How to Use Multiple AI Models in One Application

Strategies for routing, blending, and orchestrating multiple LLMs to get better results than any single model alone.

Identify which use cases benefit from each model

Map your product's AI features to model strengths. GPT-5.2 excels at structured reasoning and code, Claude Sonnet 4.5 handles nuanced writing and long-context analysis, and Gemini 3 Flash delivers fast, cost-efficient responses. Not every feature needs the most expensive model, and some benefit from multiple models working together.

Create routing rules

Define rules that direct each request to the best model based on task type, latency requirements, or cost budget. Simple regex-based classifiers work well for clear categories like code versus prose. LLMWise Auto mode does this automatically with a zero-latency heuristic router that classifies queries and selects the optimal model.

Implement model selection logic

Build a routing layer in your backend that inspects incoming requests and forwards them to the appropriate model. If you use LLMWise, this is a single API call with the model set to Auto, or you can specify exact models per request. The OpenAI-compatible endpoint means no SDK changes regardless of which model handles the request.

Monitor per-model performance

Track latency, error rate, cost, and output quality for each model independently. Look for drift over time: a model that was fastest last month may have slowed after a provider update. LLMWise logs every request with model, latency, token count, and cost, giving you a built-in observability layer.

Optimize model allocation over time

Review performance data weekly and adjust routing. Promote models that over-perform on certain tasks and demote ones that under-deliver. LLMWise Optimization policies automate this loop by analyzing your request history and recommending primary and fallback model chains based on your chosen goal: balanced, lowest cost, lowest latency, or highest reliability.

Key takeaways

✓No single model is best at everything: multi-model routing matches each task to the model that handles it best.

✓LLMWise Auto mode provides zero-latency heuristic routing across nine models with no configuration required.

✓Continuous optimization based on real usage data keeps your model allocation aligned with changing performance and pricing.

Common questions

Is it complicated to manage multiple AI models?

It can be if you integrate each provider separately. A unified platform like LLMWise abstracts the complexity: you send requests to one endpoint and the platform handles routing, failover, and monitoring across all nine models.

What is the difference between routing and orchestration?

Routing sends each request to one model. Orchestration goes further by combining models: comparing outputs side by side, blending responses from multiple models, or having one model judge another's output. LLMWise supports all five orchestration modes through a single API.

Try it yourself

500 free credits. One API key. Nine models. No credit card required.

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