Introducing Model Router: iterate quickly with seamless access to models

Frontier and specialized models are advancing every day. Developers now have an unprecedented array of models to choose from, each optimized for different tasks, budgets, and performance requirements based on the specific use case. But integrating multiple models into an app remains unnecessarily complex—until now.

Today, we’re excited to introduce Model Router, a powerful new feature in Hypermode that enables developers to connect to both open-source and commercial language models through a single, unified API. Model Router lets you use the SDKs and tools you already know and love, while giving you full flexibility to switch between different models—all while maintaining a developer-first, composable experience.

Growing use cases accelerate model proliferation

As AI capabilities expand, so do the options for developers. With open-source models like LLaMA and DeepSeek, and competing with commercial offerings like OpenAI’s GPT, Anthropic’s Claude, and Google’s Gemini, developers often find themselves juggling multiple APIs, SDKs, and pricing structures.

That complexity isn’t limited to reasoning models. Embedding models, which power vector search, retrieval-augmented generation (RAG), and recommendation systems, also come with significant fragmentation. But these models aren’t interchangeable: they produce different vector representations, making it difficult to switch between providers without retraining or recalibrating downstream systems.

Each provider comes with its own integration requirements, hidden switching costs, and operational trade-offs, making it difficult to easily experiment with different models, optimize for cost, or dynamically route requests based on performance without managing a series of subscriptions.

However, with the adoption of the OpenAI-compatible API standard by model providers, developers can use familiar SDKs with entirely different backends to access model inputs and outputs. This open schema is the first step in managing the fragmentation and rapid proliferation of models, which often results in increased development complexity, higher maintenance costs, and limited agility in adapting to the latest breakthroughs in AI research.

Introducing Model Router

Model Router eliminates these pain points by providing a single API that connects to a diverse set of AI models. Instead of managing multiple API integrations, developers can use Model Router to:

• Seamlessly switch between open-source and commercial models based on performance, pricing, or availability.
• Optimize costs by routing requests to the most cost-effective models for a given task.
• Enhance reliability with automatic fallback mechanisms in case of API outages or rate limits.
• Experiment and iterate faster by dynamically selecting different models without rewriting application logic.

With Model Router, you get the flexibility to choose the best AI model for the job—whether it’s a powerful commercial model for production workloads or an open-source model for cost-efficient experimentation.

Key features include:

• Unified API access: One integration point for multiple AI models, reducing development overhead.
• Fallback & load balancing: Improve reliability by distributing requests across multiple models and providers.
• Customizable model preferences: Prioritize specific models or providers based on business needs.
• Cost & performance optimization: Select models dynamically to optimize for both budget and response time.
• Security & compliance: Maintain control over data privacy and compliance by choosing region-specific models when necessary.
• Developer agility: Directly access Model Router through the OpenAI SDK, Vercel AI SDK, and Modus SDK for fine-grained control and iteration.

Why Model Router matters

While model innovation is accelerating, swapping one model for another isn’t as simple as updating an API key. In reality, developers face hidden costs: differences in tokenization, output structures, system prompt behavior, and embedding representations often require significant downstream adjustments. Migrating between models without an orchestration layer can introduce quality regressions, additional maintenance burden, and slow down iteration velocity.

Though models may never be completely interchangeable, the ability to seamlessly integrate multiple models will be a competitive advantage. Model Router allows for fast iteration by providing a unified API to simplify development—allowing you to test, tune, and deploy across models without incurring hidden switching costs. It abstracts away low-level differences while giving organizations full control over settings and behavior.

By providing a single API for a diverse AI ecosystem, Model Router unlocks new possibilities for developers and enterprises alike. Instead of being locked into a single provider, you can harness the full spectrum of AI innovation while maintaining flexibility and control over your infrastructure.

Get started

It is easy to get started with Model Router. In the Hypermode Console, generate an API key and drop it into your favorite framework. Check out the Hypermode Docs for additional details.

For example, in the Vercel AI SDK, you can call a hosted model.

import { createOpenAI } from '@ai-sdk/openai'
import { generateText } from 'ai'

// Configure with your Hypermode Workspace API key and Hypermode Model Router base url
const hypermode = createOpenAI({
  name: 'hypermode',
  apiKey: '<YOUR_HYP_WKS_KEY>',
  baseURL: 'https://models.hypermode.host/v1',
})

async function generateHoliday() {
  try {
    const { text, usage, providerMetadata } = await generateText({
      model: hypermode('o3-mini'),
      prompt: 'Invent a new holiday and describe its traditions.',
      providerOptions: { hypermode: { reasoningEffort: 'low' } },
    })

    // Print the response
    console.log(text)
    return { text, usage, providerMetadata }
  } catch (error) {
    console.error('Error generating text:', error)
    throw error
  }
}

// Call the function
generateHoliday()

You can also access the API directly.

import requests
import json

# Your Hypermode Workspace API key
api_key = "<YOUR_HYP_WKS_KEY>"

# Use the Hypermode Model Router base url
base_url = "https://models.hypermode.host/v1"

# API endpoint
endpoint = f"{base_url}/chat/completions"

# Headers
headers = {"Content-Type": "application/json", "Authorization": f"Bearer {api_key}"}

# Request payload
payload = {
    "model": "meta-llama/llama-4-scout-17b-16e-instruct",
    "messages": [
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "What is Dgraph?"},
    ],
    "max_tokens": 150,
    "temperature": 0.7,
}

# Make the API request
response = requests.post(endpoint, headers=headers, data=json.dumps(payload))

# Check if the request was successful
if response.status_code == 200:
    # Parse and print the response
    response_data = response.json()
    print(response_data["choices"][0]["message"]["content"])
else:
    # Print error information
    print(f"Error: {response.status_code}")
    print(response.text)

The future of AI is open, flexible, and interoperable

Model Router is now available in Hypermode, bringing the next level of AI model orchestration to developers worldwide. Whether you’re an enterprise looking to optimize AI costs or a startup experimenting with different models, Model Router empowers you to build smarter, more adaptable AI applications.

Ready to experience seamless AI model integration? Sign up for Hypermode today and start building with Model Router. Stay tuned for more updates as we continue to expand our support for new models and features.