How to Choose the Right LLM for Your Project
A practical guide to evaluating LLMs by quality, speed, cost, and use case — with key metrics that actually matter.
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.
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