LLM Reference
SWE-bench MultilingualactiveCoding

SWE-bench Multilingual

Metric: % Resolved (higher is better)Introduced: 2026

About

Multilingual SWE-bench suite evaluating software engineering issue resolution across repositories and languages beyond the original Python-heavy SWE-bench tasks.

How to read this benchmark

This benchmark uses a scoring system where higher is better. Scores are useful for directional filtering and model shortlisting, not for universal quality ranking.

Interpretation checklist: prefer benchmarks that are closest to your workload style, then validate against the linked model pages for pricing, context window, and provider availability.

Trust when:

  • There is a fresh timestamped snapshot (or multiple snapshots) for this benchmark.
  • Model list covers the same version family you can actually deploy today.
  • Top candidates overlap with your required routing and feature requirements.

Don't trust when:

  • There is only one benchmark snapshot or the dataset appears stale.
  • Benchmark metric direction is opposite of your decision objective.
  • The score difference between options is narrow and likely within implementation variance.

Current modeled score band for tracked entries is roughly 79.8 – 75.9.

Window

Apr 20 to May 21

last 3 snapshots

Mean delta

+1.15

score points

Coverage

2

models in latest snapshot

Leaderboard preview (top 5)

  1. 1. Composer 2.579.8
  2. 2. Kimi K2.676.7
  3. 3. DeepSeek V4 Pro76.2
  4. 4. Claude Sonnet 4.675.9