Open-Source LongCat-2.0 Matches GPT-5.5 in Coding Tasks
rohanpaul_ai · x · 2026-07-08
Open-sourced by Meituan, LongCat-2.0 is a MoE model with approximately 1.6T parameters. In local agent tests conducted via the @kilocode CLI on atomic.chat, LongCat-2.0 performed similarly to GPT-5.5 on a "Duck Hunt" game coding task. The local run consumed about 70.3K tokens at $0 cost, while the cloud-based GPT-5.5 consumed 64.9K tokens costing $0.65.
Rather than directly answering prompts, the task required the agent to actually build and iteratively modify code. LongCat reportedly handled the logic for ducks, waves, ammo, hit physics, falling animations, and the dog retrieval loop quite well, demonstrating competitive performance in a three-round iterative agent workflow.
Related event: Meituan Open-Sources LongCat-2.0, a Trillion-Parameter MoE Model(2 posts)→
More from coding & agent
- GPT-6 Astra beats Factorio with enemies in 44 in-game hours at ~$4,500 API cost — liminal_bardo · 2026-09-11
- Investment Analyst Asks How to Build a Claude-Based Diligence Agent Stack — Careless_Tie2286 · 2026-09-11
- How Do You Catch Behavioral Regressions in LLM Agents Between Releases? — Beautiful_Belt_601 · 2026-09-11
- Treating agents like 50 First Dates: a 3-layer context system so every conversation doesn't start from zero — evielync · 2026-09-11
- Running the Firefox MCP on Android via Termux, ngrok, and mcp-proxy — Nervous-Strain7544 · 2026-09-11
- Run Firefox MCP on Android: Termux + ngrok tunnel tutorial — Nervous-Strain7544 · 2026-09-11