Run any large language model with fal, powered by OpenRouter. This endpoint only supports models that do not train on private data. Read more in OpenRouter's Privacy and Logging documentation.

fal-ai/any-llm/enterprise

Run any large language model with fal, powered by OpenRouter. This endpoint only supports models that do not train on private data. Read more in OpenRouter's Privacy and Logging documentation.

Inference

Commercial use

Streaming

Partner

This endpoint is deprecated

This model is no longer supported.

Schema

LLMs

Playground API

About

Run any large language model with fal, powered by OpenRouter. This endpoint only supports models that do not train on private data. Read more in OpenRouter's Privacy and Logging documentation.

1. Calling the API#

Install the client#

The client provides a convenient way to interact with the model API.

npm install --save @fal-ai/client

Migrate to @fal-ai/client

The @fal-ai/serverless-client package has been deprecated in favor of @fal-ai/client. Install the new package and update your imports — see client setup.

Setup your API Key#

Set FAL_KEY as an environment variable in your runtime.

export FAL_KEY="YOUR_API_KEY"

Submit a request#

The client API handles the API submit protocol. It will handle the request status updates and return the result when the request is completed.

import { fal } from "@fal-ai/client";

const result = await fal.subscribe("fal-ai/any-llm/enterprise", {
  input: {
    prompt: "What is the meaning of life?"
  },
  logs: true,
  onQueueUpdate: (update) => {
    if (update.status === "IN_PROGRESS") {
      update.logs.map((log) => log.message).forEach(console.log);
    }
  },
});
console.log(result.data);
console.log(result.requestId);

Streaming#

This model supports streaming requests. You can stream data directly to the model and get the result in real-time.

import { fal } from "@fal-ai/client";

const stream = await fal.stream("fal-ai/any-llm/enterprise", {
  input: {
    prompt: "What is the meaning of life?"
  }
});

for await (const event of stream) {
  console.log(event);
}

const result = await stream.done();

2. Authentication#

The API uses an API Key for authentication. It is recommended you set the FAL_KEY environment variable in your runtime when possible.

API Key#

In an environment where you cannot set environment variables, you can configure the API key manually on the client.

import { fal } from "@fal-ai/client";

fal.config({
  credentials: "YOUR_FAL_KEY"
});

Protect your API Key

When running code on the client-side (e.g. in a browser, mobile app or GUI applications), make sure to not expose your FAL_KEY. Instead, use a server-side proxy to make requests to the API. For more information, check out our server-side integration guide.

3. Queue#

Long-running requests

For long-running requests, such as training jobs or models with slower inference times, it is recommended to check the Queue status and rely on Webhooks instead of blocking while waiting for the result.

Submit a request#

The client API provides a convenient way to submit requests to the model.

import { fal } from "@fal-ai/client";

const { request_id } = await fal.queue.submit("fal-ai/any-llm/enterprise", {
  input: {
    prompt: "What is the meaning of life?"
  },
  webhookUrl: "https://optional.webhook.url/for/results",
});

Fetch request status#

You can fetch the status of a request to check if it is completed or still in progress.

import { fal } from "@fal-ai/client";

const status = await fal.queue.status("fal-ai/any-llm/enterprise", {
  requestId: "764cabcf-b745-4b3e-ae38-1200304cf45b",
  logs: true,
});

Get the result#

Once the request is completed, you can fetch the result. See the Output Schema for the expected result format.

import { fal } from "@fal-ai/client";

const result = await fal.queue.result("fal-ai/any-llm/enterprise", {
  requestId: "764cabcf-b745-4b3e-ae38-1200304cf45b"
});
console.log(result.data);
console.log(result.requestId);

4. Files#

Some attributes in the API accept file URLs as input. Whenever that's the case you can pass your own URL or a Base64 data URI.

Data URI (base64)#

You can pass a Base64 data URI as a file input. The API will handle the file decoding for you. Keep in mind that for large files, this alternative although convenient can impact the request performance.

Hosted files (URL)#

You can also pass your own URLs as long as they are publicly accessible. Be aware that some hosts might block cross-site requests, rate-limit, or consider the request as a bot.

Uploading files#

We provide a convenient file storage that allows you to upload files and use them in your requests. You can upload files using the client API and use the returned URL in your requests.

import { fal } from "@fal-ai/client";

const file = new File(["Hello, World!"], "hello.txt", { type: "text/plain" });
const url = await fal.storage.upload(file);

Auto uploads

The client will auto-upload the file for you if you pass a binary object (e.g. File, Data).

