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MiniMax M2.5 vs GLM-4.7

A detailed comparison of MiniMax M2.5 (MiniMax) and GLM-4.7 (Zhipu AI) across pricing, performance, and features.

Pricing Comparison

MetricMiniMax M2.5GLM-4.7Difference
Input / 1M tokens$0.30$0.60+100%
Output / 1M tokens$1.20$2.20+83%
Context window200K200K
Max output128K128K

Benchmark Comparison

BenchmarkMiniMax M2.5GLM-4.7
MMLU-Pro82%84.3%
HumanEval90%
GPQA85.7%

Capabilities

CapabilityMiniMax M2.5GLM-4.7
code
reasoning
text
vision

MiniMax M2.5 Strengths

  • Frontier quality at budget pricing ($0.30/$1.20)
  • 80.2% SWE-Bench Verified — among the best
  • Open-source (MIT) with 10B active params — easy to run

MiniMax M2.5 Weaknesses

  • Text-only — no vision or audio
  • No tool-use support
  • Newer provider — smaller ecosystem

GLM-4.7 Strengths

  • Excellent value — strong benchmarks at $0.60/$2.20
  • Open-weight (MIT license)
  • Top scores on AIME 25 and BrowseComp

GLM-4.7 Weaknesses

  • No tool-use support yet
  • 358B parameters — still heavy for self-hosting
  • Smaller ecosystem than OpenAI/Anthropic

Quick Verdict

Best value: MiniMax M2.5 is the more affordable option at $0.3/$1.2 per 1M tokens.

Higher benchmarks: MiniMax M2.5 scores higher on average across available benchmarks (86.0% avg).

Choose MiniMax M2.5 if cost matters most. Choose GLM-4.7 if you need the best possible quality for complex tasks.

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