BytePlus ModelArk

Portkey provides a robust and secure gateway to integrate various Large Language Models (LLMs) into applications, including BytePlus ModelArk models. With Portkey, take advantage of features like fast AI gateway access, observability, prompt management, and more, while securely managing API keys through Model Catalog.

Provider Slug: byteplus

Quick Start

Get BytePlus ModelArk working in 3 steps:

from portkey_ai import Portkey

# 1. Install: pip install portkey-ai
# 2. Add @byteplus provider in model catalog
# 3. Use it:

portkey = Portkey(api_key="PORTKEY_API_KEY")

response = portkey.chat.completions.create(
    model="@byteplus/ep-m-your-endpoint-id",
    messages=[{"role": "user", "content": "Say this is a test"}]
)

print(response.choices[0].message.content)

import Portkey from 'portkey-ai'

// 1. Install: npm install portkey-ai
// 2. Add @byteplus provider in model catalog
// 3. Use it:

const portkey = new Portkey({
    apiKey: "PORTKEY_API_KEY"
})

const response = await portkey.chat.completions.create({
    model: "@byteplus/ep-m-your-endpoint-id",
    messages: [{ role: "user", content: "Say this is a test" }]
})

console.log(response.choices[0].message.content)

from openai import OpenAI
from portkey_ai import PORTKEY_GATEWAY_URL

# 1. Install: pip install openai portkey-ai
# 2. Add @byteplus provider in model catalog
# 3. Use it:

client = OpenAI(
    api_key="PORTKEY_API_KEY",  # Portkey API key
    base_url=PORTKEY_GATEWAY_URL
)

response = client.chat.completions.create(
    model="@byteplus/ep-m-your-endpoint-id",
    messages=[{"role": "user", "content": "Say this is a test"}]
)

print(response.choices[0].message.content)

import OpenAI from "openai"
import { PORTKEY_GATEWAY_URL } from "portkey-ai"

// 1. Install: npm install openai portkey-ai
// 2. Add @byteplus provider in model catalog
// 3. Use it:

const client = new OpenAI({
    apiKey: "PORTKEY_API_KEY",  // Portkey API key
    baseURL: PORTKEY_GATEWAY_URL
})

const response = await client.chat.completions.create({
    model: "@byteplus/ep-m-your-endpoint-id",
    messages: [{ role: "user", content: "Say this is a test" }]
})

console.log(response.choices[0].message.content)

# 1. Add @byteplus provider in model catalog
# 2. Use it:

curl https://api.portkey.ai/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "x-portkey-api-key: $PORTKEY_API_KEY" \
  -d '{
    "model": "@byteplus/ep-m-your-endpoint-id",
    "messages": [
      { "role": "user", "content": "Say this is a test" }
    ]
  }'

Tip: You can also set provider="@byteplus" in Portkey() and use just model="ep-m-your-endpoint-id" in the request.

Add Provider in Model Catalog

Go to Model Catalog → Add Provider
Select BytePlus
Choose existing credentials or create new by entering your BytePlus API key
Name your provider (e.g., byteplus-prod)

Configuration Parameters

Parameter	Description	Required
`apiKey`	BytePlus ModelArk API key	Yes
`byteplusRegion`	Region for API requests (`ap-southeast-1` or `eu-west-1`)	No (defaults to `ap-southeast-1`)

When using headers directly, pass the region via the x-portkey-byteplus-region header.

Complete Setup Guide →

See all setup options, code examples, and detailed instructions

Supported Endpoints

Endpoint	Support
`/chat/completions`	Supported
`/embeddings`	Supported (multimodal)
`/images/generations`	Supported
`/responses`	Supported

Supported Features

Feature	Support
Thinking/Reasoning	Supported via `thinking` parameter
Streaming	Supported

Embeddings

Generate multimodal embeddings using BytePlus ModelArk:

from portkey_ai import Portkey

portkey = Portkey(api_key="PORTKEY_API_KEY")

response = portkey.embeddings.create(
    model="@byteplus/ep-m-your-embedding-endpoint",
    input="The quick brown fox jumps over the lazy dog"
)

print(response.data[0].embedding[:5])

import Portkey from 'portkey-ai'

const portkey = new Portkey({
    apiKey: "PORTKEY_API_KEY"
})

const response = await portkey.embeddings.create({
    model: "@byteplus/ep-m-your-embedding-endpoint",
    input: "The quick brown fox jumps over the lazy dog"
})

console.log(response.data[0].embedding.slice(0, 5))

curl https://api.portkey.ai/v1/embeddings \
  -H "Content-Type: application/json" \
  -H "x-portkey-api-key: $PORTKEY_API_KEY" \
  -d '{
    "model": "@byteplus/ep-m-your-embedding-endpoint",
    "input": "The quick brown fox jumps over the lazy dog"
  }'

