# 开源模型LongCat-2.0在Duck Hunt编程测试中与GPT-5.5表现相当，成本为零

- 来源：Rohan Paul (@rohanpaul_ai)
- 发布时间：2026-07-08 05:58
- AIHOT 分数：61
- AIHOT 链接：https://aihot.virxact.com/items/cmrb7wi2a01k8ihl1tetxsfit
- 原文链接：https://x.com/rohanpaul_ai/status/2074614088647454855

## AI 摘要

开源模型LongCat-2.0在Duck Hunt游戏编程测试中与GPT-5.5表现相当，但成本为零。测试由atomic.chat使用kilocode CLI的agent完成，双方均需在三次agent迭代内构建并改进同一游戏（含鸭子、波浪、弹药、物理碰撞、掉落动画和狗回收循环）。LongCat-2.0本地运行70.3K token（$0），GPT-5.5云端运行64.9K token（$0.65）。美团将其列为1.6T参数MoE，每token约48B active。结果证明，对于小型明确任务，本地开源模型质量已可接近前沿云模型，主要差异转向成本。

## 正文

So cool, Open-source model LongCat-2.0 matched GPT-5.5 on a Duck Hunt coding run for $0.

Test was done by atomic[.]chat, a desktop app that runs LLMs locally using @kilocode CLI with their agent.

The side-by-side run used 70.3K tokens locally against 64.9K cloud tokens costing $0.65.

The task was not a prompt answer; the agent had to build and revise code.

LongCat apparently handled ducks, waves, ammo, hit physics, falling animation, and the dog retrieval loop well enough to look competitive in a three-iteration agent workflow.

Meituan lists LongCat-2.0 as a 1.6T-parameter MoE with about 48B active per token.

Shows something very practical: for small, clearly defined tasks, a local open model can sometimes produce work that looks almost as good as a frontier cloud model.

So the main difference may stop being quality and start being cost.

### 引用推文

> atomic.chat：Open-weight LongCat 2.0 matched GPT-5.5 level on agentic game dev for $0! We ran Meituan's LongCat 2.0 against cloud frontier GPT-5.5 in @kilocode CLI with thei...