# MLS-Bench-Lite ![Category](https://img.shields.io/badge/Category-ML%20Systems%20Engineering-1f6feb) ![Metric](https://img.shields.io/badge/Metric-%25%20Success-2da44e) ![Saturation](https://img.shields.io/badge/Saturation-Low-d29922) ![Created](https://img.shields.io/badge/Created-2026-555) > **Quick answer:** MLS-Bench-Lite is a lightweight benchmark testing AI agents on machine learning systems engineering tasks — configuring training pipelines, debugging ML infrastructure, and optimizing model deployment workflows. Kimi K3 scores 48.3% as of July 2026. ## At a Glance **What it tests:** A model's ability to complete practical ML systems engineering work, such as pipeline configuration and infrastructure debugging, as opposed to model-level ML research tasks. **Why it matters:** ML systems engineering (as distinct from ML research) is a large share of real-world ML team workload. MLS-Bench-Lite targets this practical, infrastructure-focused capability. **Known limitations:** As a "Lite" variant, task coverage is narrower than a full-scale ML systems benchmark, and independent documentation of methodology is limited. ## What MLS-Bench-Lite Measures MLS-Bench-Lite evaluates AI agents on machine learning systems engineering tasks — the practical infrastructure work surrounding model training and deployment, including pipeline configuration, debugging training failures, and optimizing resource usage. It complements broader coding benchmarks by focusing specifically on ML-adjacent systems work. ## Benchmark Specifications | Field | Value | |---|---| | Task category | Coding / ML systems engineering | | Metric | % task success | | Saturation | Low | | Created by | Not yet independently documented | ## How MLS-Bench-Lite Is Scored Agents attempt ML systems engineering tasks, and outcomes are verified against reference configurations or pipeline behavior, producing an aggregate % task success rate. ## State-of-the-Art Results | Rank | Model | Score | Source | Date | |---|---|---|---|---| | 1 | [Kimi K3](/models/moonshot/kimi-k3) | 48.3% | [Kimi K3 technical report](https://www.kimi.com/blog/kimi-k3) | 2026-07 | *Score sourced from Moonshot AI's Kimi K3 announcement, July 2026.* ## MLS-Bench-Lite on Benchgen > No Benchgen results yet — [be the first to run MLS-Bench-Lite](/benchmarks/platform/mls-bench-lite). ## MLS-Bench-Lite vs Other Benchmarks | Benchmark | What it tests | Saturation | |---|---|---| | **MLS-Bench-Lite** | ML systems engineering tasks | Low | | [PostTrainBench](/benchmarks/platform/posttrainbench) | Post-training/ML research workflows | Low | | [DeepSWE](/benchmarks/datacurve/deepswe) | Real-world software engineering tasks | Low | | [SWE-Bench Pro](/benchmarks/swe-bench-team/swe-bench-pro) | Professional-grade SWE agent tasks | Low | ## Run MLS-Bench-Lite on Your Model Benchgen lets you run MLS-Bench-Lite against your own model, tracking ML systems engineering task success as your team evaluates agents for infrastructure and pipeline work. ## Frequently Asked Questions

What is MLS-Bench-Lite?

MLS-Bench-Lite is a lightweight benchmark testing AI agents on machine learning systems engineering tasks, such as pipeline configuration and infrastructure debugging.

What does a good score look like on MLS-Bench-Lite?

Kimi K3 reports 48.3% as of July 2026, reflecting the practical difficulty of real ML systems engineering work for current frontier agents.

Who created MLS-Bench-Lite?

MLS-Bench-Lite's originating team is not yet independently documented outside of its citation in Kimi K3's July 2026 technical report.

Rank	Model	Score
1	kimi-k3	48.3
2	qwen3-8-max	41

MLS-Bench-Lite — Results

MLS-Bench-Lite — Evaluations