📌What model should be used for shrimp farming?丨Let's take a look at the performance of major AI large language models in the OpenClaw agent task——

Based on my current usage experience and some feedback collected from friends around me, I will briefly discuss these models:

1⃣Gemini: Currently recognized as one of the most stable Agent models, it works well for complex task decomposition, tool invocation, and multi-step processes, but its drawbacks include frequent API load and rate limiting issues.

2⃣MiniMax: Extremely low cost, high cost-performance ratio, but its personality is rather bland, making it very suitable as a worker model in domestic automated assembly lines.

3⃣Kimi K2.5: Strong in long context and Agent architecture, very suitable for tasks like document analysis and visual processing, also a domestic worker model. However, its stability is average; it tends to write a lot of code for complex processes and then keeps fixing bugs.

4⃣Claude Opus: The most reliable model for executing complex tasks, with extremely strong inference stability. The only downside is that it is "expensive," and costs can easily exceed expectations, but mastering certain techniques makes this not a problem.

5⃣GPT: Fast, suitable for small tools or lightweight tasks, but complex tasks can easily forget context, and instruction tracking stability is not as strong as Gemini / Claude, so it's not very suitable for shrimp farming and can be directly passed over.

In summary——

Gemini: The most disciplined quantitative fund

Kimi: A highly talented but occasionally lazy trader

MiniMax: A low-paid but diligent intern

GPT: The most famous but with fluctuating performance

Claude: A top-tier researcher

In conclusion, I chose Claude Opus + Xianyu, the absolute king of cost-performance ratio!

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