Hamburger_menu.svg

Restore truth to model evaluation.

Turing CodeBench rebuilds benchmark rigor on a private, human-crafted dataset of 900+ multilingual coding problems engineered for reproducibility and calibrated difficulty. Where public leaderboards inflate scores through memorization, CodeBench reveals authentic capability through deterministic scoring that any lab can verify. Accurate, repeatable, and free from benchmark bias.

Start Hillclimb

Advancing code evaluation with reproducible benchmarks

Public coding benchmarks deliver visibility but not validity: models train on the same datasets that test them, hiding regression behind rising scores. CodeBench restores fairness with a non-public dataset, expert-vetted prompts, and pass/fail determinism across six programming languages. It transforms evaluation into an auditable process that is consistent across releases, comparable across models, and trusted by research teams pushing frontier code intelligence.

Core CodeBench capabilities

Each feature is designed to make evaluation scientific, auditable, and repeatable. Not guess-and-check.

Human-expert crafted

Every problem is built by expert engineers who intentionally create edge cases that expose reasoning failures in frontier models.

Non-public dataset

CodeBench uses 900+ unseen coding challenges across Python, Java, JavaScript, Go, C++, and Swift. No public overlap means results reflect true model capability.

Aider-native design

Fully plug-and-play with the Aider benchmark harness. No custom setup or code modification required.

Calibrated difficulty

Every sample is selected to challenge at least one leading model, ensuring meaningful spread across the leaderboard.

Transparent and reproducible

Run benchmarks yourself, audit outputs, and regenerate reports with identical conditions.

Deterministic scoring

Binary pass/fail unit testing; if one test fails, it’s a FAIL. Accuracy is simply the percentage of samples passed.

What powers CodeBench

Strengthen your model evaluations with CodeBench

Subjects

STEM (math, AI, chemistry, earth sciences, civil & structure engineering, aerospace engineering, electronics engineering), Business (finance, analytics, operations, HR, accounting, marketing, sales).

Data types

Sales graphs, line charts, engineering schematics, scientific visuals, multi-column tables, technical diagrams, and more.

Capabilities

Advanced perception, spatial, numerical, logical, temporal, contextual commonsense, abstract, counterfactual, and multi-step reasoning.

Prompt structure

75-token average length with embedded visuals, multi-step dependencies.

Scoring

LLM-as-a-judge system using score 0–1 per output, averaged across five generations.

Strengthen your model evaluations with CodeBench

Detect regression before release. Compare frontier models under fair conditions. Build confidence in your code-generation stack with verified, reproducible results that reflect true capability.

Start with 900+ off-the-shelf tasks or request a tailored dataset aligned to your model’s focus language and domain.

Start Hillclimb