RLTasksby ulam.ai
Models / ByteDance

ByteDance / MODEL EVIDENCE

Doubao-1.5-pro

1 benchmarks · 24 observations · 1 sources

Scores at a glance

1 benchmarks · large dot: Doubao-1.5-pro; small dots: closest models where reported

Multi-SWE-bench ↗26.2%
26.248.2

Doubao-1.5-pro · Multi-SWE-bench · Resolved · Python · language-specific results ↗

20 more reported results

12.4% · Doubao-1.5-pro · Multi-SWE-bench · Resolved · Python · language-specific results · Multi-SWE-bench: A Multilingual Benchmark for Issue Resolving ↗

8.8% · Doubao-1.5-pro · Multi-SWE-bench · Resolved · Python · language-specific results · Multi-SWE-bench: A Multilingual Benchmark for Issue Resolving ↗

5.47% · Doubao-1.5-pro · Multi-SWE-bench · Resolved · Java · language-specific results · Multi-SWE-bench: A Multilingual Benchmark for Issue Resolving ↗

7.03% · Doubao-1.5-pro · Multi-SWE-bench · Resolved · Java · language-specific results · Multi-SWE-bench: A Multilingual Benchmark for Issue Resolving ↗

0.78% · Doubao-1.5-pro · Multi-SWE-bench · Resolved · Java · language-specific results · Multi-SWE-bench: A Multilingual Benchmark for Issue Resolving ↗

2.23% · Doubao-1.5-pro · Multi-SWE-bench · Resolved · TypeScript · language-specific results · Multi-SWE-bench: A Multilingual Benchmark for Issue Resolving ↗

1.79% · Doubao-1.5-pro · Multi-SWE-bench · Resolved · TypeScript · language-specific results · Multi-SWE-bench: A Multilingual Benchmark for Issue Resolving ↗

0% · Doubao-1.5-pro · Multi-SWE-bench · Resolved · TypeScript · language-specific results · Multi-SWE-bench: A Multilingual Benchmark for Issue Resolving ↗

1.12% · Doubao-1.5-pro · Multi-SWE-bench · Resolved · JavaScript · language-specific results · Multi-SWE-bench: A Multilingual Benchmark for Issue Resolving ↗

1.4% · Doubao-1.5-pro · Multi-SWE-bench · Resolved · JavaScript · language-specific results · Multi-SWE-bench: A Multilingual Benchmark for Issue Resolving ↗

2.1% · Doubao-1.5-pro · Multi-SWE-bench · Resolved · Go · language-specific results · Multi-SWE-bench: A Multilingual Benchmark for Issue Resolving ↗

1.64% · Doubao-1.5-pro · Multi-SWE-bench · Resolved · Go · language-specific results · Multi-SWE-bench: A Multilingual Benchmark for Issue Resolving ↗

4.18% · Doubao-1.5-pro · Multi-SWE-bench · Resolved · Rust · language-specific results · Multi-SWE-bench: A Multilingual Benchmark for Issue Resolving ↗

1.67% · Doubao-1.5-pro · Multi-SWE-bench · Resolved · Rust · language-specific results · Multi-SWE-bench: A Multilingual Benchmark for Issue Resolving ↗

0.84% · Doubao-1.5-pro · Multi-SWE-bench · Resolved · Rust · language-specific results · Multi-SWE-bench: A Multilingual Benchmark for Issue Resolving ↗

0% · Doubao-1.5-pro · Multi-SWE-bench · Resolved · C · language-specific results · Multi-SWE-bench: A Multilingual Benchmark for Issue Resolving ↗

2.34% · Doubao-1.5-pro · Multi-SWE-bench · Resolved · C · language-specific results · Multi-SWE-bench: A Multilingual Benchmark for Issue Resolving ↗

0% · Doubao-1.5-pro · Multi-SWE-bench · Resolved · C++ · language-specific results · Multi-SWE-bench: A Multilingual Benchmark for Issue Resolving ↗

6.2% · Doubao-1.5-pro · Multi-SWE-bench · Resolved · C++ · language-specific results · Multi-SWE-bench: A Multilingual Benchmark for Issue Resolving ↗

3.1% · Doubao-1.5-pro · Multi-SWE-bench · Resolved · C++ · language-specific results · Multi-SWE-bench: A Multilingual Benchmark for Issue Resolving ↗

Closest models by score

Similar positions on shared benchmark leaderboards. Smaller gaps are closer.

25.0 percentile-point gap

1 of 1 shared benchmark comparisons

37.5 percentile-point gap

1 of 1 shared benchmark comparisons

How similarity is measured

Mean absolute gap in percentile positions on shared benchmark settings; lower is closer. At least 60% overlap is required, with a minimum of three when available. Independent evaluations are used when there are at least three; otherwise published report comparisons are used. Repeated model observations within one setting are averaged before ranking. Writing-style traits and composite indices are excluded. This describes similarity, not an overall quality ranking.

Filter comparisons to this model →

Sources

Every plotted observation links to its evaluation. Reported configurations, benchmark versions and task subsets can differ; similarity describes shared scores, not training lift.

  1. Multi-SWE-bench: A Multilingual Benchmark for Issue Resolving — published result table ↗

    Daoguang Zan, Zhirong Huang, Wei Liu, Hanwu Chen, Linhao Zhang, Shulin Xin et al. · 2025

    All 19 authors

    Daoguang Zan, Zhirong Huang, Wei Liu, Hanwu Chen, Linhao Zhang, Shulin Xin, Lu Chen, Qi Liu, Xiaojian Zhong, Aoyan Li, Siyao Liu, Yongsheng Xiao, Liangqiang Chen, Yuyu Zhang, Jing Su, Tianyu Liu, Rui Long, Kai Shen, Liang Xiang

Names used in source tables

Doubao-1.5-pro