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.
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 3 models tested
| # | 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 |