Boxmining v2

Tag: minimax

  • Chinese AI Labs ARE COPYING Claude?! Anthropic’s Distillation Bombshell

    Chinese AI Labs ARE COPYING Claude?! Anthropic’s Distillation Bombshell

    Anthropic just dropped a bombshell — and the AI community is having a field day with it. The company behind Claude is publicly accusing three major Chinese AI labs of running massive “distillation attacks” against their model. And honestly? The reaction has been anything but sympathetic.

    What Anthropic Is Claiming

    According to Anthropic’s official blog post, DeepSeek, Moonshot AI (the makers of Kimi), and MiniMax allegedly created over 24,000 fake accounts and generated more than 16 million queries against Claude. The goal? To extract Claude’s “secret sauce” — specifically its capabilities in agentic reasoning, tool use, and coding — and use that knowledge to train their own models.

    This technique is called model distillation. It’s actually a legitimate training method that AI labs use on their own models to create smaller, more efficient versions. But when you do it to a competitor’s model at industrial scale, that’s a different story entirely.

    The Scale Is Staggering

    The numbers Anthropic shared are pretty wild. According to TechCrunch, DeepSeek was tracked with over 150,000 exchanges focused on foundational logic and alignment — particularly around finding censorship-safe alternatives to policy-sensitive queries. Moonshot AI racked up 3.4 million exchanges targeting agentic reasoning, coding, and computer vision. But MiniMax was the biggest offender with 13 million exchanges, and Anthropic says they actually watched MiniMax redirect nearly half its traffic to siphon capabilities from the latest Claude model the moment it launched.

    Think about it this way: Anthropic has likely spent billions of dollars training Claude. These Chinese labs potentially replicated significant chunks of that capability for a fraction of the cost — maybe tens of thousands of dollars in API fees. That’s quite the ROI.

    Why This Isn’t Surprising

    If you’ve been following the Chinese AI scene, none of this should shock you. We’ve covered MiniMax and Kimi extensively on this channel, and their performance is genuinely impressive — roughly 95% of Claude’s capability at a fraction of the cost. MiniMax offers comparable performance at about 5% of the price. That kind of rapid improvement had to come from somewhere.

    China has a long history of building parallel ecosystems inspired by Western platforms. Taobao for eBay/Amazon, Weibo for Twitter (there are actually four Twitter clones in China), WeChat for everything else. The AI space is just the latest frontier, and the stakes are astronomically higher.

    The Internet Clapped Back Hard

    Here’s where it gets spicy. Anthropic is calling for “rapid, coordinated action among industry players, policy makers, and the broader AI community” to address these attacks. But the AI community’s response has been… let’s say unsympathetic.

    The backlash centers on one word: hypocrisy. Anthropic, now valued at a staggering $380 billion, is itself facing multiple lawsuits accusing the company of illegally using copyrighted internet data to train Claude. Even Elon Musk weighed in, pointing out that Anthropic allegedly settled a $1.5 billion lawsuit related to training Claude on copyrighted books. Someone even demonstrated that Claude could reproduce roughly 95% of Harry Potter books when prompted — suggesting Anthropic dumped massive amounts of copyrighted material into their training data.

    As many in the community put it: it’s “circle stealing.” Everyone’s copying from everyone. The Chinese labs at least paid for API access — the millions of writers whose work was scraped to train Claude weren’t given that courtesy.

    The Bigger Picture: Who Actually Wins?

    Here’s my take on why this whole situation is actually good for us. All this competition — whether through legitimate research or questionable distillation — is driving costs down dramatically. We no longer have to shell out thousands of dollars for top-tier AI access. Sure, Anthropic’s Opus 6 is still expensive, but when MiniMax gives you 95% of the performance at 5% of the cost, that’s massive savings for developers and businesses.

    And the race is far from over. DeepSeek is reportedly preparing to release V4, which could outperform both Claude and ChatGPT in coding tasks. Meanwhile, Moonshot just released Kimi K2.5 and a new coding agent last month.

