LongCat-2.0 vs Qwen3.6 Plus

Both target 1M-context agent workloads — Qwen3.6 Plus emphasizes terminal efficiency and value; LongCat-2.0 pushes trillion-scale MoE for deep agentic coding.

Side-by-Side

Dimension LongCat-2.0 Qwen3.6 Plus
Parameters 1.6T / 33–56B active Undisclosed
Context 1M 1M
Positioning Trillion-scale agentic coding MoE Terminal tasks + cost efficiency
SWE-bench Pro 59.5
SWE-Bench Verified 78.8%
Terminal-Bench 2.0 61.6% (beats Claude Opus 4.6 at 59.3%)
Terminal-Bench 2.1 (LongCat official) 70.8
Training narrative Full domestic chip pipeline Alibaba Qwen ecosystem

Terminal-Bench 2.0 and 2.1 may differ in task sets — compare version numbers when evaluating.

Terminal vs Deep SWE: Two Different Wins

Qwen3.6 Plus leads on Terminal-Bench 2.0 (61.6%), beating Claude Opus 4.6 (59.3%) on that harness. LongCat reports Terminal-Bench 2.1 at 70.8 — different suite versions; not a direct A/B.

Qwen's SWE-Bench Verified (78.8%) and LongCat's SWE-bench Pro (59.5) measure different task mixes. Match the suite to your production patch shape.

Cost & Ecosystem

Qwen3.6 Plus targets value and terminal efficiency in the Alibaba Qwen stack. LongCat offers trillion-scale open weights and promo API pricing ($0.30/$1.20 per M). See pricing table.

When to Choose LongCat-2.0

  • Deep multi-file software engineering (SWE-bench Pro leadership)
  • Maximum active compute per token for complex agent reasoning
  • Open trillion-parameter weights with documented domestic training stack
  • Aggressive API promo pricing for high-volume agent loops

When to Choose Qwen3.6 Plus

  • Terminal-first workflows where Qwen's Terminal-Bench 2.0 score is the reference
  • Teams already embedded in the Qwen / Alibaba Cloud ecosystem
  • Cost-efficiency positioning for standardized terminal automation tasks
  • Strong SWE-Bench Verified (78.8%) when that suite matches your eval harness