SWE-bench Verified (OpenCode)
SWE-bench Verified evaluated with the OpenCode agent harness. As evaluated on the LFM2.5-24B-A2B model card: vLLM, temperature = 0.6, max output tokens = 32768. Harness-specific variant distinct from the harness-unspecified SWE-bench Verified.
Category
Agentic Coding
Score type
percent
Scale
100%
higher is better
Models tested
1
What is SWE-bench Verified (OpenCode)?
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 Verified (OpenCode) 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 Verified (OpenCode) 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 Verified (OpenCode) test?
SWE-bench Verified (OpenCode) 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 Verified (OpenCode) 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 Verified (OpenCode) data and scores?
The official source is https://huggingface.co/LiquidAI/LFM2.5-24B-A2B. 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-verified-opencode/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.
Official benchmark page / paper
The benchmark maintainer's website, paper or leaderboard
Benchmark metadata (JSON)
This page's benchmark record from the Open LLM Radar API
All model scores (JSON download)
Full score table as JSON; paginate with limit/offset params
Open LLM Radar API docs
Swagger UI to explore and reuse all radar API endpoints
Model scores 1 model tested
| # | Model | Score | Date | Source | Verified |
|---|---|---|---|---|---|
| 1 |
LFM2.5-24B-A2B
LiquidAI
|
51%
|
13.10.2026 | Report | Verified |