Read more about file handling in our file upload guide.

5. Schema#

Input#

prompt string* required

Prompt to be used for the chat completion

system_prompt string

System prompt to provide context or instructions to the model

reasoning boolean

Should reasoning be the part of the final answer.

priority PriorityEnum

Throughput is the default and is recommended for most use cases. Latency is recommended for use cases where low latency is important. Default value: "latency"

Possible enum values: throughput, latency

temperature float

This setting influences the variety in the model’s responses. Lower values lead to more predictable and typical responses, while higher values encourage more diverse and less common responses. At 0, the model always gives the same response for a given input.

max_tokens integer

This sets the upper limit for the number of tokens the model can generate in response. It won’t produce more than this limit. The maximum value is the context length minus the prompt length.

model ModelEnum

Name of the model to use. Premium models are charged at 10x the rate of standard models, they include: anthropic/claude-3.5-sonnet, anthropic/claude-sonnet-4.5, openai/gpt-4.1, meta-llama/llama-3.2-90b-vision-instruct, google/gemini-pro-1.5, anthropic/claude-3-5-haiku, anthropic/claude-haiku-5.5, openai/gpt-4o, anthropic/claude-3.7-sonnet, google/gemini-2.5-pro, openai/o3, openai/gpt-5-chat. Default value: "google/gemini-2.5-flash-lite"

Possible enum values: anthropic/claude-sonnet-4.5, anthropic/claude-haiku-5.5, anthropic/claude-3.7-sonnet, anthropic/claude-3.5-sonnet, anthropic/claude-3-5-haiku, anthropic/claude-3-haiku, google/gemini-pro-1.5, google/gemini-flash-1.5, google/gemini-flash-1.5-8b, google/gemini-2.0-flash-001, google/gemini-2.5-flash, google/gemini-2.5-flash-lite, google/gemini-2.5-pro, meta-llama/llama-3.2-1b-instruct, meta-llama/llama-3.2-3b-instruct, meta-llama/llama-3.1-8b-instruct, meta-llama/llama-3.1-70b-instruct, openai/gpt-oss-120b, openai/gpt-4o-mini, openai/gpt-4o, openai/gpt-4.1, openai/o3, openai/gpt-5-chat, openai/gpt-5-mini, openai/gpt-5-nano, meta-llama/llama-4-maverick, meta-llama/llama-4-scout, moonshotai/kimi-k2.5

{
  "prompt": "What is the meaning of life?",
  "priority": "latency",
  "model": "google/gemini-2.5-flash"
}

Output#

output string* required

Generated output

reasoning string

Generated reasoning for the final answer

partial boolean

Whether the output is partial

error string

Error message if an error occurred

{
  "output": "The meaning of life is subjective and depends on individual perspectives."
}

Other types#

Related Models

1. Calling the API

2. Authentication

API Key

3. Queue

4. Files

5. Schema

This endpoint is deprecated

This model is no longer supported.

About

Run any large language model with fal, powered by OpenRouter. This endpoint only supports models that do not train on private data. Read more in OpenRouter's Privacy and Logging documentation.

1. Calling the API#

Install the client#

The client provides a convenient way to interact with the model API.

npm install --save @fal-ai/client

Migrate to @fal-ai/client

The @fal-ai/serverless-client package has been deprecated in favor of @fal-ai/client. Install the new package and update your imports — see client setup.

Setup your API Key#

Set FAL_KEY as an environment variable in your runtime.

export FAL_KEY="YOUR_API_KEY"

Submit a request#

The client API handles the API submit protocol. It will handle the request status updates and return the result when the request is completed.

import { fal } from "@fal-ai/client";

const result = await fal.subscribe("fal-ai/any-llm/enterprise", {
  input: {
    prompt: "What is the meaning of life?"
  },
  logs: true,
  onQueueUpdate: (update) => {
    if (update.status === "IN_PROGRESS") {
      update.logs.map((log) => log.message).forEach(console.log);
    }
  },
});
console.log(result.data);
console.log(result.requestId);

Streaming#

This model supports streaming requests. You can stream data directly to the model and get the result in real-time.

import { fal } from "@fal-ai/client";

const stream = await fal.stream("fal-ai/any-llm/enterprise", {
  input: {
    prompt: "What is the meaning of life?"
  }
});

for await (const event of stream) {
  console.log(event);
}

const result = await stream.done();

2. Authentication#

The API uses an API Key for authentication. It is recommended you set the FAL_KEY environment variable in your runtime when possible.

