activeCoding
MLS Bench Lite
Metric: Score (higher is better)Introduced: 2026
Moonshot-reported lightweight machine-learning-systems benchmark from the Kimi K2.7-Code launch materials. Use as Kimi-family evidence, not as a public SWE-bench substitute.
Models ranked
1
tracked on this benchmark
Score band
35.1 – 35.1
best → lowest tracked
Snapshot trend
—
need ≥2 snapshots
Leaderboard
Tracked models ranked by Score (higher is better).
#Model variant and provenanceRelative to leaderScore
1
Kimi K2.7-Code
35.1Version: official, Moonshot-reportedHarness: Not recordedEvaluator: Not recordedObserved: Jun 12, 2026Confidence: Not recordedSource
Notes: DAT-6271: Moonshot proprietary benchmark. No cross-model public leaderboard row was found.
How to read this benchmark
This benchmark scores models where higher is better. Use scores for directional filtering and shortlisting, not universal quality ranking; then validate pricing, context window, provider availability, and fit for your workload.
Trust this score when
- There is a fresh timestamped snapshot (or multiple snapshots) for this benchmark.
- The model list covers the same version family you can actually deploy today.
- Top candidates overlap with your required routing and feature requirements.
Be cautious when
- There is only one benchmark snapshot or the dataset appears stale.
- The benchmark metric direction is opposite of your decision objective.
- The score difference between options is narrow and likely within implementation variance.
Related benchmarks
Last reviewed: Jun 19, 2026