Small vs Large AI Models: Bigger Isn't Always Better

Models are often described by parameters, the adjustable values learned in training. More parameters usually means more capability, but not always more value.

Strengths

• Stronger at complex reasoning, nuanced writing and broad knowledge.
• Better at unfamiliar or multi-step tasks.

Weaknesses

• Slower and more expensive to run.
• Usually need cloud infrastructure.
• Higher energy use.

Strengths

• Fast and cheap, suitable for high-volume tasks.
• Run on laptops, phones and edge devices.
• Private: data can stay on the device.
• Easier to fine-tune for a narrow job.

Weaknesses

• Less general knowledge and weaker complex reasoning.
• More prone to errors on hard problems.

• Classification and tagging.
• Extracting fields from documents.
• Short summaries and rewrites.
• Autocomplete and on-device assistants.
• Routing requests to the right team or tool.
• Speech-to-text and text-to-speech.

• Complex analysis and planning.
• Long, nuanced writing.
• Difficult code and debugging.
• Open-ended research and brainstorming.

1. Send simple, high-volume tasks to a small, cheap model.
2. Escalate hard or high-stakes requests to a larger model.
3. Test both on your own examples, and measure quality, speed and cost.
4. Re-test when new versions appear. Small models improve quickly.

Choose the smallest model that meets your quality bar. You'll save money, time and energy.