Atlas

Benchmarks

Terminal-Bench 2.1 (Harness Leaderboard, avg@3)

Hugging Face

The Harness Leaderboard's run of Terminal-Bench 2.1 (commit d49e28f1) on 87 of its 89 tasks, comparing coding-agent harnesses on fixed open models in Harbor; qemu-startup and qemu-alpine-ssh are left out because they need KVM, which Hugging Face's sandboxes lack. Each task scores the mean of three attempts and the rate is the mean over tasks. Infrastructure failures are retried and excluded, and trials that looked up a task's answer key count as failures. Kept as a component beside the 89-task average task accuracy.

Models
3
Top score
76.2
Leader
DeepSeek V4.1 Flash
Updated
Nov 5, 2026
Resolution
—
Saturation
—
Signal
69thpercentile
205 benchmarks from 0.8 to 3.6219 benchmarks from 3.6 to 6.4134 benchmarks from 6.4 to 9.393 benchmarks from 9.3 to 12.155 benchmarks from 12.1 to 14.925 benchmarks from 14.9 to 17.713 benchmarks from 17.7 to 20.55 benchmarks from 20.5 to 23.37 benchmarks from 23.3 to 26.27 benchmarks from 26.2 to 29.01 benchmark from 29.0 to 31.83 benchmarks from 31.8 to 34.66 benchmarks from 34.6 to 37.43 benchmarks from 37.4 to 40.21 benchmark from 40.2 to 43.11 benchmark from 45.9 to 48.71 benchmark from 65.6 to 68.4

241st strongest of 779

Signal-to-noise +8.74 for the Terminal-Bench 2.1 family

Difficulty
Not estimated

Another benchmark represents this family in the fit.

3 dated models · 2 frontier modelsClosed weightsOpen weights
Data: Harness Leaderboardatlas.village.dev
#ModelSettingScoreSource
1DeepSeek V4.1 Flashlow · Harness Leaderboard; mini-swe-agent 2.4.6 in Harbor
76.2
2GLM-5.3 Flashhigh · Harness Leaderboard; Pi 1.2.0 in Harbor; assumed a 128,000-token window and 16,384-token replies
69.3
3Flintalways on · Harness Leaderboard; Kimi Code 2.2.0 in Harbor; told every model holds 1,000,000 tokens
32.2
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