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Z.ai GLM 5 VS MiniMax MiniMax M3

GLM 5 vs MiniMax M3: which wins at real work?

22 task areas · same graded test runs · rank comparison only, so 0–100 and Elo collections never mix raw scores.

MiniMax M3 wins 15 of 22 task areas we tested; GLM 5 takes 7. MiniMax M3 costs 1.7× less per token ($1.5 vs $2.52 per 1M).

7
Task areas won
15
49
Avg percentile
65
0
Top-3 finishes
4
$2.52
Price / 1M tokens
$1.5
Z.ai
Provider
MiniMax

MiniMax M3 costs 1.7× less per token ($1.5 vs $2.52 per 1M).

Task by task

Task area GLM 5 MiniMax M3 Winner
Landing Pages #69 / 72
Needs editing
#2 / 72
Strong
MiniMax M3
Sales #68 / 110
Usable
#2 / 110
Strong
MiniMax M3
Frontend & Landing Pages #76 / 109
Weak
#20 / 109
Needs editing
MiniMax M3
Product & Project Management #58 / 110
Strong
#3 / 110
Excellent
MiniMax M3
Investor & Pitch #62 / 66
Usable
#12 / 66
Strong
MiniMax M3
Customer Support #35 / 113
Strong
#83 / 113
Usable
GLM 5
Knowledge & Docs #24 / 110
Strong
#67 / 110
Usable
GLM 5
Data & Analytics #43 / 110
Excellent
#1 / 110
Excellent
MiniMax M3
AI Strategy #66 / 126
Strong
#27 / 126
Strong
MiniMax M3
Content & Brand #60 / 124
Strong
#28 / 124
Strong
MiniMax M3
Summarization & Meeting Notes #48 / 110
Excellent
#79 / 110
Excellent
GLM 5
Structured Output #94 / 113
Strong
#65 / 113
Strong
MiniMax M3
Translation & Localization #26 / 110
Excellent
#55 / 110
Excellent
GLM 5
RAG, Safety & Grounding #47 / 113
Excellent
#72 / 113
Excellent
GLM 5
Training & Education #67 / 110
Strong
#42 / 110
Excellent
MiniMax M3
Executive Assistant #62 / 112
Strong
#39 / 112
Strong
MiniMax M3
Presentations & Decks #49 / 110
Excellent
#29 / 110
Excellent
MiniMax M3
Chef / Home Cooking #57 / 126
Usable
#74 / 126
Usable
GLM 5
Creative & Comedy #72 / 110 #55 / 110 MiniMax M3
Research & Competitive Analysis #48 / 110
Strong
#33 / 110
Strong
MiniMax M3
Coding #36 / 115
Strong
#49 / 115
Strong
GLM 5
Legal & HR #37 / 110
Excellent
#34 / 110
Excellent
MiniMax M3

Rank = position among every model config we tested in that task area (lower is better). Sorted by biggest gap first.

Same task, both models — judged

Both models answered the same test case; an independent judge graded each. Quotes are the judge's actual rationale.

Investor & Pitch

Global education market TAM (Market Sizing Reality Check)
GLM 5 44/100

“The model provides a strong, realistic critique of the top-down TAM fallacy and good wedge framing, but the output cuts off mid-sentence and completely fails to include the requested data to gather and revised deck version.”

MiniMax M3 94/100

“The response is expert-level. It provides a highly realistic critique of top-down TAMs, specific and accurate industry comparables, a perfectly tailored data gathering list, and a rigorous bottom-up revised slide. It leaves almost no room for improvement.”

Frequently asked

Is GLM 5 better than MiniMax M3?

Across 22 task areas we benchmarked, MiniMax M3 ranks higher in 15 and GLM 5 in 7.

Which is cheaper, GLM 5 or MiniMax M3?

MiniMax M3 costs 1.7× less per token ($1.5 vs $2.52 per 1M).

What is GLM 5 better at?

GLM 5 out-ranks MiniMax M3 at Customer Support, Knowledge & Docs, Summarization & Meeting Notes.

What is MiniMax M3 better at?

MiniMax M3 out-ranks GLM 5 at Landing Pages, Sales, Frontend & Landing Pages.

Full GLM 5 review → Full MiniMax M3 review → Full model leaderboard →

More comparisons

This page is Spring Prompt, running in public

We just did this for every model. Do it for your prompt.

The rankings above come from running real tasks through real models and scoring every output. Spring Prompt is that same engine — pointed at your prompt, your test cases, and your definition of good.

  • Generate test cases from your prompt — no eval set required to start.
  • Compare models side by side with quality, cost and latency in one matrix.
  • Optimise the winner until the scores say it's ready to ship.
Experiment · Cold outreach email

Prompt × model results

12 test cases · 3 evals
Claude Opus
GPT-5
Gemini
v1
7.1
6.8
7.4
v2
8.3
7.9
8.0
v3
9.2
8.6
8.4
Best combo: v3 × Claude Opus
9.2 quality · $0.004/run · 1.8s