Build your own AI model tier list
Model Tier List
A personal model tier list should show what I would actually choose, the work I would choose it for, and why. Benchmark results are supporting context, not the rule that assigns the tier.
The editor is available now with an unranked roster. Choose Make your own to create your list. My first personal ranking is still being prepared.
Latest roster update
Checked 14 October 2026. The shared roster adds Claude Haiku 5.5, Muse Wonder and Wonder Full, downloadable Kolibri-1, and the latest image models. Gemini 4 Argon and Muse Spark 1.3 max now have public hosted access.
Mistral Large 4 remains a public API preview with weights pending. Reflection Beam remains selective early access with weights planned for later in October. Ling 3.1 Flash remains hosted, and its free promotion ended on 13 October.
General and coding reference evidence was captured again on 14 October. New entries start unranked. A blank score means no comparable result was captured for that model or modality. Open a card for access details and sources, or use Floor / Ceiling to compare the measured cohorts.
Recommendations with reasons
S means first choice, A strong choice, B situational, C limited use and D not recommended. Unranked means I have not recorded a recommendation. A model can be a strong choice for writing without being my first choice for coding; the note beside its placement should explain that distinction.
A history that stays put
Every published edition keeps its own placements and reasons. Selecting an older date shows that edition as it was saved. New model releases and benchmark updates do not rewrite my old recommendations. The first personal edition is being prepared; there is no invented historical ranking.
Make a separate list
Make your own copies the selected edition into a private browser draft. Change tiers, add reasons and save a dated snapshot. Export the JSON to back it up or import it on another device. Saving in your browser does not publish a change to my list.
Related reading
- Harness Tier List covers the tools around the model.
- Astra for Writing, Fable for Coding explains task-specific preferences.
- Floor / Ceiling provides a separate benchmark comparison.