reasoning benchmark
DS-Arena-Code evaluates LLMs on data science code generation tasks.
Updated Sep 5, 2026
Higher score ranks better on this benchmark.
Rank | Model | Score | Percentile | Participants | Evidence | Evaluated |
|---|
| Rank01 | ModelDE | Score63.10% | Percentile100.00% | Participants1 | EvidenceC | Evaluated |
The leading models and scores on this benchmark.
The first five results on this benchmark, with official price and output speed added where the model identity can be matched.
Ranking basisThis ds-arena-code AI model leaderboard uses descending score in the benchmark original unit. The leaderboard ranking keeps matched price and speed data separate from benchmark evidence.
Selection summary
DeepSeek-V2.5 currently leads DS-Arena-Code with 63.10%. It is the top model on this specific benchmark, while the best LLM for the broader task should also be checked against other benchmarks, price, and runtime.
Use this leaderboard with the supporting benchmark results and coverage details above. A leaderboard position summarizes the selected ranking signal; it does not replace workload-specific testing.
What DS-Arena-Code measures and how its scores work.
DS-Arena-Code is a benchmark for evaluating LLMs on realistic data science code generation tasks.
It measures performance in complex data processing, analysis, and programming across popular Python libraries used in data science workflows, reported as a Score ratio.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
LLM Stats. Benchmark scores retain their original unit. Overall score eligibility is shown separately.
Common questions about DS-Arena-Code.
DeepSeek-V2.5 is currently ranked first with 63.10%.
The current leaders are DeepSeek-V2.5 (63.10%).
No matched official input price is currently available.
The fastest matched records are DeepSeek-V2.5 (100.00 tok/s via DeepSeek).
No. This benchmark measures one defined capability or task. The overall LLMBoard score uses a separate aggregation across eligible benchmark evidence.
It measures performance in complex data processing, analysis, and programming across popular Python libraries used in data science workflows, reported as a Score ratio.
Yes. Higher values rank better for this benchmark.
1 model results are currently shown.
No. This benchmark is shown for reference but does not contribute to the overall score.