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.
Benchmark data & downloads
Where to get, inspect and reuse the data behind this benchmark.
Model scores 6 models tested
| # | 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 |