Free AI APIs: Which Ones Are Actually Free?

TL;DR — Best truly-free LLM APIs in 2026: Gemini 1.5 Flash (15 RPM, no credit card), Groq (14,400 RPQ free), Mistral (1 RPM free tier). OpenAI requires billing info. Anthropic gives $5 credit. None of them are free past early prototyping volume — budget $10–50/month for any real project.

"Free tier" doesn't mean free. It means free until you hit a limit — and every provider sets that limit differently. Some give you free requests per minute. Some give you a one-time credit that runs out. Some give you nothing at all and call it a free trial because you can look at the playground.

If you're trying to build a side project, prototype an AI feature, or test before committing to a provider, you need to know exactly what "free" means for each API. This guide covers every major LLM provider's free tier as of June 2026 — what you actually get, where the limits bite, and which free tiers are viable for real development.

The complete free tier comparison

Google Gemini: The best free tier for development

Google offers the most generous free tier by far. The Gemini API through Google AI Studio provides:

• 15 requests per minute for Gemini 2.5 Flash
• 2 requests per minute for Gemini 2.5 Pro
• 1 million tokens per minute (input)
• No credit card required
• No expiration — it's a permanent free tier, not a trial credit

What you can realistically build: At 15 RPM, you can handle roughly 1 request every 4 seconds. That's enough for a personal tool, a demo, or a low-traffic prototype. It's not enough for a production app with concurrent users.

The catch: Free tier requests get lowest priority. During peak hours, you may experience slower responses or temporary throttling. Google also reserves the right to use free tier data for model improvement — paid tier data is not used for training.

When you'll need to pay: When you need more than 15 RPM, when you need guaranteed latency, or when you're handling user data that shouldn't be used for training. Paid pricing starts at $0.15/1M input tokens for Flash — one of the cheapest options available.

OpenAI: No free tier at all

OpenAI does not offer a free API tier. Period.

You must add a payment method before making any API call. There's no trial credit, no free RPM allocation, no sandbox mode. The OpenAI Playground lets you test prompts in the browser, but that uses your paid credits.

How to test OpenAI cheaply: Use GPT-4o-mini at $0.15/1M input tokens. A day of heavy testing (1,000 calls with 500-token prompts and 200-token responses) costs approximately $0.20. That's cheaper than any "free tier" frustration. For current pricing across all OpenAI models, see our pricing guide.

Why no free tier? OpenAI's models are the most expensive to run. Offering a free tier would attract massive abuse. Their strategy is to keep GPT-4o-mini pricing so low ($0.15/1M) that the cost of a free tier isn't worth the support overhead.

Anthropic: $5 one-time credit

Anthropic gives new accounts a $5 API credit. That's enough for:

• ~1.6 million input tokens on Claude Sonnet 4 ($3/1M)
• ~6.25 million input tokens on Claude Haiku 3.5 ($0.80/1M)
• Roughly 500-2,000 API calls depending on prompt length

How long it lasts: For a developer testing prompts and building a prototype, $5 lasts 1-3 weeks of moderate use. For a hackathon or weekend project, it's plenty.

The catch: Once the credit runs out, you pay per token. There's no ongoing free tier. You also need to add a payment method even to use the credit.

Best use: Evaluate whether Claude fits your use case. Test prompt quality, compare output against GPT-4o, and decide if it's worth paying for before committing.

Groq: Free and fast (open-source models only)

Groq offers a genuinely useful free tier:

• 30 requests per minute
• 15,000 tokens per minute
• Access to Llama 3.3 70B, Mixtral 8x7B, Gemma 2 9B
• No credit card required

What you can build: Groq's free tier is viable for low-traffic production apps. 30 RPM is enough for a tool that handles a few concurrent users. The speed is exceptional — Groq runs on custom LPU hardware and delivers sub-second responses.

The catch: Only open-source models. No GPT-4o, no Claude. If your app depends on a specific proprietary model's behavior, Groq's free tier doesn't help. But for tasks where Llama 3.3 70B performs comparably (summarization, classification, simple Q&A), it's a legitimate free option.

The hidden limit: 15,000 tokens per minute means roughly 10-15 calls per minute with typical prompt sizes. The RPM limit (30) is higher than the TPM limit effectively allows. Plan around TPM, not RPM.

Can you test without being charged?

Yes, if you pick the right provider:

Zero-cost testing options:

1. Google Gemini free tier — permanent, no card needed, 15 RPM
2. Groq free tier — permanent, no card needed, 30 RPM (open-source models)
3. Local models via Ollama — run Llama, Mistral, or Gemma on your own machine, zero API cost
4. OpenRouter free models — aggregator that offers some models at no cost

Near-zero testing options: 5. OpenAI GPT-4o-mini — $0.15/1M tokens, a full day of testing costs under $0.50 6. DeepSeek V3 — $0.27/1M input tokens, slightly more than mini but strong reasoning 7. Anthropic with $5 credit — enough for weeks of testing

The best approach: Test with Google Gemini's free tier first. If your use case requires OpenAI or Anthropic specifically, switch to their cheapest model (GPT-4o-mini or Claude Haiku) for validation. Only move to expensive models when you've confirmed the cheaper ones don't meet your quality bar.

Free vs paid: the real cost comparison

The question isn't whether free tiers exist — it's whether they're viable beyond prototyping.

The transition from free to paid is where costs surprise people. On a free tier, you're rate limited to 15 RPM. When you move to paid, there's no rate limit safety net — your app can make 500 RPM and your bill scales accordingly. This is exactly when teams realize they need budget alerts and spending caps.

Why free tiers create bad cost habits

Free tiers encourage three habits that become expensive at scale:

1. Using the biggest model for everything. When it's free, why not use the best model? Because when you switch to paid, that habit costs 10-20x more than necessary. Start with the smallest model that works, even during testing. It builds the right instinct.

2. Not tracking usage. If you're not paying, you're not watching. When you start paying, you have no baseline for what "normal" usage looks like. Start tracking your usage from day one, even on the free tier.

3. Ignoring prompt efficiency. A 3,000-token system prompt is free on the free tier. At GPT-4o rates, that same prompt costs $0.0075 per call. At 10,000 calls/day, that's $75/day for the system prompt alone. Optimize your prompts before you need to pay for them.

The practical recommendation

For testing and prototyping:

1. Start with Google Gemini free tier (best models available for free)
2. Test with Groq for speed comparison (Llama 3.3 is surprisingly capable)
3. Use Anthropic's $5 credit if you specifically need Claude

For moving to production:

1. Switch to paid tiers with the cheapest model that works
2. Set up cost monitoring before your first paid call
3. Set budget alerts at 50% and 80% of your monthly target
4. Estimate your costs using the formulas in our estimation guide

For ongoing cost control:

1. Use a proxy like Tokonomics to track every call across all providers
2. Audit monthly to catch model-mix inefficiencies
3. Set hard caps to prevent runaway costs

The free tier is where you learn. The paid tier is where you optimize. The teams that treat both phases seriously are the ones whose AI features stay profitable.

Last updated June 2026. All sources retrieved June 2026.