Summary of "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.

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
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
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)
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)
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