LamBench
120 λ-calculus challenges that cut through AI benchmark gaming
The Panel's Take
LamBench is a benchmark of 120 fresh lambda calculus programming questions designed by Victor Taelin (creator of the HVM runtime) to test genuine AI reasoning capabilities rather than pattern-matched performance on contaminated datasets. Questions range from implementing basic operations like addition for λ-encoded natural numbers to deriving generic folds for arbitrary data types. The benchmark measures both accuracy (percentage of 120 tasks solved correctly) and speed (average solution time). Current top performers include GPT-5.4 at 91.7% accuracy, Anthropic's Opus 4.6 at 90.0%, and GPT-5.3-Codex at 89.2%. Lower-tier models bottom out at 28-58% accuracy — revealing significant gaps in symbolic reasoning capability that other benchmarks obscure. Taelin released LamBench in direct response to community requests for a benchmark resistant to training data contamination. Lambda calculus is a clean, closed formal system — ideal for testing reasoning because memorizing examples provides minimal advantage over actually understanding the abstractions.
Share this verdict
LamBench verdict: SKIP ⏭️ 2 ships · 2 skips from the expert panel Full review: shiporskip.io/tool/lambench-lambda-calculus-benchmark-ai-models-120-questions-2026
Weekly AI Tool Verdicts
Get the next verdict in your inbox
7 critics review a new AI tool every day. Weekly digest — free.
Embed this verdict
Tool makers can add a live ShipOrSkip badge to their site. Badge loads track impressions; clicks route back to this review.
<a href="https://shiporskip.io/api/badge-click/lambench-lambda-calculus-benchmark-ai-models-120-questions-2026" target="_blank" rel="noopener"><img src="https://shiporskip.io/api/badge/lambench-lambda-calculus-benchmark-ai-models-120-questions-2026" alt="LamBench Skip verdict on ShipOrSkip" width="360" height="90" /></a>[](https://shiporskip.io/api/badge-click/lambench-lambda-calculus-benchmark-ai-models-120-questions-2026)<iframe src="https://shiporskip.io/embed/lambench-lambda-calculus-benchmark-ai-models-120-questions-2026" title="LamBench ShipOrSkip verdict" width="360" height="260" style="border:0;border-radius:16px;max-width:100%;" loading="lazy"></iframe>The reviews
“Lambda calculus is a great choice for a hard-to-contaminate benchmark — you can't just memorize your way to success on symbolic reasoning. The gap between top models (90%+) and mid-tier (50-60%) is much larger than most leaderboards show, which gives it real signal.”
“120 questions is a very small sample size for a benchmark claiming to measure fundamental reasoning — statistical noise could easily explain a 5-10% difference between models. And lambda calculus is a narrow domain; strong performance here doesn't generalize to most real tasks.”
“As LLMs saturate mainstream benchmarks, we'll rely increasingly on formal, symbolic tasks to measure genuine reasoning progress. LamBench points toward a class of evaluation that correlates with the kind of compositional thinking needed for real AGI-level capabilities.”
“Lambda calculus reasoning benchmarks are fascinating from a research perspective but have zero direct connection to creative workflows. The leaderboard is worth bookmarking to track which models are actually getting smarter vs. just getting better at gaming evals.”