I’m searching for free AI API platform for
chating, genarate code and create image.
please help me,
Hmm… for now, based on what I found, maybe something like this:
Yes — there are quite a few workable options. The useful distinction, I think, is that chat and code generation can usually be handled by the same text/chat API, while image generation is worth checking separately.
As of August 23, 2026, a rough map looks like this:
| If you want… | A few current options | Main thing to know |
|---|---|---|
| One hosted platform for chat + code + image | Cloudflare Workers AI, Vercel AI Gateway, Hugging Face Inference Providers | All three can cover text and image generation, but their free allowances work very differently |
| Mostly chat + code | Groq, Gemini API, OpenRouter, Mistral | Much more choice; these are generally easier to get useful free text inference from |
| Free evaluation / development access is enough | Cohere, NVIDIA NIM, Cerebras, Fireworks | Some of these are trials/credits rather than a recurring free quota |
| No hosted API bill is the real goal | Puter.js, or local inference such as Ollama | Different cost model rather than a conventional free hosted API |
If I were just trying to get a first project working, I would probably start with one of the first three routes below, make one successful text call and one successful image call, and only then decide whether it is worth mixing providers.
Three fairly simple starting routes
1. Cloudflare Workers AI — good if a recurring free allowance matters
Cloudflare currently includes 10,000 Neurons per day on the Workers Free plan, reset daily at 00:00 UTC. “Neurons” are Cloudflare’s compute unit, not a number of requests, so a small text request and an image generation do not consume the allowance at the same rate.
The useful part for your particular list is that the current Workers AI model catalog includes both text/code models and image-generation models. For example, there is a Qwen2.5-Coder model and FLUX.1-schnell for text-to-image generation.
The current limits and per-model compute costs are on the Workers AI pricing page.
One qualification: the free plan does not mean every Workers AI model is free-plan accessible. Cloudflare moved several resource-heavy models to Workers Paid in July 2026, for example. Their changelog entry is a good example of why I would check the current catalog rather than assume a particular model will stay free forever.
2. Vercel AI Gateway — good if you want one gateway over many model providers
Vercel’s AI Gateway is another interesting fit for “one platform.” Current model pages say that users who have not made a payment receive $5 of Gateway credits every 30 days.
The Gateway covers both ordinary LLM calls and image generation, so that credit can be used across a much broader model catalog rather than being tied to one model family. Vercel has separate official quickstarts for AI Gateway and image generation, and its model pages include playgrounds and sample code.
For example, Vercel currently exposes image generators such as Google Imagen/Gemini-image models, FLUX-family models, GPT Image, Grok Imagine, Recraft, and others through the Gateway.
The important conceptual point is that “one platform” does not have to mean “one model company.” A gateway can give you one account/API surface while routing to different model providers underneath.
I would still use the current model catalog / Free Tier filter when choosing the exact model, because model eligibility changes independently of the gateway itself.
3. Hugging Face Inference Providers — probably the most natural HF-native place to learn
Since we’re already on Hugging Face, this is also a very reasonable first route.
The main limitation is worth stating clearly: the current included credit for a Free HF account is only $0.10/month (and HF explicitly marks that amount as subject to change). See the current Inference Providers pricing page.
That is not a large recurring quota, but the beginner workflow is quite nice:
- find a model currently backed by an inference provider,
- try it from the model widget/playground,
- copy the generated example,
- use
InferenceClient, - optionally let
provider="auto"choose an available provider.
HF’s first Inference Provider call guide actually uses image generation as its beginner example.
So if your immediate goal is simply “learn how an AI API works and get my first calls working”, HF is still a good route even though its included free credit is much smaller than some alternatives.
A low-friction way to start
Whichever provider you choose, I would keep the first experiment very small:
- Try one current model in its playground/widget first.
- Confirm it actually produces the type of output you want.
- Create the API key/token.
- Store the key in an environment variable.
- Run the provider’s smallest official example unchanged.
- Only then connect it to your application.
That separates “does this model fit my task?” from “did I configure the API correctly?”, which saves quite a bit of debugging.
And if image generation later turns out to be the awkward part, you do not need to throw away the rest of the application. Keeping text and image generation behind separate functions is enough to make provider changes fairly painless:
def generate_text(messages):
# Groq / Gemini / Cloudflare / HF / ...
...
def generate_image(prompt):
# Cloudflare / Vercel / HF / ...
...
You can initially point both at the same service, then change only one later.