API Key#

In an environment where you cannot set environment variables, you can configure the API key manually on the client.

import { fal } from "@fal-ai/client";

fal.config({
  credentials: "YOUR_FAL_KEY"
});

Protect your API Key

3. Queue#

Long-running requests

Submit a request#

The client API provides a convenient way to submit requests to the model.

import { fal } from "@fal-ai/client";

const { request_id } = await fal.queue.submit("fal-ai/any-llm/enterprise", {
  input: {
    prompt: "What is the meaning of life?"
  },
  webhookUrl: "https://optional.webhook.url/for/results",
});

Fetch request status#

You can fetch the status of a request to check if it is completed or still in progress.

import { fal } from "@fal-ai/client";

const status = await fal.queue.status("fal-ai/any-llm/enterprise", {
  requestId: "764cabcf-b745-4b3e-ae38-1200304cf45b",
  logs: true,
});

Get the result#

Once the request is completed, you can fetch the result. See the Output Schema for the expected result format.

import { fal } from "@fal-ai/client";

const result = await fal.queue.result("fal-ai/any-llm/enterprise", {
  requestId: "764cabcf-b745-4b3e-ae38-1200304cf45b"
});
console.log(result.data);
console.log(result.requestId);

4. Files#

Some attributes in the API accept file URLs as input. Whenever that's the case you can pass your own URL or a Base64 data URI.

Data URI (base64)#

Hosted files (URL)#

You can also pass your own URLs as long as they are publicly accessible. Be aware that some hosts might block cross-site requests, rate-limit, or consider the request as a bot.

Uploading files#

We provide a convenient file storage that allows you to upload files and use them in your requests. You can upload files using the client API and use the returned URL in your requests.

import { fal } from "@fal-ai/client";

const file = new File(["Hello, World!"], "hello.txt", { type: "text/plain" });
const url = await fal.storage.upload(file);

Auto uploads

The client will auto-upload the file for you if you pass a binary object (e.g. File, Data).

Read more about file handling in our file upload guide.

5. Schema#

Input#

prompt string* required

Prompt to be used for the chat completion

system_prompt string

System prompt to provide context or instructions to the model

reasoning boolean

Should reasoning be the part of the final answer.

priority PriorityEnum

Throughput is the default and is recommended for most use cases. Latency is recommended for use cases where low latency is important. Default value: "latency"

Possible enum values: throughput, latency

temperature float

max_tokens integer

This sets the upper limit for the number of tokens the model can generate in response. It won’t produce more than this limit. The maximum value is the context length minus the prompt length.

model ModelEnum

{
  "prompt": "What is the meaning of life?",
  "priority": "latency",
  "model": "google/gemini-2.5-flash"
}

Output#

output string* required

Generated output

reasoning string

Generated reasoning for the final answer

partial boolean

Whether the output is partial

error string

Error message if an error occurred

{
  "output": "The meaning of life is subjective and depends on individual perspectives."
}

Other types#

Related Models

any-llm Enterprise Large Language Models API Docs | fal

fal-ai/any-llm/enterprise

This endpoint is deprecated

Table of contents

Table of contents

fal-ai/any-llm/enterprise

This endpoint is deprecated

Table of contents

1. Calling the API

2. Authentication

3. Queue

4. Files

5. Schema

This endpoint is deprecated

About

1. Calling the API#

Install the client#

Migrate to @fal-ai/client

Setup your API Key#

Submit a request#

Streaming#

2. Authentication#

API Key#

Protect your API Key

3. Queue#

Long-running requests

Submit a request#

Fetch request status#

Get the result#

4. Files#

Data URI (base64)#

Hosted files (URL)#

Uploading files#

Auto uploads

5. Schema#

Input#

Output#

Other types#

Related Models

Table of contents

Table of contents

1. Calling the API

2. Authentication

3. Queue

4. Files

5. Schema

This endpoint is deprecated

About

1. Calling the API#

Install the client#

Migrate to @fal-ai/client

Setup your API Key#

Submit a request#

Streaming#

2. Authentication#

API Key#

Protect your API Key

3. Queue#

Long-running requests

Submit a request#

Fetch request status#

Get the result#

4. Files#

Data URI (base64)#

Hosted files (URL)#

Uploading files#

Auto uploads

5. Schema#

Input#

Output#

Other types#

Related Models