Image Generation

Generate images using BytePlus ModelArk image models:

from portkey_ai import Portkey

portkey = Portkey(api_key="PORTKEY_API_KEY")

image = portkey.images.generate(
    model="@byteplus/ep-m-your-image-endpoint",
    prompt="A serene landscape with mountains and a lake at sunset",
    size="1024x1024"
)

print(image.data[0].url)

import Portkey from 'portkey-ai'

const portkey = new Portkey({
    apiKey: "PORTKEY_API_KEY"
})

const image = await portkey.images.generate({
    model: "@byteplus/ep-m-your-image-endpoint",
    prompt: "A serene landscape with mountains and a lake at sunset",
    size: "1024x1024"
})

console.log(image.data[0].url)

curl https://api.portkey.ai/v1/images/generations \
  -H "Content-Type: application/json" \
  -H "x-portkey-api-key: $PORTKEY_API_KEY" \
  -d '{
    "model": "@byteplus/ep-m-your-image-endpoint",
    "prompt": "A serene landscape with mountains and a lake at sunset",
    "size": "1024x1024"
  }'

Additional Image Generation Parameters

BytePlus supports additional parameters for image generation:

Parameter	Description
`negative_prompt`	Text describing what to exclude from the image
`guidance_scale`	How closely to follow the prompt (1-10)
`seed`	Seed for reproducible generation (-1 to 2147483647)
`width` / `height`	Image dimensions
`watermark`	Add watermark to generated images

Responses API

Use the OpenAI Responses API format with BytePlus models:

from portkey_ai import Portkey

portkey = Portkey(api_key="PORTKEY_API_KEY")

response = portkey.responses.create(
    model="@byteplus/ep-m-your-endpoint-id",
    input="Tell me a three sentence bedtime story about a unicorn."
)

print(response)

import Portkey from 'portkey-ai'

const portkey = new Portkey({
    apiKey: "PORTKEY_API_KEY"
})

const response = await portkey.responses.create({
    model: "@byteplus/ep-m-your-endpoint-id",
    input: "Tell me a three sentence bedtime story about a unicorn."
})

console.log(response)

curl https://api.portkey.ai/v1/responses \
  -H "Content-Type: application/json" \
  -H "x-portkey-api-key: $PORTKEY_API_KEY" \
  -d '{
    "model": "@byteplus/ep-m-your-endpoint-id",
    "input": "Tell me a three sentence bedtime story about a unicorn."
  }'

Responses API reference

Full Responses API docs — streaming, tools, instructions, and more

Regions

BytePlus ModelArk is available in multiple regions. Configure the region when setting up your provider:

Region	Base URL
`ap-southeast-1` (default)	`https://ark.ap-southeast.bytepluses.com/api/v3`
`eu-west-1`	`https://ark.eu-west.bytepluses.com/api/v3`

Supported Models

BytePlus ModelArk provides access to various foundation models. Use either:

Model ID (preset endpoints): ep-m-xxx format
Endpoint ID (custom endpoints): ep-xxx format

For the complete list of available models, refer to the BytePlus ModelArk documentation.

Next Steps

Add Metadata

Add metadata to your BytePlus requests

Gateway Configs

Add gateway configs to your BytePlus requests

Tracing

Trace your BytePlus requests

Fallbacks

Setup fallback strategies with BytePlus

For complete SDK documentation:

SDK Reference

Complete Portkey SDK documentation

Ecosystem

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Tracing Providers

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Quick Start

Add Provider in Model Catalog

Configuration Parameters

Complete Setup Guide →

Supported Endpoints

Supported Features

Embeddings

Image Generation

Additional Image Generation Parameters

Responses API

Responses API reference

Regions

Supported Models

Next Steps

Add Metadata

Gateway Configs

Tracing

Fallbacks

SDK Reference

​Quick Start

​Add Provider in Model Catalog

​Configuration Parameters

Complete Setup Guide →

​Supported Endpoints

​Supported Features

​Embeddings

​Image Generation

​Additional Image Generation Parameters

​Responses API

Responses API reference

​Regions

​Supported Models

​Next Steps

Add Metadata

Gateway Configs

Tracing

Fallbacks

SDK Reference

Quick Start

Add Provider in Model Catalog

Configuration Parameters

Supported Endpoints

Supported Features

Embeddings

Image Generation

Additional Image Generation Parameters

Responses API

Regions

Supported Models

Next Steps