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This model is no longer supported.
This model is no longer supported.
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.
The client provides a convenient way to interact with the model API.
npm install --save @fal-ai/clientThe @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.
Set FAL_KEY as an environment variable in your runtime.
export FAL_KEY="YOUR_API_KEY"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);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();The API uses an API Key for authentication. It is recommended you set the FAL_KEY environment variable in your runtime when possible.
import { fal } from "@fal-ai/client";
fal.config({
credentials: "YOUR_FAL_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.
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",
});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,
});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);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.
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.
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.
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);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.
prompt string* requiredPrompt to be used for the chat completion
system_prompt stringSystem prompt to provide context or instructions to the model
reasoning booleanShould reasoning be the part of the final answer.
priority PriorityEnumThroughput 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 floatThis 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 integerThis 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 ModelEnumName of the model to use. Premium models are charged at 10x the rate of standard models, they include: openai/o3, openai/gpt-5-chat, openai/gpt-4o, google/gemini-pro-1.5, anthropic/claude-3.5-sonnet, anthropic/claude-sonnet-4.5, meta-llama/llama-3.2-90b-vision-instruct, google/gemini-2.5-pro, anthropic/claude-haiku-5.5, anthropic/claude-3.7-sonnet, openai/gpt-4.1, anthropic/claude-3-5-haiku. 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 string* requiredGenerated output
reasoning stringGenerated reasoning for the final answer
partial booleanWhether the output is partial
error stringError message if an error occurred
{
"output": "The meaning of life is subjective and depends on individual perspectives."
}