MLS-Bench Lite leaderboard
2 ranked models · higher is better
View accessible chart data
| Model | Rank | Provider | Score |
|---|---|---|---|
| Kimi K3 | #1 | Moonshot AI | 48.3% |
| Kimi K2.7 Code | #2 | Moonshot AI | 35.1% |
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Coding · Benchmark profile
A 30-task subset of MLS-Bench that evaluates whether AI systems can invent generalizable and scalable machine-learning methods.
Data verified 21 Jul 2026 · Methodology 1.6.0
Benchmark score on MLS-Bench Lite
Kimi K3 leads at 48.3%, followed by Kimi K2.7 Code (35.1%).
Visual analysis
Switch between model placement, score distribution and descriptive provider averages. Every view uses the same sourced leaderboard.
2 ranked models · higher is better
| Model | Rank | Provider | Score |
|---|---|---|---|
| Kimi K3 | #1 | Moonshot AI | 48.3% |
| Kimi K2.7 Code | #2 | Moonshot AI | 35.1% |
One best score per model · higher is better
| Rank | Model | Provider | License | Evidence use | Score |
|---|---|---|---|---|---|
| #1 | Kimi K3 kimi-k3 | Moonshot AI | closed | Reference only | 48.3% |
| #2 | Kimi K2.7 Code kimi-k2.7-code | Moonshot AI | open | Estimated reference | 35.1% |
About MLS-Bench Lite
A 30-task subset of MLS-Bench that evaluates whether AI systems can invent generalizable and scalable machine-learning methods. Results stay tied to the exact model variant and evaluation system. Multiple systems for the same model use the best published score on this page; overall Lumina scoring uses the median of ranking-eligible rows.
Open benchmark source ↗FAQ
A 30-task subset of MLS-Bench that evaluates whether AI systems can invent generalizable and scalable machine-learning methods.
Kimi K3 by Moonshot AI currently leads with 48.3%.
2 models in the LuminaBench cohort have a qualifying score on this benchmark.
No. This benchmark is display-only and does not enter the overall Lumina composite.
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