Boxmining v2

Tag: claude opus

  • MiniMax 2.5 vs Claude Opus: Which AI Model Is Best for OpenClaw?

    MiniMax 2.5 vs Claude Opus: Which AI Model Is Best for OpenClaw?

    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.

  • Why Your OpenClaw Agent Gets DUMB (Context Window Explained)

    Why Your OpenClaw Agent Gets DUMB (Context Window Explained)

    If you’ve been running an OpenClaw agent and noticed it getting progressively dumber throughout the day, you’re not alone. In this video, we break down exactly why this happens and what you can do about it. It all comes down to one thing: the context window.

    What Is the Context Window?

    Think of the context window as your AI agent’s short-term memory — its working brain. Every message you send, every file it reads, every task it processes takes up space in that window. It’s measured in tokens (roughly 4 characters per token), and every model has a hard limit.

    The best analogy is a human assistant who’s been given too many tasks at once. Tell them to handle your car, your house, your parents visiting, your dinner reservations — at some point they get overloaded and start dropping balls. That’s exactly what happens to your AI agent when the context window fills up.

    Research backs this up too. A 2025 study by Chroma Research called “Context Rot” tested 18 different LLMs and found that models do not use their context uniformly — their performance grows increasingly unreliable as input length grows. Even for simple tasks, LLMs exhibit inconsistent performance across different context lengths. The longer the context, the worse the reasoning gets, especially for multi-step problems.

    Why Your Agent Wakes Up Already Loaded

    Here’s something that surprised us. Every day, OpenClaw essentially kills your agent and restarts it fresh. It wakes up, reads its long-term memory files (your SOUL.md, MEMORY.md, AGENTS.md, and other config files), and loads all of that into the context window. It’s like an assistant coming to work, reading their briefing notes, and getting up to speed.

    The problem? If you’ve stuffed those files with your life story, your preferences, your childhood memories, and every random thought you’ve ever had — your agent wakes up with a context window that’s already half full before it’s done a single task.

    In our test, Jeff (running on MiniMax 2.5) woke up at the start of the day already at 136K tokens. That’s because in the early days, the common advice was to “blast your agent with your life story so it understands you better.” Turns out, that’s actually counterproductive. All that irrelevant context is eating into the space your agent needs for actual work.

    Cheap Models Get Hit Harder

    Not all models handle large context equally. We compared two setups side by side:

    Stark running on Claude Opus — woke up at around 100K out of 200K capacity, and still performed fluidly. Opus is genuinely good at working with large context windows and maintaining quality throughout.

    Jeff running on MiniMax 2.5 — started struggling almost immediately. As one of our viewers, Note, put it: “The moment you go above 120K context window, it feels like I’m talking to ChatGPT 3.5.”

    There’s a hidden reason for this beyond just model quality. To save costs, cheaper models like MiniMax aggressively dump parts of the context they consider unimportant. This is an internal optimization to reduce compute costs — but sometimes what they dump is actually critical to your task. You might ask it to make a presentation and halfway through it forgets what the presentation is even about.

    This aligns with what researchers have found: relevant information buried in the middle of longer contexts gets degraded considerably, and lower similarity between questions and stored context accelerates that degradation.

    How to Keep Your Agent Smart

    Based on our testing, here are the practical tips that actually work:

    1. Trim your memory files. Go through your SOUL.md, USER.md, and other long-term storage files. Remove anything that isn’t directly relevant to the tasks you need your agent to do. Your agent doesn’t need to know your life story — it needs to know how to do its job.

    2. Specialize your agent. AI models actually gravitate toward specialization. Instead of making your agent a general-purpose assistant that handles everything from dinner reservations to research reports, train it for specific tasks. In our test, Stark was trained specifically for making presentations and research — and it delivered significantly better results than Jeff, who was loaded with general life context.

    3. Monitor your context usage. You can simply ask your agent “How much context are you using?” and it’ll tell you. On the OpenClaw terminal, it sometimes displays this automatically. Keep an eye on it throughout the day.

    4. Clear context when needed. If you feel your agent getting dumber, start a new session. This kills the current context and lets the agent restart fresh. There’s also a natural compacting stage where the agent automatically summarizes and compresses older context — similar to how your own brain forgets the details of brushing your teeth but remembers the important meeting you had.

    5. Choose your model wisely. If you’re on a budget with MiniMax or other Chinese models, context management becomes even more critical. These models aggressively optimize to save compute, which means they’ll cut corners on context retention. If you can afford it, models like Claude Opus handle large context windows much more gracefully.

    The Bottom Line

    Context window management is probably the single most impactful thing you can do to improve your OpenClaw agent’s performance. It’s not about giving your agent more information — it’s about giving it the right information and keeping that working memory clean.

    The takeaway is simple: less irrelevant context equals a smarter agent. Trim the fat from your memory files, specialize your agent’s purpose, and don’t be afraid to restart sessions when things get sluggish. Your agent will thank you — by actually being useful.