No credit cards, no expiring credits: just pure, free API access for your next AI project

· Source: AI on Medium · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Software Development & Engineering, Cloud Computing & IT Infrastructure · Depth: Intermediate, medium

Summary

This guide details five platforms offering "true free" API access to large language models (LLMs) for developers, eliminating credit card requirements and expiring credits for prototyping. OpenRouter acts as an aggregator, providing Meta Llama, Google Gemma, and Mistral models via an OpenAI SDK-compatible API, though it is rate-limited. Google AI Studio offers Gemini 3.5 Flash, 1.5 Flash, 2.5 Flash, and 1.5 Pro, notably featuring a 1-2 million token context window, with rate limits of 15 RPM for Flash and 5 RPM for Pro. Groq utilizes proprietary LPUs for high-speed inference, supporting Llama (up to 70B) and Qwen models, ideal for real-time applications despite strict rate limits. GitHub Models provides free access to OpenAI's GPT-4o and GPT-4o-mini, Llama, and Mistral via a GitHub Personal Access Token for testing. Finally, NVIDIA NIM offers a permanent 40 RPM for its optimized Nemotron (up to 550B), Kimi, and other specialized models on NVIDIA's infrastructure.

Key takeaway

For AI Engineers or students prototyping new applications, you no longer need to incur costs or manage expiring credits. Evaluate your project's specific needs—whether it's high-speed inference, massive context windows, or access to specific models like GPT-4o—and select from the available free API tiers. This accessibility allows you to experiment freely and build robust prototypes without financial barriers.

Key insights

Developers can now access numerous free, permanent LLM APIs without credit cards or expiring credits for prototyping.

Principles

Method

Sign up for a developer account, navigate to the "Keys" or "Models" section, and generate an API key or use an existing Personal Access Token.

In practice

Topics

Code references

Best for: AI Engineer, Machine Learning Engineer, AI Student

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Editorial summary, takeaway, and curation by AIssential. Original article published by AI on Medium.