    From our internal testing, Opus still has an edge. It’s appreciably smarter on logic tasks — like knowing you should drive to a car wash rather than walk (MiniMax still gets that wrong about 30% of the time, while Opus nails it 95% of the time). But whether that intelligence gap is worth paying 20x more is a question every developer has to answer for themselves.

    What Happens Next

    This story ties directly into the broader US-China AI rivalry. The Trump administration recently allowed Nvidia to export advanced H200 chips to China, and Anthropic is now arguing that distillation attacks “reinforce the rationale for export controls” since restricted chip access would limit both direct model training and the scale of these extraction campaigns.

    One thing that makes this race particularly interesting: AI doesn’t care what language you speak. You can paste a Chinese API, a Chinese website, and your AI tools will work with it seamlessly. The global push toward AGI is accelerating from all directions, and the competition between US and Chinese labs is only going to intensify.

    China produces more engineers per year than the US simply due to population scale, and those developers are feeding data back into Chinese models just as Western developers improve Claude through their interactions with it. This isn’t the first shot fired in this AI arms race, and it certainly won’t be the last.

    Whether you think this is good or bad for the industry, one thing’s clear: we’re all benefiting from cheaper, more capable AI as a result. And that’s something worth watching closely.

  • MaxClaw Guide: Free OpenClaw with MiniMax 2.5 — No Server Required

    MaxClaw Guide: Free OpenClaw with MiniMax 2.5 — No Server Required

    If you’ve been following my OpenClaw journey, you know I’ve been a big fan of setting up AI agents on cheap cloud servers. But MiniMax just dropped something that makes the whole process even easier — and completely free. It’s called MaxClaw, and it’s basically OpenClaw running in the cloud with MiniMax’s M2.5 model, ready to go out of the box. No servers, no API keys, no deployment headaches.

    What Is MaxClaw?

    MaxClaw is MiniMax’s new cloud-based AI assistant that combines three things: OpenClaw’s open-source agent framework, MiniMax’s own agent infrastructure, and their latest M2.5 model. The result is a fully managed OpenClaw instance that runs 24/7 without you having to touch a terminal.

    For context, in my previous guide, I walked through setting up OpenClaw on a Zeabur server with MiniMax M2.5 — choosing a server, installing via command line, configuring API keys, the whole thing. It worked great and only cost about $20 a month, but it still required some technical know-how. MaxClaw removes all of that friction entirely.

    Why This Is a Big Deal

    The biggest selling point is simplicity. With MaxClaw, there’s no deployment to handle and no extra API costs. MiniMax is hosting everything for you, and the M2.5 model is included. You just sign up through the MiniMax Agent web interface and you’re running.

    What makes this particularly interesting is the platform support. MaxClaw works across Telegram, WhatsApp, Slack, and Discord right out of the box. Previously, connecting OpenClaw to messaging platforms required additional configuration — setting up bot tokens, configuring webhooks, and making sure your server stayed online. MaxClaw handles all of that automatically with 24/7 uptime.

    MiniMax also launched this alongside their Expert 2.0 upgrade, which means you get access to their ready-made Expert ecosystem. These are pre-built specialized agents that can handle specific tasks, and they integrate directly into MaxClaw without any extra setup.

    How MiniMax M2.5 Stacks Up

    For those unfamiliar, MiniMax M2.5 is a seriously capable model. It’s available on platforms like Ollama for local use, and it performs well on coding benchmarks like SWE-Bench Verified. The model supports both text and code tasks, making it versatile for the kind of agent work OpenClaw excels at — web searches, data scraping, task automation, and more.

    What’s impressive is the cost-to-performance ratio. When I was running M2.5 through the API on my own server, it was already one of the cheapest options for the intelligence level you get. With MaxClaw, that cost drops to zero since MiniMax is absorbing the compute costs. Whether this stays free forever remains to be seen, but right now it’s an incredible deal.

    Getting Started with MaxClaw

    The setup process is dramatically simpler than the manual route I covered before. Here’s the gist:

    1. Head to the MiniMax Agent web interface
    2. Look for the MaxClaw option — it’s integrated directly into the platform
    3. Connect your preferred messaging platform (Telegram, Discord, WhatsApp, or Slack)
    4. Start chatting with your AI agent

    That’s it. No server provisioning, no SSH terminals, no PATH exports, no API key juggling. The whole thing takes minutes instead of the 15+ minutes my previous setup required.