What does “free” actually mean here?
This is probably the main thing that makes “free AI API” lists confusing. Several quite different things get called free.
| Kind of free access | Example | What it really means |
|---|---|---|
| Recurring compute/request/token allowance | Cloudflare, Groq, selected Gemini models, OpenRouter, Mistral | A limited allowance remains available or resets |
| Recurring dollar credit | Vercel AI Gateway, HF Inference Providers | You receive a small monetary credit periodically |
| Evaluation/trial API | Cohere, NVIDIA development endpoints | Intended mainly for testing/prototyping |
| One-time promotional credit | Cerebras, Fireworks | Free until the starting credit is consumed |
| User-pays architecture | Puter.js | The developer pays $0; each signed-in user consumes their own allowance |
| Local inference | Ollama | No hosted inference bill, but you provide the hardware |
There is another distinction worth remembering:
rate limit != free allowance
A provider might let an account make, say, 1,000 requests/day from a rate-limiting perspective while still charging for those requests after trial credits are gone.
Likewise:
free plan != every model is free
The service itself may have a free tier while particular expensive models are paid-only.
So when comparing providers, I would check:
- Does the allowance reset, or is it a one-time credit?
- Is usage measured in requests, tokens, compute units, or dollars?
- What happens when the allowance runs out?
- Is a payment method needed before the first real API request?
- Is the free access intended for normal experimentation or only evaluation?
- Does the model produce the output you actually need?
- Is that exact model currently enabled for the free tier?
More current chat/code options
If you mainly need chat and code generation, the field gets much wider.
Groq
Groq has a clearly documented Free Plan with model-specific RPM/RPD/TPM/TPD limits.
For example, at the moment the Groq rate-limit table lists models such as openai/gpt-oss-120b, openai/gpt-oss-20b, and qwen/qwen3.6-27b at:
- 30 requests/minute
- 1,000 requests/day
- 8K tokens/minute
- 200K tokens/day
Those numbers can change, so I would use the live table rather than copy them permanently into an application plan.
For straightforward chat/code experiments, though, this is a fairly generous and easy-to-understand free route.
Google Gemini API
Google currently has a genuine Free Tier for several Gemini text/multimodal models. The exact current list and pricing are on the Gemini API pricing page.
For example, the current page lists free input/output for several Flash-family models and for Gemini 2.5 text models.
Gemini is especially worth mentioning for coding and multimodal input, but there is one easy source of confusion:
image input is not the same thing as image generation.
The current pricing tables for native image generators such as Gemini 3.1 Flash Image and Gemini 2.5 Flash Image show Free Tier: Not available for image generation, even though ordinary Gemini text/multimodal models have free tiers.
The Free Tier also currently has different data-use terms from the paid tier, so I would check that page before sending proprietary code or other sensitive material.
OpenRouter
OpenRouter’s current Free plan lists:
- 25+ free models
- API/chat access
- 50 requests/day
This is particularly convenient if the goal is “I want to try several models without creating a provider account for each one.”
OpenRouter also now has a unified image-generation API, but the situation is different from its free text pool. The current OpenRouter image-generation guide says that no image-generation model currently carries the :free suffix, so image generation draws from paid credits.
That guide also makes a distinction I think is useful generally:
image understanding:
image -> text
image generation:
text -> new image
A provider supporting one does not automatically mean it supports the other for free.
Mistral
Mistral currently has an unusually clear beginner setup: its Studio/API-key quickstart says that Free mode is enabled by default and no credit card is required, with usage/rate limits.
Its first API request guide is also deliberately short — create a key, put it in MISTRAL_API_KEY, install the SDK, and make a chat request.
So this is another good candidate if your main needs are chat/code rather than free image generation.
Cohere
Cohere has free evaluation/trial keys, but I would put it in a slightly different category from the providers above.
The current Cohere rate-limit documentation says trial keys are free but limited and currently capped at 1,000 API calls/month, with endpoint-specific per-minute limits.
Good for learning/evaluation; I would not describe that as the same thing as an unrestricted production free tier.
NVIDIA NIM
NVIDIA Build currently advertises “Free serverless APIs for development” and exposes many models as Free Endpoints, including models oriented toward coding, reasoning, vision, speech, retrieval, and other tasks.
I could not find a single stable public quota such as “exactly X requests/day for every NIM,” though. So I would treat it as a useful development/evaluation option and check the current limit shown for the exact endpoint/account rather than assuming a universal recurring quota.
