Head-to-head
mistral-small:24b vs GPT-5.5 (Codex)
Both models answered the same one-shot tasks under identical rules — no retries, answers published unedited. Two separate axes, never blended: does the code actually run, and how do blind-scored text answers hold up against a written gold answer. Frontier references sit outside the blind field and are deliberately unscored on text.
| Model | Runnable sims | Blind text avg | Failure modes |
|---|---|---|---|
| mistral-small:24b | 33% (28/85) | 8.65 (31 scored) | ERR 26 · STAT 11 · BLANK 9 · HANG 3 · DNF 8 |
| GPT-5.5 (Codex) frontier reference | 95% (81/85) | unscored by design | ERR 2 · STAT 1 · BLANK 1 |
Biggest contrasts
Rounds where the two models diverged most — open them and read both answers side by side.
| Round | mistral-small:24b | GPT-5.5 (Codex) |
|---|---|---|
| R152 · Hash table with visible collision chains | ERR | OK |
| R151 · Binary search tree growing node by node | BLANK | OK |
| R140 · Water sloshing in a tilting container | ERR | OK |
| R139 · Small n-body system orbiting a central star | OK | ERR |
| R137 · Cloth mesh hanging and rippling in the wind | ERR | OK |
→ Interactive round-by-round comparison · → Full leaderboard
Generated 2026-07-31 from the published rounds. Harness failures are excluded from every base — a broken cluster node is our fault, not the model's.