agents benchmark
NL2Repo
NL2Repo evaluates long-horizon coding capabilities including repository-level understanding, where models must generate or modify code across entire repositories from natural language specifications.
Updated Aug 11, 2026
Models14
Model coverage14
MetricScore
EvidenceB
NL2Repo Ranking
Higher score ranks better on this benchmark.
| 01 | AC | 55.9% | 100.0% | 14 | C | |
| 02 | DE | 54.2% | 92.3% | 14 | C | |
| 03 | ZA | 48.9% | 84.6% | 14 | C | |
| 04 | AC | 47.2% | 76.9% | 14 | C | |
| 05 | BY | 47.0% | 69.2% | 14 | C | |
| 06 | TE | 45.6% | 61.5% | 14 | C | |
| 07 | BY | 43.7% | 53.9% | 14 | C | |
| 08 | ZA | 42.7% | 46.1% | 14 | C | |
| 09 | MI | 42.1% | 38.5% | 14 | C | |
| 10 | AC | 41.1% | 30.8% | 14 | C | |
| 11 | MI | 39.8% | 23.1% | 14 | C | |
| 12 | AC | 37.9% | 15.4% | 14 | C | |
| 13 | AC | 36.2% | 7.7% | 14 | C | |
| 14 | AC | 29.4% | 0.0% | 14 | C |
NL2Repo Score Distribution
A closer view of the leading scores on this benchmark.
NL2Repo Highlights
The leading models and scores on this benchmark.
What is NL2Repo?
What NL2Repo measures and how its scores work.
NL2Repo evaluates long-horizon coding capabilities including repository-level understanding, where models must generate or modify code across entire repositories from natural language specifications.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
- Family
- NL2Repo
- Modality
- text
- Primary category
- agents
- Score direction
- higher
- LLMBoard eligible
- Yes
- Evaluation key
- nl2repo|llm-stats-current
Benchmark scores retain their original unit. Overall score eligibility is shown separately.
FAQ
Common questions about NL2Repo.
Which model scores highest on NL2Repo?
Qwen3.8 Max is currently ranked first with 55.9%.
What does NL2Repo measure?
NL2Repo evaluates long-horizon coding capabilities including repository-level understanding, where models must generate or modify code across entire repositories from natural language specifications.
Is a higher score better?
Yes. Higher values rank better for this benchmark.
How many models are compared?
14 model results are currently shown.
Does this benchmark affect the overall score?
Yes. This benchmark can contribute to the current LLMBoard capability score.