Rankings come from what real users say about AI models on public forums, not from benchmarks. Refreshed every month.

How we rank AI models

Our rankings summarize what people say after using AI models for real work. Here is exactly how a ranking is made, and where it can go wrong.

1. Collect public comments

Each month we collect public Reddit comments from 17 communities where people discuss AI models, including r/LocalLLaMA, r/ClaudeAI, r/GeminiAI, r/OpenAI, r/ChatGPT, r/singularity, r/SillyTavernAI, r/StableDiffusion, r/WritingWithAI and r/translator. The October 2026 edition covers Aug 29, 2026 to Oct 28, 2026.

2. Keep only real opinions about a specific model and task

We match exact model names and versions (for example "Opus 5.5" or "Gemma 4", not just "Claude" or "Gemini"), then keep comments that give an opinion. A person reads each candidate and decides which task it is about and whether the comment is positive, mixed or negative about each model it names. News, questions, jokes and benchmark screenshots are skipped. Bots are excluded, and each author counts at most three times per task.

The everyday assistant page is the exception: it ranks the apps (ChatGPT, Claude, Gemini and so on), so plain product names count there.

3. Score

The score is the share of positive opinions, counting mixed as half, with five neutral votes added so a model with three positive comments does not outrank one with forty. A model needs at least 6 opinions on a task to be ranked. Below that it appears under "early reads".

Score = (positive + ½ × mixed + 2.5) ÷ (opinions + 5) × 100

4. Write it up and link the sources

We summarize what people liked and disliked in our own words and link to the original comments so you can check them yourself.

What this can't tell you

Found a comment we got wrong? Tell us and we'll fix it in the next edition.