5 Free LLM API Providers You Can Use in 2026

You do not need to pay for LLM inference just to start building AI applications. Several providers now offer genuine free API access that is more than enough for learning, prototypes, side projects, hackathons, and experimentation.

What I find most impressive is the quality and size of the models you can now access for free. Depending on the provider, you can experiment with models such as NVIDIA Nemotron 3 Ultra, Laguna S 2.1, Mistral Medium, GPT-OSS-120B, and the latest Gemini models without having to host these huge models yourself or pay for every API call.

In this article, we will explore five free LLM API providers, what models they give you access to, their free limits, and where I think each one is most useful.

1. GroqCloud

GroqCloud is probably the first free LLM API I would recommend if inference speed matters.

Its free plan gives you access to some surprisingly large models, including Groq Compound, GPT-OSS-20B, GPT-OSS-120B, and Qwen3.6-27B. The limits are different for each model rather than having one allowance shared across everything.

Example usage:

from groq import Groq

client = Groq()
completion = client.chat.completions.create(
    model="openai/gpt-oss-120b",
    messages=[
      {
        "role": "user",
        "content": "Explain PEP 8 in one sentence."
      }
    ],
    temperature=1,
    max_completion_tokens=2048,
    top_p=1,
    reasoning_effort="medium",
    stream=True,
    stop=None
)

for chunk in completion:
    print(chunk.choices[0].delta.content or "", end="")

What I like about Groq is that the free tier is generous enough to actually build something instead of making only a handful of test calls. The extremely fast inference also makes it useful for chatbots and agentic applications where you want quick responses.

2. OpenRouter

OpenRouter is the option I use when I want to experiment with lots of different models without creating an account and API key for every provider.

It currently lists 25+ free models, and many free endpoints use the :free suffix. You can also use openrouter/free, which automatically routes your request to an available free model that supports the capabilities you need, such as tool calling or structured outputs.

Free accounts currently receive 50 requests per day and 20 requests per minute. If you have purchased at least \$10 in credits, the free-model daily limit increases to 1,000 requests, while the models themselves remain free.

Example usage:

import os
from openai import OpenAI

client = OpenAI(
    base_url="https://openrouter.ai/api/v1",
    api_key=os.environ["OPENROUTER_API_KEY"],
)

response = client.chat.completions.create(
    model="nvidia/nemotron-3.5-lightning:free",
    messages=[
        {
            "role": "user",
            "content": "How many r's are in the word 'strawberry'?"
        }
    ],
    extra_body={
        "reasoning": {
            "enabled": True
        }
    },
)

message = response.choices[0].message

print(message.reasoning)
print(message.content)

For me, the biggest advantage is model variety. Instead of changing your entire application whenever you want to test another model, you can keep using essentially the same OpenAI-compatible API.

The free models rotate over time, so I would not build a production application around one specific free endpoint. For learning and comparing models, however, it is difficult to beat.

3. Cloudflare Workers AI

Cloudflare Workers AI is a little different because it combines hosted AI models with Cloudflare's broader serverless developer platform.

Every account currently receives 10,000 Neurons of AI inference per day for free, and the allowance resets daily. What I like about this approach is that a model does not necessarily have to be listed as "\$0" for you to use it for free. Many models have normal per-token pricing, but as long as they are available on the Workers Free plan, their usage can be covered by your daily 10,000-Neuron allocation.

And you are not limited to old or small models. Cloudflare added Qwen3.8-27B on August 17, 2026, and it is a 27-billion-parameter vision-language model with reasoning, function calling, vision, and a 262K context window.

Example usage:

import os
import requests

ACCOUNT_ID = os.environ["CLOUDFLARE_ACCOUNT_ID"]
API_TOKEN = os.environ["CLOUDFLARE_AUTH_TOKEN"]

response = requests.post(
    f"https://api.cloudflare.com/client/v4/accounts/{ACCOUNT_ID}/ai/run/"
    "@cf/qwen/qwen3.8-27b",
    headers={
        "Authorization": f"Bearer {API_TOKEN}"
    },
    json={
        "messages": [
            {
                "role": "user",
                "content": (
                    "Write a Python function that reverses "
                    "a string without using slicing."
                ),
            }
        ],
        "max_tokens": 1024,
    },
)

print(response.json()["result"]["choices"][0]["message"]["content"])

This is what makes Workers AI interesting to me. You can experiment with newly released and fairly large models without immediately paying for every token, as long as your usage stays within the daily free allocation.

