SWE-bench Pro v2.0 Hard
SWE-bench Pro v2.0 Hard split as reported on the Ripple-65B Preview card; Terminus 2 harness used for Ripple-65B Preview, competitor scores partially official model-card values.
Category
Agentic Coding
Score type
percent
Scale
100%
higher is better
Models tested
3
What is SWE-bench Pro v2.0 Hard?
SWE-bench Verified is a benchmark in which a model has to fix real software bugs in popular open-source Python projects. The model gets a GitHub issue and the repository, and must change the code itself until the automated tests pass. The tasks were reviewed by OpenAI engineers, so a task is only in the set when it is clearly reproducible and solvable.
What does it test?
SWE-bench Pro v2.0 Hard measures how well a model works as an autonomous coding agent. In detail, it checks:
- finding the right file(s) in a large real codebase
- writing a fix that solves the described issue
- making sure the project's existing tests still pass
- working autonomously with terminal and file-editing tools
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.
Frequently asked questions
What is the SWE-bench Pro v2.0 Hard benchmark?
SWE-bench Verified is a benchmark in which a model has to fix real software bugs in popular open-source Python projects. The model gets a GitHub issue and the repository, and must change the code itself until the automated tests pass. The tasks were reviewed by OpenAI engineers, so a task is only in the set when it is clearly reproducible and solvable.
What does SWE-bench Pro v2.0 Hard test?
SWE-bench Pro v2.0 Hard mainly measures how well a model works as an autonomous coding agent. Concretely, it checks: finding the right file(s) in a large real codebase; writing a fix that solves the described issue; making sure the project's existing tests still pass.
How is the SWE-bench Pro v2.0 Hard 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 SWE-bench Pro v2.0 Hard 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/swe-bench-pro-v2-hard/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 3 models tested
| # | Model | Score | Date | Source | Verified |
|---|---|---|---|---|---|
| 1 |
Ripple-65B Preview
Ricursive Intelligence
|
79.1%
|
— | Report | Verified |
| 2 |
Muse-Spark-Open-816B-A42B
Meta Superintelligence Lab
|
72.4%
|
— | Report | Unverified |
| 3 |
NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16
NVIDIA
|
30.7%
|
— | Report | Unverified |