How to Choose the Right LLM

Bottom line: The best LLM for your project is the cheapest model that fits your context, supports the capabilities you need, and produces outputs accurate enough for your users. Start by eliminating models that fail hard requirements, then optimize for cost and speed.

1. Define your hard requirements

Before comparing benchmarks, list the things a model must have. Common hard requirements include:

• Context window: the longest document or conversation you need to process.
• Capabilities: vision, function calling, JSON mode, streaming, tool use.
• Compliance: data residency, HIPAA, SOC 2, or self-hosting requirements.
• Latency: maximum acceptable time to first token for real-time features.

Any model that does not meet every hard requirement is out. Use the Context Window Comparison tool to filter by minimum context size and required capabilities.

2. Estimate your cost ceiling

Pricing varies by orders of magnitude. A model that costs $30 per million output tokens is fine for low-volume internal tools but can break the budget at high scale. Estimate your monthly token volume with the LLM Cost Calculator and compare candidates side-by-side.

Do not forget output tokens. Some models are cheap on input but expensive on generation, which matters for long-form writing, coding, or multi-step agents.

3. Match the model to the task

4. Evaluate quality on your data

Public benchmarks are a starting point, but your data is what matters. Build a small evaluation set of real inputs and golden answers for your use case. Score each candidate on:

• Accuracy: does the output match the expected answer?
• Format adherence: does it follow JSON schemas or structured output requirements?
• Hallucination rate: does it invent facts or citations?
• Latency: is the response fast enough for your UX?

5. Plan for fallback and redundancy

Models change, rate limits happen, and providers have outages. Design your system so you can swap models or fall back to an alternative. OpenRouter and similar routing layers make this easier by exposing many providers behind one API.

Quick decision framework

1. List hard requirements (context, capabilities, compliance, latency).
2. Filter models that fail any hard requirement.
3. Estimate monthly cost for the remaining candidates.
4. Benchmark the top 2–3 on your own data.
5. Pick the cheapest model that meets your quality bar, with a fallback ready.

Tools to help you decide

• Model Compare - side-by-side pricing and capabilities
• Context Window Comparison - find models that fit your documents
• LLM Cost Calculator - estimate monthly spend
• LLM Tokenizer - count tokens in your inputs

Published 2026-06-12