    What Can You Actually Do With It?

    Since MaxClaw is built on OpenClaw, you get the full range of agent capabilities. Web searching, browsing, file management, code execution — all the tools that make OpenClaw powerful are available here. The upgraded built-in tools that MiniMax added make it even more capable for real work tasks.

    You also get access to the MiniMax Expert ecosystem, which adds specialized agents on top of the base capabilities. Think of it as having pre-configured skills that your agent can tap into without you having to build them from scratch.

    For beginners especially, this is the easiest on-ramp to AI agents I’ve seen. You don’t need to understand Linux, cloud servers, or command-line tools. You just need a MiniMax account and a messaging app.

    Should You Switch from a Manual Setup?

    If you already have OpenClaw running on your own server, MaxClaw isn’t necessarily a replacement — it’s more of a complement. Running your own instance gives you full control over configuration, data, and which models you use. MaxClaw trades that control for convenience and zero cost.

    For anyone who hasn’t set up OpenClaw yet, though, MaxClaw is the obvious starting point. Try it for free, see if AI agents fit your workflow, and then decide if you want to invest in a more customized setup later.

    Final Thoughts

    MaxClaw is exactly the kind of move I was hoping to see in the AI agent space — taking powerful open-source tools and making them accessible to everyone. MiniMax combining their M2.5 model with OpenClaw’s framework and hosting it for free is a strong play that lowers the barrier to entry significantly.

    I’ll be doing more deep dives into MaxClaw’s capabilities in upcoming videos, including how to set up advanced integrations and get the most out of the Expert ecosystem. If you want to follow along, make sure to subscribe to @BoxminingAI and join our Discord community for tips and discussions.

  • 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.

  • Cheap AI vs Premium AI: MiniMax 2.5 vs Claude Opus (Full Breakdown for OpenClaw Users)

    Cheap AI vs Premium AI: MiniMax 2.5 vs Claude Opus (Full Breakdown for OpenClaw Users)

    If you’re running OpenClaw and wondering whether you really need to pay for Claude Opus — or whether a cheap MiniMax plan can do the job — this breakdown is for you. We ran real tests, compared costs, and came to a clear conclusion: cheap AI can work, but it comes with a catch.

    The Test Setup — Multi-Agent OpenClaw in Action

    Meet our Agents: Stark, Banner, and Jeff

    The test uses a real multi-agent OpenClaw setup with three agents running simultaneously — Stark, Banner, and Jeff — each powered by different models. This isn’t a synthetic benchmark. It’s a live production environment where the agents handle real tasks every day.

    The Logic Test: Walk or Drive to the Car Wash?

    The benchmark is deceptively simple: a car wash is 50 metres away — do you walk or drive? It’s a common-sense reasoning test that exposes how well a model handles real-world context, implicit assumptions, and practical decision-making. The answer seems obvious, but AI models handle it very differently.

    MiniMax 2.5 vs Claude Opus — Performance Comparison

    Consistency Is the Key Metric

    The biggest difference between cheap and premium models isn’t raw intelligence — it’s consistency. MiniMax 2.5 can produce excellent results, but it also overthinks variables, introduces unnecessary complexity, and occasionally slips on straightforward logic. Opus fails rarely, but when it does fail, it can fail in a big, hard-to-catch way.

    The Inconsistency Problem with Cheap Models

    MiniMax 2.5 and Kimi are fast and affordable, but they require more manual oversight. You can’t fully trust them to run autonomously without checking their work. For tasks where mistakes are costly — financial decisions, automated publishing, customer-facing responses — that inconsistency is a real risk.

    When Opus Fails, It Fails Hard

    Claude Opus has a much lower failure rate, but its failures tend to be more dramatic when they do occur. This is worth understanding: a cheap model that fails 10% of the time in small ways may actually be easier to manage than a premium model that fails 1% of the time in catastrophic ways, depending on your use case.

    Cost vs Performance — Is Opus Worth 20x the Price?

