Implementation:FlowiseAI Flowise Models Registry

Knowledge Sources	FlowiseAI Flowise
Domains	Configuration, AI Models
Last Updated	2026-02-12 07:00 GMT

Overview

This JSON registry file catalogs all available AI models for chat, LLM, and embedding providers, including their pricing and supported AWS regions for Bedrock deployments.

Description

The file is organized into three top-level categories: chat (line 2), llm (line 1655), and embedding (line 2031). Each category contains an array of provider entries, where each provider has a name and an array of model definitions. Model entries include a human-readable label, an API-compatible name, an optional description, and input/output cost per token. The chat category is the largest, covering providers such as awsChatBedrock, azureChatOpenAI, chatAnthropic, chatGoogleGenerativeAI, chatOpenAI, groqChat, chatMistralAI, chatPerplexity, and others. The AWS Bedrock providers also include a regions array listing all supported AWS deployment regions.

Usage

Use this registry to populate model selection dropdowns in the Flowise UI, calculate cost estimates for model usage, and validate model availability per provider. It is loaded by components that need to present users with available model choices.

Code Reference

Source Location

• Repository: FlowiseAI Flowise
• File: packages/components/models.json
• Lines: 1-2417

Signature

{
    "chat": [
        {
            "name": "awsChatBedrock",
            "models": [ ... ],
            "regions": [ ... ]
        },
        { "name": "azureChatOpenAI", "models": [ ... ] },
        { "name": "chatAnthropic", "models": [ ... ] },
        { "name": "chatGoogleGenerativeAI", "models": [ ... ] },
        { "name": "chatOpenAI", "models": [ ... ] },
        ...
    ],
    "llm": [
        { "name": "awsBedrock", "models": [ ... ] },
        { "name": "azureOpenAI", "models": [ ... ] },
        { "name": "cohere", "models": [ ... ] },
        { "name": "openAI", "models": [ ... ] },
        ...
    ],
    "embedding": [
        { "name": "openAIEmbeddings", "models": [ ... ] },
        { "name": "mistralAIEmbeddings", "models": [ ... ] },
        { "name": "voyageAIEmbeddings", "models": [ ... ] },
        { "name": "AWSBedrockEmbeddings", "models": [ ... ] },
        ...
    ]
}

Import

// Import the models registry in a Node.js/component context
const models = require('./models.json')
// Or in ES module style:
import models from './models.json'

I/O Contract

Inputs

Name	Type	Required	Description
(static JSON file)	N/A	N/A	This file is a static data source with no runtime inputs

Outputs

Name	Type	Description
chat	array	Array of chat model provider objects, each with name, models[], and optional regions[]
llm	array	Array of LLM provider objects, each with name and models[]
embedding	array	Array of embedding model provider objects, each with name and models[]
models[].label	string	Human-readable display label for the model
models[].name	string	API-compatible model identifier
models[].description	string	Optional description of the model capabilities
models[].input_cost	number	Cost per input token
models[].output_cost	number	Cost per output token

Usage Examples

Basic Usage

import models from '@/packages/components/models.json'

// Get all available chat providers
const chatProviders = models.chat.map(provider => provider.name)
// ["awsChatBedrock", "azureChatOpenAI", "chatAnthropic", ...]

// Get models for a specific provider
const anthropicModels = models.chat.find(p => p.name === 'chatAnthropic')
anthropicModels.models.forEach(m => {
    console.log(`${m.label}: input=$${m.input_cost}, output=$${m.output_cost}`)
})

// Get all embedding providers
const embeddingProviders = models.embedding.map(provider => provider.name)
// ["openAIEmbeddings", "mistralAIEmbeddings", "voyageAIEmbeddings", ...]

Related Pages

• Principle:FlowiseAI_Flowise_Model_Configuration