LLM Reference
Terminal-Bench 2.0activeCodingAgents

Terminal-Bench 2.0

Metric: % Tasks Completed (higher is better)Introduced: 2026

About

Second-generation terminal agent benchmark with 89 high-quality tasks spanning software engineering, machine learning, security, data science, and other real shell environments.

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 69.3 – 59.1.

Window

Apr 20 to May 21

last 3 snapshots

Mean delta

-2.50

score points

Coverage

2

models in latest snapshot

Leaderboard preview (top 5)

  1. 1. Composer 2.569.3
  2. 2. DeepSeek V4 Pro67.9
  3. 3. Kimi K2.666.7
  4. 4. Claude Sonnet 4.659.1