Which LLM Should I Use?
Answer 6 questions about your use case, traffic volume, and budget. We'll recommend the best model with a score breakdown and cost estimate — in under 60 seconds.
What are you building?
How to Choose the Right LLM — Framework
Choosing an LLM is not just about picking the most capable model — it is about matching capability, cost, and latency to your specific workload. The right model for a high-volume customer service bot is rarely the right model for a complex research agent. Here is the framework we use to score each model:
Use-case alignment. Models specialize. Claude Sonnet and GPT-4.1 excel at coding and instruction-following. Claude Fable 5 and o3 are built for multi-step reasoning. Gemini 2.5 Flash is optimal for large-document RAG with its 1M context window. Using a reasoning model for simple FAQ responses is like using a sledgehammer for a finishing nail — you pay 10× more for no quality gain.
Volume × price = monthly bill. The most common mistake teams make is evaluating models in isolation rather than at their actual request volume. A $0.30/M difference in input pricing sounds trivial — until you are processing 500M tokens per month, where it becomes $150/month. Run the numbers at your real scale before committing to a model.
Context window as architecture decision. If your workflow requires processing 100,000-token documents, you need a model with a large context window — period. Gemini 2.5 Pro's 1M context eliminates the need for complex chunking strategies that add latency, engineering complexity, and cost. Conversely, if your prompts are small, paying for a large context window you never use is wasted budget.
Start cheap, upgrade selectively. Most teams get better ROI by starting with the cheapest model that meets their baseline quality bar, then upgrading specific requests to a premium model when needed. A routing layer that sends 80% of requests to GPT-4o Mini and 20% to Claude Sonnet typically outperforms using Claude Sonnet for everything — at 60–70% lower cost.