With so many AI models available for OpenClaw, the big question everyone keeps asking is: which one should you actually use? We’ve been testing two popular options head-to-head — Claude Opus and MiniMax 2.5 — and I wanted to share our honest, real-world experience rather than just throwing benchmark numbers at you.
The Setup: Luxury vs Budget
For this comparison, we set up two very different configurations. I went with Claude Opus for my bots Stark and Banner — the premium option running through Anthropic’s API. My colleague went with MiniMax 2.5 for his bot Jeff, which is significantly cheaper. We’re talking about $20/month for MiniMax versus $30-60 per day for Opus usage. Yes, per day. Over a month, that’s roughly $1,800 for Opus compared to $20 for MiniMax. The cost difference is staggering.
MiniMax claimed their M2.5 model delivers 95% of Claude Opus performance at a fraction of the cost. On paper, that sounds incredible — and their benchmark scores are genuinely impressive, with an 80.2% on SWE-Bench Verified and strong results in multi-turn function calling tasks. But benchmarks and daily use are two very different things.
Where MiniMax 2.5 Struggled
The real-world results told a different story. Every morning, I’d see my colleague frustrated with Jeff’s performance. Here’s what went wrong:
First, the cron job timing. He asked Jeff to deliver a daily news briefing at 7:00 AM. Simple enough, right? But it never came at 7:00 AM. We tried fixing it, explicitly telling the bot to set it up properly — and it still didn’t register it as a cron job. Meanwhile, Opus-powered Stark delivered daily briefings consistently to spec.
Then there was the logic test. We asked both bots: “If I need to wash my car, should I drive or walk to the car wash?” Opus got it right most of the time — obviously you drive, because you need your car there. MiniMax? It told him to walk to the car wash. Without the car. The first few times it answered correctly, but on repeated runs, the inconsistency showed up hard.
Where Claude Opus Shined
Opus wasn’t perfect either — it once labeled a February 26 briefing as February 24 in the title, which gave me a brief heart attack. But the actual content was correct and dated properly. More importantly, Opus showed genuine initiative. When OpenClaw got an update, Opus proactively found the previous presentation, incorporated the new information, and updated everything without being asked. That kind of contextual awareness and follow-through is what separates a useful AI agent from a frustrating one.
There was also a noticeable difference in what I’d call the “bonus touch.” Opus would include things like “this was yesterday’s briefing in case you missed it” — small quality-of-life additions that showed it understood the workflow, not just the individual task. Jeff’s approach was more like: you missed it, tough luck.
The Slot Machine Problem
One of the most interesting takeaways from our testing is what we call the “slot machine” effect. AI agents are inherently inconsistent — you can give the exact same prompt to the same model and get different results each time. There’s a randomness factor baked into how these models generate responses, which means your experience can vary wildly from someone else’s even on identical tasks.
This is why some community members reported great results with MiniMax while we were pulling our hair out. It’s not necessarily about skill — it’s about which “pull of the lever” you got. One practical tip from the Silicon Valley approach: run the same task multiple times and pick the best result. It sounds wasteful, but when AI is cheap enough, it’s actually more efficient than trying to get perfection on the first attempt.
Context Window: The Hidden Performance Killer
A community member named Note shared an important insight: MiniMax 2.5 works well with low context, but once you push past the 120K context window, performance drops dramatically — “like talking to ChatGPT 3.5,” as he put it. This is a critical factor that benchmarks don’t capture. In real agent use, context accumulates fast as your bot handles conversations, reads files, and processes tasks throughout the day. You often don’t even know how much context your bot is consuming, and the intelligence degradation is exponential.
This likely explains a lot of the inconsistency we experienced. Early in a session, MiniMax might perform admirably. But as context builds up over hours of use, the quality cliff is steep and sudden.
The Verdict: 60-70%, Not 95%
After weeks of daily use, our gut feeling is that MiniMax 2.5 delivers about 60-70% of what Claude Opus can do — not the 95% claimed in benchmarks. That gap matters enormously when you’re relying on an AI agent for real daily tasks like briefings, research, and automation.
Is Opus worth the premium? If you need reliability and proactive intelligence for mission-critical workflows, absolutely. If you’re experimenting, learning, or running lighter tasks, MiniMax at $20/month is still a solid entry point — just temper your expectations and be prepared to re-run tasks when results aren’t right.
We’re going to keep testing and tuning MiniMax to see if better prompt engineering can close that gap. The model has potential, and the price point is hard to ignore. But for now, when it comes to daily AI agent work in OpenClaw, you really do get what you pay for.