The Best Free LLM APIs in 2026: 5 Jobs Each One Does Best

How to pick a free LLM API

Free tiers differ on more than just "is it free." The exact limits below are a snapshot from the community-maintained free-llm-api-resources list — check it for the current numbers, since providers change them often. Before you commit, weigh five things — and remember the golden rule: a free tier is for prototyping and low volume, never for production traffic.

1. 01Rate limits — both requests-per-minute and requests-per-day. A high daily cap (Groq, Gemma) suits batch jobs; a low one (Cohere's 1,000/month) suits light use.
2. 02Models — do you get frontier-class models (Gemini Flash, GPT-5 via GitHub Models) or open models (Llama, Qwen, GPT-OSS)?
3. 03Speed — Groq and Cerebras are built for it; most others are standard.
4. 04Data terms — some free tiers train on your inputs (Google outside the EU/UK, Mistral's Experiment plan). Keep sensitive data off those.
5. 05Path to production — can you upgrade on the same API when you outgrow free (OpenRouter, Google, Mistral), or will you re-integrate?

OpenRouter — one key to dozens of free models

OpenRouter is an aggregator that fronts many providers behind one API. Its free tier (20 requests/min, 50/day — up to 1,000/day after a one-time $10 top-up) reaches strong open models: Llama 3.3 70B, GPT-OSS 120B/20B, Qwen3-Coder, Nemotron, and Hermes 405B, all on a shared quota.

1. 01When you want to compare models for a task, reach for OpenRouter to A/B them behind one key instead of five signups.
2. 02When a free model hits its cap mid-prototype, route to another automatically so your app stays up.
3. 03When you're building an agent, swap models with a single string as better or cheaper ones appear.
4. 04When you need a big model free (Hermes 405B, GPT-OSS 120B), use it without hosting anything yourself.
5. 05When you'll pay later, prototype and launch on the same API so there's no re-integration.

Google AI Studio — the most usable free tier

Google AI Studio is the strongest no-credit-card tier of the big providers. Gemini 3 Flash gives ~250,000 tokens/minute (20 requests/day, 5/min); Flash-Lite allows 500/day; and Gemma 3 open models allow a generous 14,400 requests/day. Note: outside the UK/EEA/Switzerland, free-tier inputs are used to train Google's models.

1. 01When you're prototyping a chat or agent feature, build it on a near-frontier model (Gemini Flash) with no credit card.
2. 02When you need high daily volume, run batch jobs on Gemma 3's 14,400 requests/day.
3. 03When your app is multimodal, parse images, screenshots, or PDFs with Gemini's free vision input.
4. 04When you need long context, feed whole documents into Gemini Flash's large window without paying per token.
5. 05When data is sensitive, keep it off the free tier outside the EU/UK — those inputs train Google's models.

Groq — the fastest inference, plus free speech-to-text

Groq's LPU hardware is built for raw speed, and its free tier is generous on daily requests. It serves open models (Llama 3.1 8B at 14,400 requests/day, Llama 3.3 70B, GPT-OSS, Qwen3) and — usefully — Whisper Large v3 for audio, at 2,000 requests/day.

1. 01When you're building a real-time voice or chat app, use Groq so sub-second latency becomes the feature.
2. 02When you need transcription, run Whisper Large v3 free for up to 2,000 requests/day.
3. 03When you have a high-throughput extraction or classification job, push it through Llama 3.1 8B at 14,400 requests/day.
4. 04When your agent calls the model many times per task, let Groq's speed compound across the loop.
5. 05When you want tool-use free, prototype with groq/compound while you validate the idea.

Cerebras — the other speed king, with a big daily allowance

Cerebras's wafer-scale engine rivals (and on some models beats) Groq on speed, and its free tier is one of the most generous: up to 14,400 requests/day and 1,000,000 tokens/day on gpt-oss-120b and Llama 3.1 8B.

1. 01When your feature lives or dies on latency, serve it from Cerebras's near-instant inference.
2. 02When you need real daily volume, use the 1,000,000 tokens/day free allowance on gpt-oss-120b.
3. 03When you're building a fast reasoning loop, run it where speed, not quality, is the bottleneck.
4. 04When you're demoing, avoid the slow-response problem that kills a live pitch.
5. 05When you're choosing, benchmark Cerebras against Groq on your own workload — both are free and fast.

Mistral (La Plateforme) — the biggest free monthly allowance

Mistral (La Plateforme)'s free Experiment plan is unusually generous: 1 request/second, 500,000 tokens/minute, and up to 1,000,000,000 tokens per month across its open and proprietary models. The catch: it requires opting into data training and phone verification.

1. 01When your prototype is genuinely high-volume, use the up-to-1-billion-tokens/month allowance.
2. 02When you want European models, reach Mistral's open and proprietary lineup from one console.
3. 03When you're running a data-processing pipeline, push it at 500,000 tokens/minute without a bill.
4. 04When data residency matters, prototype in the EU with a European provider.
5. 05When the data is non-sensitive, accept the Experiment plan's training opt-in as a fair trade for the volume.

Mistral (Codestral) — a free dedicated coding model

Codestral is a separate free endpoint for Mistral's code-specialist model — 30 requests/min and 2,000 requests/day, currently free on a monthly-subscription basis (phone verification required).

1. 01When you're adding completion or generation to an editor or tool, use a purpose-built coding model free.
2. 02When you're prototyping a coding agent, do it without burning a paid frontier budget.
3. 03When you need boilerplate, tests, or migrations, generate them at 2,000 requests/day.
4. 04When you want a fair test, compare a code specialist against general models on your own tasks.
5. 05When you're validating an internal dev tool, ship it on the free coding endpoint first.

