Benchmarks

AI model benchmarks explained.

Benchmarks are useful maps, not final truth. They compress many tasks into scores, but your workflow may care about different things.

TestSame prompt

CheckFiles, sources, privacy

CompareSide by side

Guide

What to test before choosing.

These notes avoid fragile plan details and focus on durable buying criteria: workflow fit, output quality, verification effort and risk.

Useful for scientific and logic-heavy tasks, but they may not reflect everyday business writing.

Useful when they test real issues, but local repo context and tests still decide production quality.

Arena-style voting shows what people prefer, not necessarily what is factual or compliant.

Useful for image and document understanding, but real file workflows add more failure points.

Useful for API buyers, less useful for users buying a chat subscription.

Use benchmarks to shortlist models, then test your own prompts and files.

Practical test

Use the same prompt, same file and same scoring rule. Compare the answer you would actually send, publish, present or commit.

What to score	Good answer	Warning sign
Clarity	Easy to understand and structured for the audience.	Sounds smart but hides the actual answer.
Accuracy	Separates facts, assumptions and uncertain claims.	Confident claims without support.
Usability	Needs little editing before real use.	Requires a full rewrite or misses the task.
Risk	Flags privacy, legal, medical, financial or source issues.	Encourages blind trust in the output.

Models to compare

Related guides

Broader guide for people comparing alternatives to ChatGPT.

Search-intent guide for users looking for tools similar to ChatGPT.

Dedicated deep-dive for ChatGPT and Gemini.

Dedicated deep-dive for ChatGPT and Claude.