Do you pick the best Large Language Model for your project?

When building an AI-powered solution, developers will inevitably need to choose which Large Language Model (LLM) to use. Many powerful models exist (GPT, Claude, Gemini, Llama, Mistral, Grok, DeepSeek, Qwen, etc.), and they are always changing and subject to varying levels of news and hype.

When choosing one for a project, it can be hard to know which to pick, and if you're making the right choice - being wrong could cost valuable performance and UX points.

Because different LLMs are good at different things, it's essential to test them on your specific use case to find which is the best.

Challenges in Implementing AI

• Decision fatigue - There's an overwhelming number of Language Models to choose from
• Different implementations - Not all models use the same libraries or APIs
• Tweaking parameters - To get the best result involves testing different parameters such as the temperature (creativity), token limit, reasoning effort, and more
• Cost accumulates rapidly - Costs from API calls during testing can accumulate, particularly with large prompts or frequent calls

Ultimately you need to test against different models to find one that fits your use case.

Shortlist with leaderboards, then test with your own prompts

Public leaderboards are a good way to narrow the field before you spend time and money testing:

• LMArena - Ranks models by blind, head-to-head human votes
• Artificial Analysis - Compares models on intelligence benchmarks, speed, and price

Benchmarks only tell you how a model performs on someone else's tasks. Pick 2-3 candidates, then run them against real prompts from your application to see which one actually works best.

AI Model Hubs - Experiment with different models

These platforms let you call many models from different providers through one API, so switching models is a configuration change rather than a rewrite. Most also let you test model responses interactively in a browser with configurable parameters.

OpenRouter (⭐ Recommended for Development)

• One API for hundreds of models - OpenAI, Anthropic, Google, Meta, Mistral, DeepSeek, Qwen, and more
• OpenAI-compatible - Works with the OpenAI SDK, so switching models is as simple as changing the model name
• Pay as you go - Model prices match the providers' list prices, and some models have free, rate-limited variants for experimenting
• Automatic fallbacks - Routes to another provider when one is down or rate-limited
• Side-by-side chat - Compare responses from multiple models in the browser before writing any code

Microsoft Foundry (⭐ Recommended for Production)

Microsoft Foundry (formerly Azure AI Foundry) is the enterprise option for Azure-based solutions.

• Provides access to a large catalog of models, including OpenAI, Anthropic, Meta, Mistral, and DeepSeek
• Allows side-by-side evaluation of models within the Azure ecosystem
• Covers enterprise needs like data residency, private networking, content safety, and Azure billing
• Enables fine-tuning and optimization for specific business needs

Amazon Bedrock

• The AWS equivalent of Microsoft Foundry, offering models from Anthropic, Meta, Mistral, Amazon, and more through one API
• Built-in model evaluation to compare models on your own data
• Use Amazon SageMaker AI instead only when you need to train or host your own models

Google Vertex AI Model Garden

• The Google Cloud equivalent, with Gemini models alongside open and third-party models

AI gateways

If you want one API across providers but need more control than a hosted hub, use an AI gateway:

• Vercel AI Gateway - Hosted gateway with a single key for hundreds of models, and bring-your-own-key support
• Cloudflare AI Gateway - Adds caching, rate limiting, and analytics in front of your existing provider keys
• LiteLLM - Open-source proxy you host yourself, useful when requests must stay inside your own infrastructure

Other tools to compare models

GroqCloud Playground

• Free, rate-limited API and browser tool
• Provides a low-latency inference environment for running various open models

OpenAI Playground

Browser tool which lets you test OpenAI model configurations and get associated code snippets. It has access to the latest OpenAI features first.

LM Studio and Ollama

Run open models locally. There is no cost per request, but the models you can run are limited by your hardware, and you need to download each model individually. Good for enterprise applications with high security needs, or for working offline.

Using OpenRouter as a development tool

Because OpenRouter is OpenAI-compatible, you can point the standard OpenAI SDK at it and compare models by changing a single string:

import os

from openai import OpenAI

client = OpenAI(
    base_url="https://openrouter.ai/api/v1",
    api_key=os.environ["OPENROUTER_API_KEY"],
)

for model in ["openai/gpt-5-mini", "anthropic/claude-haiku-4.5", "google/gemini-3.8-flash"]:
    response = client.chat.completions.create(
        model=model,
        messages=[{"role": "user", "content": "Summarize this support ticket: ..."}],
    )
    print(model, response.choices[0].message.content)

For example, you may be building a chatbot and find that a small, cheap model provides suitable responses, so you don't need to pay for a larger model.

Deploying to production

Once you've identified the best model for your needs:

1. Choose where to host it - Use the hub your client already has an agreement with (e.g. Microsoft Foundry or Amazon Bedrock) for enterprise requirements like data residency and billing, or keep using OpenRouter if it meets your security requirements
2. Keep your code provider-agnostic - Use an OpenAI-compatible API or an abstraction like Microsoft Agent Framework so switching models later stays cheap
3. Re-test regularly - New models are released every few months, and a newer, cheaper model might beat the one you picked

This approach allows you to make an informed decision before committing financially, ensuring you're using the right AI model for your application.