Agentic Coding Benchmark

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.

Model scores 1 model tested

Reset
# Model Score Date Source Verified
1 LFM2.5-24B-A2B
LiquidAI
51%
13.10.2026 Report Verified