OpenLuxFree LLM APIs: what is actually free in 2026?
A free LLM API removes the invoice, not the constraint. Request caps, token limits, expiring model IDs, queueing and data-use terms decide whether a free tier can support a real workload. This guide compares the public options and shows when paid inference is cheaper per accepted task.
Reviewed by OpenLux Engineering Team · Last verified
Which free LLM APIs are worth testing?
These options are useful for prototypes and evaluation. None should be treated as an unlimited production entitlement.
| Provider | What is free | Limit to verify | Main trade-off |
|---|---|---|---|
| OpenRouter | Models whose IDs end in :free | 20 requests per minute; 50 requests per day under $10 lifetime credits; 1,000 per day after that threshold | Model availability and upstream data handling vary; free IDs can disappear |
| Google Gemini API | A free tier for eligible models | RPM, TPM and RPD are project- and model-specific; daily requests reset at midnight Pacific | Not every model is free; unpaid-service data terms must be reviewed |
| Groq | Free rows for selected models | Limits are model-specific; one published gpt-oss-120b row showed 30 RPM, 1,000 RPD, 8K TPM and 200K TPD | Fast inference, but availability and free quotas can change by model |
| Mistral | Free mode with included monthly usage | Included usage and per-model request/token limits appear in the Admin limits dashboard | Your plan, project and model define the ceiling; free mode is not unlimited |
How we evaluated this
We reviewed the public rate-limit, pricing, usage and data-term documentation for OpenRouter, Google Gemini API, Groq and Mistral on 5 November 2026. The table is a snapshot of policies that can change without notice, not a contractual promise from any provider.
Free-tier availability differs by model, project, account state and region. Verify the linked official page and the terms attached to the exact model before sending production or confidential data.
Free LLM API evaluation checklist
Run these checks against the exact model and account you will deploy, not a roundup from another month.
- Measure the requests per minute, tokens per minute and requests per day that one real task consumes.
- Confirm whether limits apply per account, project, key or model.
- Check the context window and output cap after the free model has been selected.
- Record the model-ID expiry date or deprecation process and configure a paid fallback.
- Read the current training, retention, human-review and region terms for the exact service tier.
- Test peak-hour latency, queueing, timeout behavior and the error returned at the daily cap.
- Calculate cost per accepted task after retries and human review, not the displayed token price alone.
- Keep secrets server-side and use a separate key or project for experiments.
Set it up in four steps
Start with one bounded workload
Choose a classification, extraction or short chat task with a measurable acceptance rule. Avoid beginning with a long-running coding agent whose repeated context consumes the cap.
Log limits and failures
Record request count, input and output tokens, latency, 429 responses and the model ID returned. A successful demo under ten calls does not reveal the daily ceiling.
Price the paid alternative
Replay representative tokens through the LLM API cost calculator and compare delivered cost per accepted task, including retries and review.
Keep an exit path
Use an OpenAI-compatible client boundary, store the model name in configuration and test a paid compatible model before the free model expires.
What counts as a free LLM API?
A free LLM API is an inference service that does not charge for some requests under a defined allowance. The allowance may be a daily request count, token throughput, monthly credits, a promotional model ID or access tied to an account tier.
“No credit card” is not the same as unlimited, private or production-safe. A useful free tier makes its limits and data terms visible before the first request.
What limits usually run out first?
Daily request caps often bind before a raw token price matters. A coding agent may send tens of thousands of context tokens per step, so a plan advertised as one million tokens per day can support only a small number of complete tasks.
Tokens-per-minute limits matter for concurrency. A batch job that respects the daily total can still produce bursts of 429 responses if it ignores RPM and TPM. Read the headers or dashboard for every dimension instead of tracking one counter.
Do free tiers train on your prompts?
Treat free-tier data handling as a separate review from paid API terms. Some unpaid services use inputs and outputs to improve products, may involve human review, or may have different retention and regional handling. The exact terms can change by provider and model.
Do not send customer records, source code, credentials or regulated data to a free endpoint until the current terms match your data classification. If you need stronger terms, compare paid endpoints and contractual controls rather than assuming the free label changes only price.
How far does a free tier go for a coding agent?
Use a simple estimate: daily tasks = available tokens per day ÷ tokens per completed task. If one repository-aware agent loop consumes 21,000 input and output tokens across planning, tool calls and edits, one million tokens per day allows roughly 47 requests before retries and failed attempts.