I would recommend Cloudflare if you want to go beyond simply calling an LLM. You can combine Workers AI with Workers, AI Gateway, Vectorize, and other Cloudflare services to build a complete serverless AI application.

One limitation is that not every model is available to free accounts. Cloudflare currently requires the Workers Paid plan for a few resource-intensive models, including Kimi K2.6, Kimi K2.7 Code, and GLM-5.2. But many other capable models remain available under the free allocation.

4. Mistral

Mistral has one of the more interesting free offerings because its Free plan currently includes \$10 per month in API credits, with no credit card required to get started with Mistral Studio.

The important part is that this is not limited to a single free model. Instead, the \$10 allowance can be used toward API usage for the Mistral models available to your account in Studio, including newer models that normally have per-token pricing.

Mistral's Free plan also gives you limited access to Vibe Code, its agentic coding environment. Vibe can work from the terminal, IDE, or web and can inspect a codebase, make changes, run commands, and work through development tasks.

What I particularly like is that the monthly usage is shared across Studio, the API, and Vibe Code. This means the same free allowance can be used to experiment with models directly through the API or to experience an agentic coding workflow.

Example usage:

from mistralai import Mistral

client = Mistral(
    api_key="YOUR_API_KEY"
)

response = client.chat.complete(
    model="mistral-medium-latest",
    messages=[
        {
            "role": "user",
            "content": "Explain PEP 8 in one sentence."
        }
    ],
)

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

Mistral currently recommends Mistral Medium for general tasks and coding, while its broader API also includes models for text generation, document intelligence, audio, and other workloads.

There is one important limitation: I would not describe the \$10 allowance as guaranteed access to every single Mistral model or service. Your Free organization has its own model availability and rate limits, and some specialized APIs are priced differently. You can see exactly what is available to you from the Studio model picker and your Usage and Limits page.

For me, this makes Mistral much more useful than a traditional free API tier. You are effectively getting a small recurring monthly AI budget that you can spend experimenting with current Mistral models, building applications in Studio, or trying agentic coding with Vibe Code.

5. Google Gemini API

Google Gemini API has one of the strongest free API offerings, especially now that even its newer models are available through the Free Tier.

For example, Gemini 3.7 Flash is currently free for both input and output tokens on the Free Tier. It is also Google's most capable Flash model for coding, agentic workflows, and multimodal reasoning, with a 1 million-token context window and support for up to 64K output tokens.

Google now recommends its newer Interactions API for building with Gemini models and agents.

Example usage:

import os
from google import genai

client = genai.Client(
    api_key=os.environ["GOOGLE_API_KEY"]
)

interaction = client.interactions.create(
    model="gemini-3.7-flash",
    input="What is the latest stable version of Python?",
    generation_config={
        "max_output_tokens": 65536,
        "top_p": 0.95,
        "thinking_level": "medium",
    },
)

print(interaction.output_text)

What I like about Google's API is that it goes beyond text generation. Alongside free access to models such as Gemini 3.7 Flash, you can work with image, audio, and video understanding, as well as free text and multimodal embedding models. Google also provides image generation and text-to-speech models through the same API ecosystem, although these currently require paid usage.

For me, this makes the Gemini API a great platform for learning LLM development, multimodal AI, embeddings, agents, and generative AI applications without needing several different providers.

Final Thoughts

I personally use these free APIs all the time. I have used Mistral for LLM applications and even with my Spokenly dictation workflow, while Groq has been particularly useful for fast speech-to-text with Whisper models. I have also used free OpenRouter models when building and testing agentic applications.

For me, the biggest advantage is that cost is no longer the first thing I have to think about. I do not have to add a credit card and worry about accidentally consuming thousands of tokens while experimenting. I can simply connect a free API, build the application, test different models, and see what works.

Between Groq, Mistral, OpenRouter, Cloudflare, and Google, there is now enough free inference available to learn and build a surprising amount without paying anything. The limits may change, but for learning, prototyping, and experimenting with LLMs, AI agents, speech-to-text, multimodal AI, and APIs, cost does not have to be the barrier that stops you from building.
 
 

Abid Ali Awan (@1abidaliawan) is a certified data scientist professional who loves building machine learning models. Currently, he is focusing on content creation and writing technical blogs on machine learning and data science technologies. Abid holds a Master's degree in technology management and a bachelor's degree in telecommunication engineering. His vision is to build an AI product using a graph neural network for students struggling with mental illness.