How to Choose the Right LLM for Your Project

Why Choosing the Right LLM Matters

The LLM landscape has exploded. With dozens of models from OpenAI, Anthropic, Google, Meta, and others, picking the right one for your project can feel overwhelming. Price differences can be 100x between the cheapest and most expensive options — and the most expensive isn't always the best.

The Three Dimensions That Matter

Every LLM decision comes down to a tradeoff between three factors:

1. Quality

Quality encompasses how well a model understands instructions, reasons through complex problems, and generates accurate outputs. Key benchmarks include:

• MMLU Pro — broad knowledge and reasoning
• GPQA — graduate-level science questions
• LiveCodeBench — real-world coding ability
• MATH-500 — mathematical problem solving

Our Quality Index combines these into a single 0-100 score for easy comparison.

2. Speed

For real-time applications like chatbots, speed is critical. Two metrics matter:

• Output speed (tokens/second) — how fast the model generates text
• Time to First Token (TTFT) — how long before the first word appears

A model generating 300 tok/s feels instant. One at 30 tok/s feels sluggish for interactive use.

3. Cost

LLM pricing is typically per-million tokens, split between input and output. The blended price (weighted average assuming 3:1 input-to-output ratio) gives you a practical comparison point.

Budget models like Gemini 2.0 Flash cost under $0.20/1M tokens. Premium reasoning models like Claude Opus 4 can exceed $30/1M.

Decision Framework

Here's a simple framework based on common use cases:

Try It Yourself

Use our LLM Selector tool to get personalized recommendations based on your specific requirements, or dive into the Explore page to compare models interactively.