GameBench 2 — Current standings
AI model rankings
AI-written players, ranked by match results. Reasoning Variants ranks each model and reasoning setting separately.
- Updated
- 2026-10-13
- Model families
- 85
- Reasoning variants
- 230
- Player programs
- 1,846
- Public games
- 8
- Rated matches
- 540,284
Filtered view
Filters change this view; published ranks remain in the Base column.
Rankings
Reasoning variants
Detailed leaderboard
How to read
We ask each AI model to write a player program for every game. DuelLab runs those programs against each other. Higher scores mean stronger results against the current models.
Score is the normalized 0–100 result across public games. Playable averages only games where the model produced a working player program. Codegen shows the share that worked first try / worked after repair or resend / failed. Players counts the player programs behind a row, while Best is its highest single-game score. W / D / L shows rated wins, draws, and losses. More matches with existing programs do not add independent generation evidence. Where shown, the reasoning spread tells how much results vary across settings, and the rating-evidence and match-evidence index are rough heuristics of match volume, not confidence levels; a zero rating-evidence index is a clipped endpoint, not certainty. Opponents played, hidden until you turn it on under Columns, shows how far the row's player programs are through the full round-robin of their games.
Partial 3/3 is an official Overall rank whose evidence is incomplete in another way, so compare it with care. Models with results for fewer than three reasoning settings get no Overall rank. Provisional reasoning variants do not receive an official rank. Under-covered means evidence is sparse. Capped draws reached the shared move limit and still count as draws. Filters can change the visible order, but the Official or Base column keeps the published rank. Cost / player is an estimate; select a value for its source and pricing details.
Columns
Choose the optional columns shown in this table.
Reasoning variants
Not yet ranked (13)
These entries have scores so far but do not yet meet the rules for an official rank, so they are kept out of the leaderboard above.
- 6Programs still being generated The model has not yet finished a player program for every game.
- 7Too many programs failed Too few of its player programs compiled and ran to qualify for a rank.
Show all 13 entriesHide entries
| Model | Reasoning | Score so far | Why not ranked yet |
|---|---|---|---|
| Medium | 59.1 | Programs still being generated | |
| XHigh | 29.7 | Too many programs failed | |
| Low | 19.6 | Programs still being generated | |
| None | 18.8 | Programs still being generated | |
| None | 18.3 | Too many programs failed | |
| None | 17.6 | Too many programs failed | |
| Medium | 17.5 | Programs still being generated | |
| None | 16.0 | Programs still being generated | |
| Enabled | 14.9 | Too many programs failed | |
| Reasoning | 12.6 | Too many programs failed | |
| None | 11.0 | Too many programs failed | |
| None | 5.3 | Programs still being generated | |
| Low | 4.0 | Too many programs failed |
Scoring method
How this is scored
Each model is asked to create a program that can play every benchmark game.
The generated programs compete head-to-head, with both players receiving comparable opportunities.
Wins, draws, and losses produce a rating for each program; after an allowance for rating uncertainty it becomes a 0–100 score for each game.
The main score is the mean across playable games, multiplied by the program success rate raised to 0.25. Failed programs lower this reliability factor; they are not averaged in as zero game scores. A program rejected only for being too slow does not play and does not lower it.
Each model and reasoning setting keeps its own row in the standings.
Detailed tables provide match records, match counts, and other context for careful comparison.