Agentic Coding Benchmark

Kimi Code Bench 3.0 Long-horizon

Internal Moonshot long-horizon coding benchmark; Kimi tested with Claude Code, competitors at max effort with possible fallback behavior.

Category

Agentic Coding

Score type

percent

Scale

100%

higher is better

Models tested

3

What is Kimi Code Bench 3.0 Long-horizon?

Kimi Code Bench 3.0 Long-horizon is a benchmark for how well a model works as an autonomous coding agent. The official description says: "Internal Moonshot long-horizon coding benchmark; Kimi tested with Claude Code, competitors at max effort with possible fallback behavior". Below we describe in simple words what benchmarks of this type generally examine.

What does it test?

Kimi Code Bench 3.0 Long-horizon measures how well a model works as an autonomous coding agent. In detail, it checks:

  • fixing real, open bugs from real software repositories
  • planning multi-step changes, running commands and editing files by itself
  • passing the project's test suite after its changes
  • handling whole repositories, not just small isolated code snippets

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.

Official methodology note: Internal Moonshot long-horizon coding benchmark; Kimi tested with Claude Code, competitors at max effort with possible fallback behavior.

Frequently asked questions

What is the Kimi Code Bench 3.0 Long-horizon benchmark?

Kimi Code Bench 3.0 Long-horizon is a benchmark for how well a model works as an autonomous coding agent. The official description says: 'Internal Moonshot long-horizon coding benchmark; Kimi tested with Claude Code, competitors at max effort with possible fallback behavior'. Below we describe in simple words what benchmarks of this type generally examine.

What does Kimi Code Bench 3.0 Long-horizon test?

Kimi Code Bench 3.0 Long-horizon mainly measures how well a model works as an autonomous coding agent. Concretely, it checks: fixing real, open bugs from real software repositories; planning multi-step changes, running commands and editing files by itself; passing the project's test suite after its changes.

How is the Kimi Code Bench 3.0 Long-horizon 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 Kimi Code Bench 3.0 Long-horizon data and scores?

The official source is https://huggingface.co/moonshotai/Kimi-K3.1-Pro. 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/kimi-code-bench-3-0-long-horizon/scores) or pull it directly from the Open LLM Radar API with any HTTP client.

Model scores 3 models tested

Reset
# Model Score Date Source Verified
1 Kimi K3.1 Pro
Moonshot AI
61.2%
07.11.2026 Report Verified
2 DeepSeek-V4.1-Flash-1017
DeepSeek
51.5%
07.11.2026 Kimi K3.1 Pro model card comparison column (max effort) https://huggingface.co/moonshotai/Kimi-K3.1-Pro Unverified
3 GLM-5.3
Zhipu AI
45.5%
07.11.2026 Kimi K3.1 Pro model card comparison column (max effort) https://huggingface.co/moonshotai/Kimi-K3.1-Pro Unverified

What now?

You've seen how models score on Kimi Code Bench 3.0 Long-horizon — here's what to do next.