Benchmark Almanac

Sudoku-Bench

Models solve Sudoku variants with unusual rule sets that need creative, multi-step deduction. Scored by the share of puzzles solved.

In the authors’ words

Existing reasoning benchmarks for large language models (LLMs) frequently fail to capture authentic creativity, often rewarding memorization of previously observed patterns. We address this shortcoming with Sudoku-Bench, a curated benchmark of challenging and unconventional Sudoku variants specifically selected to evaluate creative, multi-step logical reasoning. Sudoku variants form an unusually effective domain for reasoning research: each puzzle introduces unique or subtly interacting constraints, making memorization infeasible and requiring solvers to identify novel logical breakthroughs (“break-ins”). Despite their diversity, Sudoku variants maintain a common and compact structure, enabling clear and consistent evaluation. Sudoku-Bench includes a carefully chosen puzzle set, a standardized text-based puzzle representation, and flexible tools compatible with thousands of publicly available puzzles – making it easy to extend into a general research environment. Baseline experiments show that state-of-the-art LLMs solve fewer than 15% of puzzles unaided, highlighting significant opportunities to advance long-horizon, strategic reasoning capabilities.

Abstract of Sudoku-Bench: Evaluating creative reasoning with Sudoku variants (arXiv:2505.16135, CC0)
Made by Sakana AI Released May 2025 Abstract puzzles and logic All tasks public Last in a launch post Sep 2025 , dropped before it saturated Last model added Sep 2025
Level
Competition The strongest students, in olympiad-style contests
Scored
Checked automatically A program checks the result against an answer key, unit tests or the expected final state.
Answers
Short answer A number, word or short string matched to a key.
1
Model scored in lab cards
1
Launch post lists it, from 1 lab
1
System card reports it, from 1 lab

LongCat-Flash-Thinking leads with 56%

Best score per model as labs report them in their own cards. Labs pick their own settings, so these compare less cleanly than independent runs.

  1. 1 LongCat-Flash-Thinking 56%
#ModelScoreSettingDateSource
1 LongCat-Flash-Thinking 56% Sep 2025 Model card

Scores over time

All the scores we have, one dot each, so a model tested at several settings appears more than once.

Reported by the lab in its model card
0% 20% 40% 60% 80% 100% Near the ceiling (85%) LongCat-Flash-Thinking 0% 20% 40% 60% 80% 100% Near the ceiling (85%) LongCat-Flash-Thinking

No launch post has listed it since Sep 2025

Launch posts from every lab that has listed it, one tick per post. Tall colored ticks include it, and short gray ones leave it out.

Meituan
DateLaunch postScoreAs the post writes it
21 Sep 2025 LongCat-Flash-Thinking 56 Sudoku-Bench(Mean@1)

One system card reports it

Besides launch posts, we read the system cards, model cards and technical reports labs publish with a release. They report far more, including safety and frontier-risk tests.

DateDocumentScoreAs the document writes itSection
23 Sep 2025 LongCat-Flash-Thinking technical report 56 Sudoku-Bench(Mean@1), nikoli_100 set (Table 2) Capabilities

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