Image generation is the part I would check most carefully
For your three requirements, image creation is currently the one that removes the most otherwise-good free options.
A rough current picture:
Cloudflare Workers AI
This is the clearest traditional recurring-free option I found for combining text/code and image generation.
The Workers AI pricing page lists image models in the same Neuron-based pricing system as text models, and the daily 10,000-Neuron Free allocation applies at the account level.
FLUX.1-schnell is one current example.
Vercel AI Gateway
Vercel is also interesting because the Free user’s recurring Gateway credit can be spent on image models available through the Gateway.
Their image-generation quickstart uses the same AI SDK/Gateway approach as text models, while individual model pages expose current per-image pricing and provider availability.
This can be easier than maintaining separate accounts for several image-model companies.
Hugging Face Inference Providers
HF’s monthly included credit is small, but image generation is a first-class supported inference task, and the official first-call tutorial demonstrates it directly.
So for “I want to understand how to call an image model through an API,” it is a very approachable place to start.
Gemini direct
Gemini is currently a strong free text/code option, but its native image-generation models are not currently listed as Free Tier API models on the pricing page.
That is why I would not just say “Gemini is multimodal, therefore it solves all three for free.”
OpenRouter
OpenRouter now gives one API/key for many image generators, which can be very useful once you are paying small per-image amounts. But its current image guide explicitly says there is no :free image-generation model at the moment.
The free model pool changes, so this is one of those things worth checking live later.
One more subtle trap: a free playground is not always a free API
Some image platforms let you experiment in a browser playground with promotional/sandbox credits, while programmatic API calls use a different billing pool.
So if an article says “you can generate images for free,” I would specifically check whether it means:
- free browser demo,
- free signup credits,
- recurring API quota,
- or an actual no-cost API endpoint.
Those are not interchangeable.
You do not need to lock the whole application to one provider
This is probably the small design choice I would make even for a beginner project.
Rather than letting provider-specific calls spread through the app, give text and image generation their own tiny boundaries:
def generate_text(messages):
...
def generate_image(prompt):
...
Maybe day one is:
text -> Cloudflare
image -> Cloudflare
Then six months later the situation may be:
text -> Groq
image -> Cloudflare
or:
text -> Gemini
image -> Vercel AI Gateway
Nothing else in the application needs to care.
That helps because free tiers and model catalogs change much faster than your application logic should.
For text/chat, provider swapping is often particularly easy because many services expose an OpenAI-compatible API.
But I would interpret “OpenAI-compatible” as:
“a useful common interface for many basic calls”
rather than:
“every feature behaves identically.”
Image generation, tools/function calling, streaming, structured outputs, unsupported parameters, model names, and error behavior can still differ.
So I would keep the application boundary simple rather than trying to build a sophisticated multi-provider router on day one.
A few beginner traps that are easy to avoid
1. multimodal does not necessarily mean image generation
A model may accept an image and answer questions about it while being unable to produce a new image.
Check output modality, not just whether the marketing page says “vision” or “multimodal.”
2. A free service does not mean every model on it is free
Cloudflare is a good current example: Workers AI still has its daily Free allocation, but some expensive models became Paid-plan-only in July.
The same general issue applies to gateways whose model catalogs change frequently.
3. A rate limit is not the amount of money you get for free
Requests/day and tokens/minute tell you how quickly the service allows calls.
Trial credits tell you who pays for those calls.
Those are separate limits.
4. Old “free AI API” lists age very badly
This market changes unusually quickly.
For example, current provider policies no longer necessarily match articles written even a few months ago. When possible I would use the provider’s current pricing/rate-limit page rather than a generic listicle.
This is especially relevant to Hugging Face because older tutorials still use the old serverless Inference API endpoint.
There are real beginner reports of getting 404s from old examples; see this HF Forum thread about the retired api-inference.huggingface.co path.
For new code, I would start from the current Inference Providers documentation.
5. A model existing on the Hub/catalog does not guarantee that every provider serves it for every task
Provider availability, task support, and model availability are separate pieces.
That is another reason to start from a provider’s live model filter/playground rather than picking an arbitrary model name from an old tutorial.
API keys and privacy — two short safety notes
For API keys, the useful beginner rule is simple:
Do not put a provider API key in a public GitHub repository or ship it directly in public client-side JavaScript unless the provider explicitly designed the authentication model for that.
