GenAI & LLM foundations
Popular LLMs Compared
A practical framework for choosing between LLMs — because "which is best" always depends on the job.
There is no single "best" large language model — the right choice depends on what you are optimizing for: raw reasoning quality, cost per request, response speed, context window size, or how well a model follows precise instructions.
Rather than naming a single winner, this guide gives you the dimensions that actually matter for comparing models, so you can evaluate whichever specific models are current when you are making the decision.
Key takeaways
The dimensions that actually matter
Reasoning quality matters most for complex, multi-step tasks like coding or analysis; for simple classification or extraction tasks, a much smaller, cheaper model often performs just as well. Cost and latency matter most at scale, where even small per-request savings compound quickly.
Context window size matters when tasks require processing long documents or maintaining long conversations; instruction-following consistency matters most when a task has strict formatting or compliance requirements.
Matching model size to task complexity
A common and expensive mistake is using the largest, most capable model available for every task, including simple ones that a smaller, cheaper model handles just as reliably. Reserving the most capable models for genuinely complex reasoning, and routing simpler tasks to smaller models, meaningfully reduces cost without hurting quality.
Many teams end up running several different models side by side for exactly this reason, rather than standardizing on one model for everything.
Why benchmarks are a starting point, not the answer
Public benchmarks are useful for a rough first comparison, but they rarely match your specific task and data closely enough to be the deciding factor on their own. Two models with similar benchmark scores can perform very differently on your actual prompts and data.
Running your own small evaluation set — a set of real, representative tasks scored consistently — is the most reliable way to choose between close contenders.
Put this into practice