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Free AI API Quotas in 2026: A Survey of LLM Free Tiers and New-User Credits

Which LLM APIs can you still use for free in September 2026? A layer-by-layer survey of free tiers, signup credits, and limited-time offers across SiliconFlow, Zhipu, Volcengine Ark, Groq, OpenRouter, and more.

Are free LLM APIs still a thing in 2026? Yes—and the landscape is more competitive than two years ago. Chinese platforms compete on new-user credits; overseas platforms compete on permanent free tiers. But free quotas are not a promise: some are permanently free small models, some are one-time signup gifts, and some only exist inside a short activity window.

This post surveys the free channels that were publicly verifiable as of 2026-09-04, organized into three layers with the fine print for each. Numbers were checked on 2026-09-04; policies change constantly, so treat every official page as the final word.

TL;DR: free quotas come in three layers

LayerExamplesPattern
Permanent free tierZhipu Flash series, SiliconFlow small models, Groq free tier, OpenRouter :freeSmall models or rate limits, but stable enough to keep in your toolbox
New-user creditsZhipu starter pack, Meituan LongCat, Volcengine Ark, Tianyi Cloud trialOne-time, expiring; campaigns come back periodically
Limited-time dealsPromotions, temporarily free models, referral rewardsShort windows; you either catch them or you don’t

Before investing time in any free channel, figure out which layer it belongs to. Layer one can enter your daily loop immediately; layers two and three need to be weighed against their expiry.

Chinese platforms

SiliconFlow: permanently free small models + signup credit

SiliconFlow keeps several small Qwen/GLM models at ¥0 indefinitely, and adds a ¥16 general credit for verified new users (campaign runs until 2026-12-31). It’s an OpenAI-compatible endpoint, so swapping a base_url gets it into your existing toolchain.

Zhipu: Flash series + 20M token starter pack

Zhipu’s open platform grants a 20-million-token starter pack on registration; Flash-series models (such as GLM-4-Flash) are open for free calls long-term. The freshly released GLM-5.3-Flash ships with open weights and free commercial use, raising the ceiling of what the free tier can do.

Volcengine Ark: 500K tokens, permanently free

ByteDance’s Volcengine Ark grants a permanent free quota of 500,000 tokens on selected models, with extra trial credits around new model launches.

Meituan LongCat: the most aggressive newcomer of 2026

Meituan’s LongCat grants 10 million tokens after real-name verification, plus a ~¥9.90 starter package of roughly 50 million tokens; cache hits are free. When a price war gets this aggressive, individual developers are the direct beneficiaries.

Tianyi Cloud Xirang: 25M token trial pack

Tianyi Cloud (Xirang) offers a 25-million-token trial pack (valid 1–3 months) after real-name verification, covering mainstream third-party models. Big volume, short window.

Baidu Qianfan / Alibaba Bailian: credits follow campaign cycles

Baidu Qianfan and Alibaba Cloud Bailian are the two veterans: new-user credits roll in with campaign cycles, so the exact amounts always depend on what the console shows. That’s the clearest difference between “signup credits” (variable) and “permanent free tier” (something you can plan around).

Overseas free tiers

PlatformFree offeringLimits
GroqPermanent free API tier, very fast LPU inferenceStrict rate limits (RPM/RPD/TPM)
OpenRouter25+ :free models behind one key20 RPM / 50 RPD; RPD rises to 1000 after $10 top-up
Google AI StudioFree Gemini quotaFloats by model and period; check the page
DigitalOcean$200 for new accounts60 days, card required; 1 year with GitHub Student Pack

Overseas free tiers run on the opposite logic: “permanent but rate-limited.” That means they won’t carry production-scale concurrency, but they’re more than enough for prototypes, personal scripts, and model comparisons.

The real difference between signup credits and free tiers

Why would a platform hand you 20 million tokens? Because a one-time credit is a customer-acquisition cost, priced so that most trial users stay and pay. The best personal use is therefore to treat credits as evaluation coupons, not infrastructure: tour the platform’s whole family of models, pick what you’d actually pay for, then decide.

The permanent free tier exists because inference has gotten cheap enough to give away in constrained form (small models, rate limits, or hardware efficiency)—which makes it the layer worth embedding in your daily workflow. A common pattern: develop on free tiers and credits, and pay only for the production tokens you actually need.

How to keep catching new deals

Quotas change; the habits shouldn’t. I’ve written up a source-priority ordered routine in How to Catch AI Freebies Without Missing a Window. One-line version: official announcement pages for big campaigns, aggregators for the long tail—you need both.

My Telegram channel Lx_groups aggregates Chinese-language AI free quotas, limited-time offers, and deal intelligence daily, filtered from 19 upstream sources, no ads and no gray-market content. If you don’t want to install a client, open the web preview at t.me/s/Lx_groups in a browser.

FAQ

Can platforms claw back free quotas?

Signup credits expire, and policies—including permanent tiers—can change or downsize. That’s why any quota roundup needs a timestamp: this one was checked on 2026-09-04, and the final confirmation should always happen in the official console.

Do overseas platforms require a credit card?

Groq and OpenRouter don’t. DigitalOcean requires a card to activate. Google AI Studio normally needs a Google account. If you’d rather not register a card, start with Chinese platforms plus Groq.

Are there student benefits?

Yes. The GitHub Student Developer Pack extends DigitalOcean’s $200 credit from 60 days to one year, and several Chinese platforms have student verification channels. Check the student pages directly.

Free API vs. running open models locally?

Free APIs need no hardware and work instantly, at the cost of rate limits and content-compliance constraints. Local open models (GLM-5.3-Flash, Llama series) keep your data on-device but carry hardware and ops costs. For most people the optimal path is: free APIs during learning and prototyping, a local evaluation once volume justifies it.