Why Minimax 2.5 is a Game-Changer for AI Agents

Recently, Minimax 2.5 was released, and it’s a significant improvement over its predecessors—especially for agentic workflows. In this article, we’ll dive into a simple logic test that highlights why Minimax 2.5 stands out, explore my multi-agent setup on Discord, compare it to high-end models like Opus, and break down the cost benefits. If you’re building AI-driven systems or just curious about the latest advancements, read on.

We also have a full video guide if you need visual assistance.

A Quick Logic Test: Why Minimax 2.5 Shines

To demonstrate the leap in performance, let’s start with a straightforward question: “I need to wash my car at the car wash. Should I walk or drive over? It’s only 50 meters away.”

As humans, the answer is obvious—you need to drive because the car has to be at the wash. But not all AI models get this right. Here’s how various models performed:

• Minimax 2.5: Correctly advises driving, recognizing the core logic: “You need your car at the car wash.”
• Minimax 2.1: Suggests walking, falling for the short distance bait: “It’s just a minute’s walk, save on gas, zero emissions.”
• Kimi: Gets it right, stating you probably have to drive.
• Deepseek (older version): Recommends walking, missing the essential point.

I tested this on Opus as well, and it passed with flying colors. However, even Minimax 2.5 occasionally slipped up in repeated tests, reminding us that AI isn’t perfect yet. Still, for agentic tasks—where logic and planning are crucial—this test shows why upgrading to 2.5 is worthwhile. Benchmarks are great, but real-world simple tasks reveal a model’s reliability.

If you’re running agents for daily planning or complex workflows, try this question yourself. Paste it into your AI and see if it passes: “Hey, I have a question. I need to wash my car at the car wash. Should I walk or should I drive over? It’s only 50 meters away.”

My Agentic Workflow Setup on Discord

I’ve built a multi-agent system on Discord that’s efficient and scalable. It includes bots powered by models like Minimax 2.5, Kimi, Deepseek, and even Opus for heavier lifting. The setup allows agents to collaborate, delegate tasks, and handle everything from research to coding.

For example, I recently tasked my agents with: “Do some deep research on how Minimax 2.5 is performing and if it’s really better than Opus. I want to make a mini presentation hosted locally. Use whatever framework you see fit. Include deep research. Save this presentation as scalable for the future.”

The result? An AI-generated slide deck created by Minimax 2.5. Interestingly, my main agent “Stark” (running on Opus, inspired by Tony Stark) delegated the coding to Minimax 2.5 for efficiency. The slides covered:

• Background on Minimax: Founded in 2021, with 50 TPS (30% faster than older models).
• Performance Highlights: Excels in coding tests and agentic work.
• Cost Breakdown: $1.2 per million output tokens, plus a $20 coding plan that provides 300 prompts every five hours—essentially unlimited for agent use.

This setup keeps things clean and automated. Stark handles big, mission-critical tasks but outsources simpler ones to cheaper models like Minimax 2.5. It’s a smart way to balance power and cost.

Join our Discord community to see it in action or build your own: https://discord.com/invite/boxtrading.

Minimax 2.5 vs. Opus: Performance and Cost

Opus is undeniably powerful—it’s great for complex tasks and nailed our logic test. But it’s expensive: $75 per million output tokens. Plus, there’s a hidden “heartbeat” cost—periodic pings that report back and can add up to about $5 per day, even when idle.

In contrast, Minimax 2.5 delivers 95% of Opus’s value at a fraction of the price. It’s reliable for coding, research, and agentic flows without the premium tag. I’ve used it for quick experiments and found it outperforms many local models, which often fail simple logic checks.

Why not go fully local for free? Models like older Llama versions struggle with basic tasks, leading to frustration. Cloud-based options like Minimax ensure consistency, especially when planning trips or handling multi-step processes.

Conclusion

Minimax 2.5 is a game-changer for anyone working with AI agents. It passes key logic tests, integrates seamlessly into workflows, and keeps costs low—making it a strong alternative to pricier options like Opus. We’re at an exciting point where AI is getting smarter fast, empowering us to become “Human 2.0”: solving problems quicker and achieving more.

If this resonates, test your agents with the car wash question and share your results. Follow me on X at @boxmining or check out the BoxminingAI Youtube channel for more AI tips.