Agentic Coding Benchmark

NL2Repo

NL2Repo - repository generation from natural language. Others evaluated via Claude Code (temp=1.0, top_p=0.95, max_turns=900).

Category

Agentic Coding

Score type

percent

Scale

100%

higher is better

Models tested

27

What is NL2Repo?

NL2Repo asks for something very hard: generate a complete, working software repository from a natural-language description. The evaluated model typically runs inside an coding agent harness (for example Claude Code) and has many turns to create, run and fix its own code.

What does it test?

NL2Repo measures how well a model works as an autonomous coding agent. In detail, it checks:

  • turning a plain-language spec into a project structure
  • writing complete code files instead of fragments
  • making the generated repo actually build and run
  • long autonomous sessions with hundreds of tool calls

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 NL2Repo benchmark?

NL2Repo asks for something very hard: generate a complete, working software repository from a natural-language description. The evaluated model typically runs inside an coding agent harness (for example Claude Code) and has many turns to create, run and fix its own code.

What does NL2Repo test?

NL2Repo mainly measures how well a model works as an autonomous coding agent. Concretely, it checks: turning a plain-language spec into a project structure; writing complete code files instead of fragments; making the generated repo actually build and run.

How is the NL2Repo 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 NL2Repo 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/nl2repo/scores) or pull it directly from the Open LLM Radar API with any HTTP client.

Model scores 27 models tested

Reset
# Model Score Date Source Verified
26 Gemma4-26B-A4B
Google
11.6%
16.04.2026 Qwen Team (official model card - comparison) Unverified
27 Gemma4-12B
Google
10.3%
25.06.2026 DeepReinforce Team (official model card - comparison) Unverified
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What now?

You've seen how models score on NL2Repo — here's what to do next.