MiniMax M2.5 vs GLM-5: Battle Between Cheap and Cheaper
In this video, I compare MiniMax 2.5 vs GLM-5 in a real, practical “AI coding agent” workflow—where the goal isn’t perfection on the first prompt, but iteration: fixing bugs, clarifying intent, and pushing the model through real friction.
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I’m testing what actually matters when you use LLMs for development:
• How well they follow instructions
• Whether they debug their own mistakes
• How often they hallucinate vs course-correct
• How smooth the overall flow feels inside an agentic coding environment
• And the big question: cost vs reliability (time is money)
To keep it grounded, I use the same project prompt for both models and then drive them through the same kinds of real-world issues: Docker setup problems, broken flows, UX edge cases, and “you didn’t do what I asked” moments—until we reach a stable result.
Key takeaways
• MiniMax 2.5 is shockingly cheap for what it can do, but tends to require more babysitting and can go down the wrong path longer.
• GLM-5 is more expensive (still far below frontier pricing), but tends to align better with intent and produce smoother iteration—when it’s not rate-limited.
If you’re choosing a model for coding agents—or designing a stack where a stronger model supervises cheaper workers—this video should help you understand the real trade-offs.
Chapters
0:00 Intro
2:36 Build brief + setup (Next.js, Docker Compose, auth/seeded user, routing/cost tracking)
5:32 Early build results + GLM rate-limit + initial app issues
16:47 MiniMax Docker/DB failure investigation → root cause + recovery
20:07 Feature/UX showdown: study flow + deck/card management (GLM vs Miniax)
24:54 Some bad news
26:33 MiniMax UI review after iterations
32:35 GLM UI review after iterations
41:49 Final review + verdict (cost, correctness, babysitting, validation/testing) + wrap-up
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