive work. Ollama isn't a hosted API at all; it runs models on your own machine, which is the only genuinely private option here.

Which Free LLM API Should You Use?

Short version, by what you're trying to do:

• Widest choice of models to experiment with → NVIDIA NIM. Start here.
• Fastest responses → Groq.
• One key across many providers → OpenRouter (mind the 50/day).
• Gemini specifically → Google AI Studio (mind the data-training terms).
• Genuine privacy / offline → Ollama, locally.
• A coding agent on free models → OpenCode + NVIDIA NIM.

My honest recommendation for someone learning: set up NVIDIA NIM in OpenCode today, add Groq as a fast backup, and keep Ollama in your back pocket for anything private. That covers breadth, speed, and privacy — for nothing. When a project gets real and you keep hitting the rate limits, that's your signal to graduate to a paid tier. The free LLM API isn't where you ship; it's where you learn. And learning for free is exactly the point.

❓ Free LLM API FAQ

Want to go further? Our best local LLM guide shows you how to choose among open models, and the Claude Code with Ollama guide covers running them privately on your own machine.

Last verified: June 2026. Free-tier rate limits and model catalogues change often — check the provider's dashboard for current numbers before you commit.

About the Author

Nathan House, Founder & CEO of StationX

Nathan House has 30 years of hands-on cybersecurity experience and is Cambridge-educated, holding CISSP, CISA, CISM, OSCP, CEH, and SABSA. He founded StationX in 1999 — one of the UK’s first cybersecurity companies — and has secured £71 billion in UK mobile banking transactions and the London 2012 Olympics, advising clients including Microsoft, Cisco, BP, Vodafone, and VISA. He authored the world’s most popular cybersecurity course — a #1 Udemy bestseller taken by over 500,000 students — and was named Cyber Security Educator of the Year 2020, AI Security Educator of the Year, and a UK Top 25 Security Influencer 2025. A DEF CON speaker and featured expert on CNN, Fox News, NBC, and the BBC, Nathan leads StationX’s training of more than half a million students worldwide.