Agentic Coding Benchmark

DSBench-FullStack

DSBench-FullStack - internal full-stack development test set for evaluating coding agents on full-stack development tasks.

Category

Agentic Coding

Score type

percent

Scale

100%

higher is better

Models tested

6

What is DSBench-FullStack?

DSBench-FullStack is a benchmark for how well a model works as an autonomous coding agent. The official description says: "DSBench-FullStack - internal full-stack development test set for evaluating coding agents on full-stack development tasks". Below we describe in simple words what benchmarks of this type generally examine.

What does it test?

DSBench-FullStack measures how well a model works as an autonomous coding agent. In detail, it checks:

  • fixing real, open bugs from real software repositories
  • planning multi-step changes, running commands and editing files by itself
  • passing the project's test suite after its changes
  • handling whole repositories, not just small isolated code snippets

How to read the score

The score is accuracy: the percentage of tasks answered or solved correctly. 100% means every task was solved perfectly; higher is better.

Official methodology note: DSBench-FullStack - internal full-stack development test set for evaluating coding agents on full-stack development tasks.

Frequently asked questions

What is the DSBench-FullStack benchmark?

DSBench-FullStack is a benchmark for how well a model works as an autonomous coding agent. The official description says: 'DSBench-FullStack - internal full-stack development test set for evaluating coding agents on full-stack development tasks'. Below we describe in simple words what benchmarks of this type generally examine.

What does DSBench-FullStack test?

DSBench-FullStack mainly measures how well a model works as an autonomous coding agent. Concretely, it checks: fixing real, open bugs from real software repositories; planning multi-step changes, running commands and editing files by itself; passing the project's test suite after its changes.

How is the DSBench-FullStack score calculated?

The score is accuracy: the percentage of tasks answered or solved correctly. 100% means every task was solved perfectly; higher is better. The declared maximum score of this benchmark is 100.0.

Where do I get DSBench-FullStack data and scores?

The official benchmark page/reference is linked on this page. Every score on Open LLM Radar is also available as open data: download the scores JSON (http://open-llm-radar-api:8000/api/v1/benchmarks/dsbench-fullstack/scores) or pull it directly from the Open LLM Radar API with any HTTP client.

Model scores 6 models tested

Reset
# Model Score Date Source Verified
1 Kimi K3
Moonshot AI
73.7%
13.08.2026 Report Verified
2 DeepSeek-V4-Pro-0813
DeepSeek
71.1%
13.08.2026 Report Verified
3 DeepSeek-V4-Flash-0731
DeepSeek
68.7%
31.07.2026 Report Verified
4 GLM-5.2
Zhipu AI
61.8%
31.07.2026 Report Verified
5 DeepSeek-V4-Pro
DeepSeek
41.8%
13.08.2026 Report Verified
6 DeepSeek-V4-Flash
DeepSeek
37%
13.08.2026 Report Verified

What now?

You've seen how models score on DSBench-FullStack — here's what to do next.