    MiniMax Pricing Breakdown

    MiniMax offers subscription plans that are dramatically cheaper than Claude Opus — roughly 20x less expensive per request. For high-volume, low-stakes tasks (summarising content, drafting social posts, processing data), this price difference is hard to ignore.

    • MiniMax 2.5 plan: affordable tiered pricing with generous request limits

    • 10% off via referral: https://platform.minimax.io/subscribe/coding-plan?code=5GYCNOeSVQ&source=link

    The Real Cost of Cheap AI — Manual Oversight

    The hidden cost of cheap models is your time. If you’re manually reviewing every output, correcting mistakes, and re-running failed tasks, the “cheap” model starts looking expensive. The true cost calculation has to include your oversight hours, not just API fees.

    Who Should Pay for Opus?

    Opus makes sense when:

    • You’re running fully autonomous agents with minimal human review

    • Mistakes have real consequences (financial, reputational, customer-facing)

    • You’ve already built systems and just need reliable execution

    MiniMax/Kimi makes sense when:

    • You’re still building and testing your setup

    • You have manual review in your workflow

    • You’re doing high-volume grunt work (research, drafts, data processing)

    The Hybrid Approach — Best of Both Worlds

    Use Opus for Architecture, Cheap Models for Execution

    The smartest approach, suggested by viewers and confirmed in testing: use Claude Opus for planning, architecture, and critical decisions — then hand off execution tasks to MiniMax or Kimi. One viewer described it perfectly: “Use Opus for architecture and planning, Kimi to generate the code and verify it, then Opus to fit the code gap against the specifications.”

    Kimi 2.5 as a MiniMax Alternative

    Kimi 2.5 is another strong contender in the cheap-but-capable category. Multiple OpenClaw users report running it successfully as their primary model. It’s particularly strong on reasoning tasks where MiniMax tends to overthink.

    • Kimi referral: https://www.kimi.com/kimiplus/sale?activity_enter_method=h5_share&invitation_code=Y4JW7Y

    OpenClaw Model Strategy — Practical Recommendations

    Turn Reasoning Mode On for Cheap Models

    A key tip from the comments: always enable reasoning mode when using MiniMax or Kimi on OpenClaw. It significantly improves output quality and reduces the inconsistency problem.

    Should Each Agent Have Its Own Model?

    A common question from new OpenClaw users: should each agent run a different LLM? The answer is yes — and this video demonstrates exactly why. Different agents have different roles, and matching the model to the task (cheap for grunt work, premium for critical decisions) is the optimal strategy.

    The Journey from MiniMax 2.1 to Near-Autonomy

    The video covers a personal journey from frustrating early experiences with MiniMax 2.1 to a near-autonomous multi-agent setup. The key insight: the model matters less than the systems you build around it. Good prompts, clear memory structures, and well-defined agent roles can make a cheap model punch above its weight.

    Verdict — Cheap AI vs Premium AI for OpenClaw

    MiniMax can be great value but inconsistent. Opus rarely fails — but when it does, it fails hard. The winning strategy is hybrid: cheap models for execution, Opus for architecture and critical decisions.

    1. • Zeabur hosting (save $5 with code boxmining): https://zeabur.com/
    2. • MiniMax 10% off: https://platform.minimax.io/subscribe/coding-plan?code=5GYCNOeSVQ&source=link
    3. • Kimi AI: https://www.kimi.com/kimiplus/sale?activity_enter_method=h5_share&invitation_code=Y4JW7Y
    4. • More AI news: https://www.boxmining.com/
    5. • Join Discord: https://discord.com/invite/boxtrading
    6. •Watch the full video: https://youtu.be/1naLl0IwuPM
  • Why Minimax 2.5 is a Game-Changer for AI Agents

    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.

  • OpenClaw Setup Guide: The Cheapest Way Using the Latest MiniMax M2.5 Model

    OpenClaw Setup Guide: The Cheapest Way Using the Latest MiniMax M2.5 Model

    In this guide, I’ll walk you through an affordable and straightforward way to get OpenClaw up and running with the cutting-edge MiniMax 2.5 model. We also have a full video guide if you need visual assistance.

