Datasets and task coverage
ModelDial evaluates backend development, frontend interaction and reasoning on its own private datasets. Below are the tasks, scoring criteria and current results.
The role and limits of private test data
Leaderboards use different tasks, scoring methods and model settings, so their rankings are not interchangeable. Private test data can complement public benchmarks, but task design and scoring still need scrutiny.
Some benchmarks are nearing their scoring ceiling
Some benchmarks have saturated: when leading models cluster near the scoring ceiling, scores become less useful for distinguishing them. Evaluation needs discriminating tasks and ongoing design and maintenance.
Published questions may enter training data
Published test material can enter training data or be used for benchmark-specific optimization. Private test data can reduce that exposure. A public leaderboard does not necessarily use public test questions.
Publish task coverage, scoring methods and results
ModelDial records backend, frontend and reasoning results separately and publishes task coverage, scoring methods and measured results. Compare generations and reasoning efforts, then relate the evidence to your work.
Keeping data private does not guarantee a good evaluation. Sound tasks, reliable scoring and continued maintenance are also necessary.
Research and industry practice
- When AI Benchmarks Plateau
ICML 2026: nearly half of the 60 benchmarks studied showed saturation. Expert curation mattered for resilience; whether test data was public did not explain it.
- LiveBench
Uses questions from recent material and regularly adds tasks to reduce contamination risks.
- Scale · MASK
Ranks models on a private test split alongside a public split, to monitor overfitting and limit gaming.
Business rules and error handling
- Task coverage
- Service contracts, retry behavior, CI audits, transaction consistency, caching and regression.
- What we check
- Identify conflicting or missing rules and produce verifiable solutions, with checks for constraints, edge cases and regression coverage.
gpt-6-astra · xhigh View the ranking Interface behavior and state
- Task coverage
- Page implementation, interaction flows, asynchronous selection, query state, responsive layouts and regression.
- What we check
- Check behavior, state management and interaction feedback against the scoring rules for the task.
gpt-6-astra · xhigh View the ranking Knowledge and reasoning across disciplines
- Task coverage
- Mathematics, computer science and AI, physics, engineering, biology and medicine, humanities and social sciences.
- What we check
- Score answer correctness and retain results by discipline. Scores describe performance on the evaluated tasks.
claude-opus-5-5 · max View the ranking How the overall score is calculated
The overall score combines backend (40%), frontend (30%) and reasoning (30%) for one tested configuration. Each axis is scored out of 100; start with the axis relevant to your work.
Read about scoring and updates