For normal server API keys, use an environment variable or server-side secret:
export MY_API_KEY="..."
and read it from the application environment.
HF’s token documentation recommends scoped/fine-grained tokens where possible, and the HF/Mistral beginner guides both demonstrate environment-variable-style key handling.
There is also a separate privacy question with free tiers.
For example, the current Gemini pricing page says Free Tier content may be used to improve Google’s products, while its paid tier has different terms.
HF’s Inference Providers security page says HF itself does not store routed request/response bodies for training, but the upstream inference provider can have its own policy.
So for toy prompts this may not matter much, but before sending private source code, customer data, credentials, medical/legal documents, etc., I would read the data-use terms of the actual provider handling the request.
A couple of less conventional ways to make the cost problem disappear
There are at least two other architectures worth knowing about.
Puter.js: the developer pays $0, users consume their own allowance
Puter.js uses what it calls a User-Pays model.
Instead of your application owning one API key and paying for every user’s AI calls:
- users sign in with a Puter account,
- every user gets their own monthly AI allowance,
- calls are charged against that user’s allowance,
- users who exceed it pay Puter directly,
- the application developer is not billed for their AI usage.
Its AI API includes both chat and text-to-image functions such as puter.ai.chat() and puter.ai.txt2img().
That is not a conventional “provider gives my developer account an unlimited free API” scheme. The cost owner has simply been moved from the developer to each user.
But if the real requirement is:
“I want to publish a small AI web app without accidentally paying for everybody’s inference,”
that may actually solve the underlying problem better than chasing increasingly complicated free-provider quotas.
Local inference
If you do not specifically need a hosted API, local inference is another branch.
Tools such as Ollama expose a local HTTP/OpenAI-compatible API, so chat/code calls can look much like hosted inference while running on your own computer.
That removes the per-request cloud bill, but it does not make compute literally free: the cost becomes RAM/VRAM, disk, power, hardware, and setup time.
Ollama also currently documents an image-generation compatibility endpoint, but that part is marked experimental, so I would not use it as the default beginner route for the image side yet.
Free-to-try options that I would keep separate from recurring free tiers
A few well-known services are still useful to experiment with, but I would label them differently so “free” does not become misleading.
Cohere
Free evaluation/trial key, currently limited to 1,000 API calls/month according to the rate-limit docs.
NVIDIA NIM
Free serverless development endpoints exist for many models, but I would check the live model/account limit rather than quote one universal recurring quota.
Cerebras
Currently advertises a $5 Free Trial on its pricing page. Useful, but that is a starting credit, not the same thing as a daily/monthly allowance that automatically renews indefinitely.
Fireworks AI
The current Fireworks pricing page says new users start with $1 in free credits. Again, useful for trying the API, but I would classify it as a starting credit rather than a recurring free plan.
Two examples of why old lists need checking
Current Together AI billing documentation says there is no current free trial and that platform access requires purchasing credits.
And SambaNova’s current Cloud plans page has a plan named “Free,” but its current onboarding text requires adding a payment method and purchasing credits to run the first API requests.
So even the word Free in a plan name is not enough to determine whether the first API call actually costs $0.
So, condensed down to a practical decision tree
Want chat + code + image from one hosted platform?
|
+-- Want a recurring compute allowance?
| -> Cloudflare Workers AI
|
+-- Want one gateway over a large catalog?
| -> Vercel AI Gateway
|
+-- Want to stay inside Hugging Face / learn with HF models?
-> Hugging Face Inference Providers
(easy route, but only $0.10/month included for Free users)
Mostly chat + code?
|
+-- Groq
+-- Gemini API
+-- OpenRouter
+-- Mistral
|
+-- Cohere / NVIDIA if evaluation access is enough
Image generation is the only awkward part?
|
+-- Keep generate_image() separate
+-- Use a different image provider without changing the text side
Want to publish an app without paying users' inference bills?
|
+-- Consider a user-pays design such as Puter.js
Have enough local hardware and do not need hosted inference?
|
+-- Consider Ollama/local models
So I would not worry too much about finding the one permanent free AI API. That target moves.
A simpler strategy is:
- choose one current provider that gets you working,
- use the official playground and smallest example first,
- keep the API key private,
- keep text and image generation as separate application boundaries,
- and check the provider’s current pricing/model page whenever you depend on a free quota.
That way, if a free tier or model changes later, it becomes a small provider swap rather than a reason to redesign the project.