    Why This Setup? A Quick Intro

    OpenClaw is an fantastic open-source AI agent framework that allows you to build and run autonomous AI tasks. The beauty of this approach is its sandboxed nature—you can test and play around without exposing your main computer to potential issues. Instead of splurging on something like a Mac Mini, we’ll use a cheap cloud server from Zeabur combined with the MiniMax 2.5 model, which costs about $20 a month for solid performance.

    This method is ideal for beginners because it’s simple, low-risk, and scalable. Plus, MiniMax 2.5 offers high intelligence at a fraction of the cost of bigger models. If you’re new to AI like me, starting here means you can focus on learning without overwhelming setup hurdles. Ready? Let’s choose your server.

    Step 1: Choosing the Right Server

    The key to keeping costs down is selecting an accessible and affordable hosting provider. I recommend Zeabur over more complex options like Digital Ocean or AWS—it’s user-friendly and perfect for quick setups.

    Here’s how to get started:

    1. Head to Zeabur’s website and create an account.
    2. Set up a new server with minimal specs: 2GB RAM and 40GB storage. This should cost you less than $2 per month.
    3. Choose a server region close to you for better speed—for example, Singapore if you’re in Asia.
    4. Once created, you’ll get an IP address, username (usually “Ubuntu”), and password.

    To connect to your server, use a terminal app like Termius. Enter the IP, username, and password, and you’re in! This remote setup keeps everything isolated, so you can experiment freely.

    Step 2: Installing OpenClaw

    With your server ready, installation is a breeze. OpenClaw’s official site makes it easy with a one-line command for Linux.

    Follow these steps:

    1. Go to openclaw.ai and find the “Max Linux” installation section.
    2. Copy the provided command (it’ll look something like a curl or wget script to download and install).
    3. In your server terminal, paste the command. On a Mac, use Shift+Ctrl+V; on other systems, try Command+V or right-click paste.
    4. The process takes about 2-3 minutes. Sit back and let it run.

    If you encounter a “warn path missing” error after installation, fix it with this command:

    export PATH=$PATH:/path/to/openclaw

    (Replace /path/to/openclaw with the actual installation path if needed.)

    During setup, you’ll be prompted to choose a model. Select MiniMax 2.5—it’s powerful and included in affordable plans. You’ll need a MiniMax API key; I suggest the coding plan, which gives you 300 prompts over 5 hours for testing. Input your key when asked.

    Pro Tip: If you mess up the initial setup, run openclaw onboard to restart the process fresh.

    Step 3: Configuring OpenClaw for Optimal Use

    Once installed, access the Terminal User Interface (TUI) with:

    openclaw TUI

    This interface lets you interact with your AI agent directly.Key configuration tips:

    • Stick with MiniMax M2.5 (avoid Lightning if it’s not in your plan).
    • Use openclaw configure to tweak settings like models, gateways, or skills.
    • For now, focus on basic setup. In future guides, I’ll cover integrations like connecting to Telegram or Discord for threaded conversations (which I prefer over TUI for better organization).

    Your OpenClaw AI can now handle tasks like web searches, Twitter (X) data scraping, managing shared notes, and even task automation. Over time, you can train it for more personalized responses. Remember, keep it isolated initially to protect your personal data—security first!

    Common Troubleshooting Commands:

    • openclaw onboard: Reset and restart setup.
    • openclaw configure: Adjust models, skills, or connections.

    Wrapping Up: Next Steps and Final Thoughts

    There you have it—a complete, budget-friendly guide to setting up OpenClaw with MiniMax 2.5. This setup has been a game-changer for me, allowing hands-on AI experimentation without the high costs or risks. In under 15 minutes, you’ll have a running AI agent ready for action.

    If you run into issues or want to dive deeper, check out my Discord community for tips and discussions: Join here. Upcoming videos will cover advanced topics like Telegram/Discord bots, fixing common errors, and even more integrations.

    If you’re enjoying this journey into AI, subscribe to my channel @BoxminingAI for more beginner-friendly guides on vibe coding, AI models, and tools.