AI tool comparison
ClawBench vs LamBench
Which one should you ship with? Here is the side-by-side panel verdict, pricing read, reviewer split, and community vote comparison.
Research
ClawBench
153 real-world browser tasks, live websites — best AI agent scores only 33%
75%
Panel ship
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Community
Free
Entry
ClawBench is a browser agent evaluation framework built around 153 real-world tasks running on 144 live production websites — not simulated environments or curated sandboxes. Tasks span e-commerce, travel booking, SaaS dashboards, government portals, and developer tools. A built-in request interceptor blocks genuinely irreversible actions (payments, form submissions that send data) so evaluations can run safely on real sites. The benchmark records five layers of data per run: session replays, screenshots at each decision point, raw HTTP traffic, agent reasoning traces, and browser action sequences. This makes failure analysis tractable — you can see exactly which DOM element the agent misidentified, not just a final score. The dataset is open and the evaluation harness is reproducible. The headline finding is sobering: Claude Sonnet 4.6, the best performer, completes only 33.3% of tasks. GLM-5 is second at 24.2%. No model exceeds 50% on any individual task category. The implication is stark — current browser agents are far from autonomous on the open web, and the gap between benchmark performance and production performance is still enormous.
Research & Benchmarks
LamBench
120 λ-calculus challenges that cut through AI benchmark gaming
50%
Panel ship
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Community
Free
Entry
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.
Reviewer scorecard
“The five-layer recording (replays, HTTP traffic, reasoning traces) is the right approach for actual debugging — finally a benchmark where failure analysis is tractable. The 33% score also sets honest expectations for teams planning to ship production browser agents right now.”
“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.”
“Live website testing is a double-edged sword: sites change their DOM, anti-bot measures evolve, and a task that passes today may fail next week with no code change. Benchmark drift on live websites could make ClawBench scores meaningless over 6-month periods without constant maintenance.”
“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.”
“33% on live websites is actually more impressive than it sounds given the adversarial diversity of the real web. The trajectory from 5% in 2024 to 33% in 2026 means we're likely crossing 60% in 18 months — at which point browser agents start displacing RPA software at scale.”
“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.”
“As someone who uses browser agents for research and competitor monitoring, the failure mode analysis is exactly what I need. Knowing which website categories agents handle well (dev tools) vs. poorly (government portals) helps me route tasks appropriately right now.”
“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.”
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