That may be enough for a small evaluation, but a team running frequent reviews or long refactors can exhaust the allowance quickly. Measure the agent's actual token direction rather than using a chatbot's short-prompt estimate.
When is a paid API cheaper than free?
A paid API becomes cheaper when it removes waiting, failed attempts, manual account juggling or extra human review. Calculate the total as model cost plus retry cost plus review time, then divide by accepted tasks. A free request that requires two retries may already deserve a paid comparison.
Paid rates also provide a stable unit for forecasting. If the workload can be priced at a fraction of a cent per small task, engineering time spent preserving an expiring free route may cost more than the inference.
How do you avoid lock-in to a free model?
Keep the provider behind an OpenAI-compatible client, move the model ID into environment configuration and avoid relying on provider-only response fields in core business logic. Test one paid fallback with the same prompts and response parser.
A fallback should be exercised, not merely documented. Run a small evaluation before the free model expires and record quality, latency and cost per accepted task.
Where does OpenLux fit?
OpenLux is not a free-model catalogue. It is a paid OpenAI-compatible API gateway with trial credit, published USD rates and request-level usage records. That makes it a comparison point for teams moving from evaluation to predictable production spend.
The right decision can still be a free tier for a prototype. Track the official limits, keep sensitive data on endpoints with suitable terms and switch when the measured workload outgrows the allowance or the free model's availability.
Paid models to compare when the free tier runs out
| Model | Vendor | Price | Vs. official |
|---|---|---|---|
| gemini-3-flash-preview | $0.0368 / 1M tokens | -92% | |
| gemini-3.8-flash | $0.0551 / 1M tokens | -92% | |
| deepseek-v4 | DeepSeek | $0.0613 / 1M tokens | -90% |
| claude-sonnet-4-5-20250929 | Anthropic | $0.2647 / 1M tokens | -91% |
Prices are metered in USD; the full list lives in the model catalogue.
Key takeaways
- Free LLM API means a limited allowance, not zero total cost.
- OpenRouter publishes 20 RPM and either 50 or 1,000 free-model requests per day depending on lifetime credits purchased.
- Google, Groq and Mistral limits are model or project specific and should be checked at implementation time.
- Daily token and request caps can make a free coding-agent loop far smaller than expected.
- Data-use terms and model expiry are buying criteria, not footnotes.
Frequently asked questions
Is a free LLM API really free?
It is free only within a defined allowance. Request limits, waiting, failures, model expiry and data-use restrictions can create engineering or business cost even when the provider charges no money.
What are OpenRouter's free model limits?
OpenRouter documents 20 requests per minute for :free model variants and 50 requests per day for accounts with less than $10 in lifetime credits. Accounts that meet the credit threshold receive a 1,000-request daily ceiling.
Is the Google Gemini API free tier private?
Do not assume that unpaid services have the same data terms as paid ones. Review Google's current terms for the exact tier and project, and keep confidential or regulated data on an endpoint whose written terms meet your policy.
Can a free LLM API run a coding agent?
Sometimes for short evaluations, but repository context, tool loops and retries consume tokens quickly. Calculate tokens per accepted task and keep a paid compatible model ready for larger workloads.
Do free model IDs expire?
Many promotional models do. Record the model's documented lifecycle and test a fallback before the free route is removed or moved to paid pricing.
Is OpenLux a free LLM API?
No. OpenLux provides trial credit and then meters paid usage at published USD rates. Its fit is moving an evaluated workload to a predictable production endpoint with request logs.
Sources and official documentation
- OpenRouter API credit and rate limitsOfficial free-model request limits and account behavior.
- Google Gemini API pricingOfficial free and paid tier model pricing.
- Google Gemini API rate limitsOfficial project and model rate-limit dimensions.
- Groq rate limitsOfficial free and developer model limits.
- Mistral usage and limitsOfficial plan, usage and model limit documentation.
- OpenLux pricingTrial credit, published rates and paid plan options.
Keep reading
LLM API cost calculator
Turn a representative workload into a monthly estimate.
Cheapest LLM API
Compare paid token and request rates without hiding retries.
OpenRouter alternative checklist
Compare free access with managed gateway trade-offs.
LLM prompt caching
Reduce repeated context before changing providers.
OpenAI-compatible quickstart
Keep a portable client boundary for the paid fallback.
Rate limits and 429 recovery
Understand RPM, TPM, Retry-After and retry budgets.
Model the workload before the free tier runs out
Use trial credit to replay representative prompts, inspect request cost and compare a paid fallback on the same compatible API.