GLM 5.1 vs MiniMax M2.7 — The Brutal Coding Test via OpenClaw

GLM 5.1 (Z.ai) vs MiniMax M2.7 (MiniMax) — OpenClaw live coding comparison

Context / setup

• Two identical servers running OpenClaw (TUI) executed the same prompts in real time (no edits). The presenter reused prior setup videos for installation/configuration.
• Goal: a brutal, practical coding comparison across realistic tasks (not toy problems).
• Codebase / tooling involved: vanilla JS + raw SVG, Bezier path animation, COBOL mainframe repo, FastAPI app with async SQLAlchemy, Redis, Celery, Alembic migrations, Docker, and an IOC (threat intelligence) ingestion pipeline.
• Models compared: GLM 5.1 (referred to as Jaifu / Z.ai) and MiniMax M2.7. Both ran in identical environments and prompts via OpenClaw.

Planned tests & outcomes

The session ran a sequence of realistic engineering tasks. Each item below lists what was tested, the outcome, and the winner.

1. Code creation — Draw a world map in raw SVG + Bezier arc animation (vanilla JS, no libraries)

   • What was tested: pure generation from a single prompt, path animation, SVG map accuracy.
   • Outcome:
     • GLM 5.1 produced a more realistic, responsive result (live-feeds, correct-looking world map).
     • MiniMax produced a reasonable attempt but looked less “real-time.”
   • Winner: GLM 5.1

2. COBOL modernization — Translate a JSON parser module from a legacy COBOL Minecraft server to idiomatic Python with tests

   • What was tested: reading unfamiliar legacy codebase, translating to clean Python, following repo patterns.
   • Outcome:
     • GLM 5.1 progressed much faster and produced a substantive translation and refactor, demonstrating strong legacy-code handling.
     • MiniMax was slower on this task.
   • Winner: GLM 5.1

3. Feature addition (FastAPI threat intel platform) — Add a watchlist system (save IOCs to named watchlists + alerting)

   • What was tested: understanding a complex modern stack, adding model/repo/service/schema/routes, Alembic migrations, hooks into the Celery pipeline, full CRUD and alerting flow.
   • Outcome:
     • GLM 5.1: fast, architecturally sound; created layers and a handwritten Alembic migration, implemented alerting and CRUD.
     • MiniMax: more thorough and modular — separated alert routes, a separate alert schema file, explicit Alembic migration with upgrade/downgrade, FK ordering, indexing, and guards to avoid duplicate alerts. Slower and used more tokens.
   • Notes: Token usage — MiniMax ≈ 62K tokens vs GLM ≈ 32K for this task.
   • Winner: MiniMax M2.7 (for architectural completeness and correctness)

4. Bug finding & fixing — Find and fix the single most critical production bug (no hints)

   • What was tested: code comprehension, reasoning, fixes, and secondary issue detection.
   • Outcome:
     • Both models found the same primary bug (silent exception / failure flag in task.py).
     • GLM’s fix: flipped the failure flag; lower token usage and faster, but did not fully utilize retry semantics.
     • MiniMax’s fix: more complete — activated self.retry with exponential backoff, fixed a dead max-retry code path, and also flagged an unrelated JWT ‘nbf’ security issue (secondary bug). Produced deeper output at higher token cost.
   • Winner: MiniMax M2.7 (for depth and additional security discovery)

5. Refactoring — Break apart a “god” module and restructure problematic areas

   • What was tested: high-level architecture, query reduction, making scoring injectable, introducing interfaces/protocols, and efficient upsert vs ORM loops.
   • Outcome:
     • GLM 5.1: split the god module into focused files (ingestion, bulk ingestion, context loading, facade), kept routes unchanged, and made scoring injectable. Clean and fast but shallower.
     • MiniMax: took a more senior-engineer approach — created a package, formal protocol interfaces, injectable classes, used native upsert for bulk paths, and caught more issues. More comprehensive structural changes.
   • Winner: MiniMax M2.7

Overall analysis / verdict

• GLM 5.1 strengths

  • Fast and decisive.
  • Creative on a blank canvas; excellent for generating UI-style code.
  • Strong at translating legacy COBOL into modern Python quickly.
  • Lower token usage and quicker turnaround for many tasks.

• MiniMax M2.7 strengths

  • Deeper reasoning and stronger software-engineering patterns.
  • Better architectural choices, more thorough migrations and bug fixes.
  • More likely to find additional issues beyond the immediate ask.
  • Produces more comprehensive, modular, and production-ready solutions (at the cost of speed and tokens).

• Trade-offs

  • MiniMax typically used more tokens and was slower but produced higher-depth, production-ready outputs.
  • GLM was faster and lower-cost in tokens, and excelled on raw generation and creative tasks.

• Recommendation

  • Choose GLM 5.1 for rapid creative coding, blank-canvas tasks, or fast legacy translation.
  • Choose MiniMax M2.7 when you need deeper architectural reasoning, robust migrations, and thorough bug fixes/refactors.

Guides / tutorials referenced

• The presenter mentioned separate videos on the channel showing how to install and configure OpenClaw and MiniMax (setup guides for running these tests).

Main speakers / sources

• Presenter / YouTuber (unnamed narrator conducting the live tests)
• Models: GLM 5.1 (Z.ai / Jaifu) and MiniMax M2.7 (MiniMax)
• Tools / systems used: OpenClaw (TUI), Docker, FastAPI, async SQLAlchemy, Redis, Celery, Alembic, legacy COBOL codebase, vanilla JS + SVG/Bezier animation