NVIDIA NIM — a catalog of open models on one console

NVIDIA NIM exposes a large catalog of open models (Nemotron, Llama, DeepSeek, Qwen) on enterprise infrastructure, at 40 requests/minute free (phone verification required; models tend to be context-window limited).

1. 01When you want to survey open models, try dozens (Nemotron, Llama, DeepSeek, Qwen) without hosting any.
2. 02When you need reasoning free, prototype on Nemotron's reasoning endpoints.
3. 03When you're picking a model, benchmark it on build.nvidia.com, then call it with the same API.
4. 04When you'll self-host later, test a model on NVIDIA infra before you buy GPUs.
5. 05When 40 requests/minute is enough, prototype on enterprise-grade infrastructure for free.

GitHub Models — prototype against the big commercial models

GitHub Models lets you call frontier commercial models — OpenAI's GPT-5, o3, and 4.1; DeepSeek; Llama; Mistral; Phi — free from your GitHub account, plus embeddings. Limits are tied to your Copilot tier and the input/output token caps are restrictive, so it's strictly for prototyping.

1. 01When you want to prototype against GPT-5 or o3 free, use your existing GitHub account.
2. 02When you're choosing a model, compare OpenAI, DeepSeek, Llama, Mistral, and Phi in one catalog.
3. 03When you're scoping cost, test which frontier model your feature actually needs before paying a provider.
4. 04When you need semantic search, grab free Text Embedding 3 to prototype it.
5. 05When you already live in GitHub, build a quick demo without leaving the flow.

Cloudflare Workers AI — LLMs at the edge

Cloudflare Workers AI runs inference inside a Cloudflare Worker, next to your users, with no separate backend. The free tier gives 10,000 neurons/day across 50+ open models (Llama, Qwen, GLM-5.2, Kimi K2.6, GPT-OSS, DeepSeek, Gemma), with LoRA-adapter support.

1. 01When your app is already on Cloudflare, run the model in a Worker with no extra backend.
2. 02When you want variety, prototype across 50+ open models on the free 10,000 neurons/day.
3. 03When you serve a global audience, keep latency low by inferring at the edge.
4. 04When you have a fine-tune, serve it with LoRA adapters at the edge.
5. 05When you're adding a small AI feature to an edge-deployed site, do it cheaply and close to users.

Cohere — retrieval, RAG, and multilingual

Cohere's models are tuned for retrieval, grounding, and enterprise search. The free tier is light — 20 requests/min and 1,000 requests/month on a shared quota — across the Command A family and the multilingual Aya models (including vision and Arabic).

1. 01When you're building RAG or search, use models built for retrieval and grounded answers.
2. 02When you need languages, ship multilingual support with the Aya family.
3. 03When you're prototyping an enterprise assistant, validate it on Command A within the monthly cap.
4. 04When documents include images, add understanding with command-a-vision.
5. 05When you're testing a knowledge-base assistant, prove it at 1,000 requests/month before scaling.

HuggingFace Inference Providers — any open model from the Hub

HuggingFace Inference Providers let you call open models straight from the Hub through one interface that routes to multiple underlying providers. The free allowance is small (~$0.10/month in credits) and serverless inference is limited to models under 10GB, but the breadth is unmatched.

1. 01When you need a specific open model, call it from the Hub without downloading or hosting it.
2. 02When you want a niche or fine-tuned model no gateway carries, reach it through HuggingFace.
3. 03When a small model (under 10GB) fits a narrow task, test it before committing to anything bigger.
4. 04When you want provider choice, route to multiple underlying hosts through one interface.
5. 05When your budget is tiny, stretch the ~$0.10/month credits across quick experiments.

Vercel AI Gateway — one key if you're already on Vercel

Vercel AI Gateway routes across supported providers behind one key, with a $5/month free allowance — most useful if you already deploy on Vercel and want your model calls in the same dashboard.

1. 01When your app runs on Vercel, add an LLM without wiring up a provider directly.
2. 02When you want flexibility, route across providers behind one key and swap models freely.
3. 03When you're prototyping, run on the $5/month allowance that comes with your account.
4. 04When you need visibility, keep model calls observable in the Vercel dashboard.
5. 05When the feature is small, ship it on the stack you already deploy to.

OpenCode Zen — a curated gateway for coding models

OpenCode Zen is an AI gateway with a curated set of models, including free coding-focused ones (DeepSeek V4 Flash, Nemotron 3 Super, and a "stealth" model). It speaks the OpenAI format, so it drops into existing tools — but free models may use your data for improvement.

1. 01When you're building a coding agent, plug in curated free coding models through one endpoint.
2. 02When your tool speaks the OpenAI format, point it at OpenCode Zen with no rewrite.
3. 03When you want a stealth, frontier-ish coding model, try it before it's widely available.
4. 04When you're prototyping a dev tool, avoid committing to a provider up front.
5. 05When code is proprietary, keep it off the free models — they may use your data.

When free runs out: providers with trial credits

Free tiers are capped by design. When you need more headroom or a specific model, a wave of providers hand out one-time trial credits — not free forever, but free to start. The largest are worth a look:

The honest caveats

Two things to keep front of mind. First, free tiers are rate-limited and meant for prototyping — do not run production traffic on one, and don't chase the sketchy "1 billion free tokens, unlimited access" gateways that come and go. Second, some free tiers train on your inputs (Google outside the EU/UK, Mistral's Experiment plan, OpenCode Zen's free models), so keep proprietary or personal data off them.

When you outgrow free, move up on an API you already use — our best AI APIs for developers guide covers the paid options, the cheap near-frontier models, and the aggregators like OpenRouter that carry you from free to production without re-integrating.