Compare · ModelsLive · 2 picked · head to head
GLM 4.7 vs MiniMax M2.5
Side by side · benchmarks, pricing, and signals you can act on.
Winner summary
GLM 4.7 wins on 14/22 benchmarks
GLM 4.7 wins 14 of 22 shared benchmarks. Leads in arena · general · reasoning.
Category leads
agentic·MiniMax M2.5arena·GLM 4.7general·GLM 4.7coding·MiniMax M2.5reasoning·GLM 4.7language·MiniMax M2.5math·MiniMax M2.5knowledge·GLM 4.7
Hype vs Reality
Attention vs performance
GLM 4.7
#152 by perf·#3 by attention
MiniMax M2.5
#131 by perf·#11 by attention
Best value
MiniMax M2.5
2.2x better value than GLM 4.7
GLM 4.7
32.9 pts/$
$1.40/M
MiniMax M2.5
72.0 pts/$
$0.68/M
Vendor risk
Mixed exposure
One or more vendors flagged
z-ai
private · undisclosed
MiniMax
$4.0B·Tier 1
Head to head
22 benchmarks · 2 models
GLM 4.7MiniMax M2.5
APEX-Agents
MiniMax M2.5 leads by +3.1
APEX-Agents · evaluates AI agents on complex, multi-step tasks requiring planning, tool use, and autonomous decision-making in realistic environments.
GLM 4.7
3.1
MiniMax M2.5
6.2
Chatbot Arena Elo · Coding
GLM 4.7 leads by +48.2
GLM 4.7
1434.5
MiniMax M2.5
1386.3
Chatbot Arena Elo · Overall
GLM 4.7 leads by +50.7
GLM 4.7
1441.5
MiniMax M2.5
1390.8
Cl Bench
GLM 4.7 leads by +4.5
GLM 4.7
15.9
MiniMax M2.5
11.4
Cl Bench Life
GLM 4.7 leads by +4.6
GLM 4.7
10.9
MiniMax M2.5
6.3
LiveBench · Agentic Coding
MiniMax M2.5 leads by +10.0
GLM 4.7
41.7
MiniMax M2.5
51.7
LiveBench · Coding
GLM 4.7 leads by +2.4
GLM 4.7
73.1
MiniMax M2.5
70.7
LiveBench · Data Analysis
GLM 4.7 leads by +5.6
GLM 4.7
55.2
MiniMax M2.5
49.6
LiveBench · If
MiniMax M2.5 leads by +21.6
GLM 4.7
35.7
MiniMax M2.5
57.2
LiveBench · Language
GLM 4.7 leads by +10.1
GLM 4.7
65.2
MiniMax M2.5
55.1
LiveBench · Mathematics
MiniMax M2.5 leads by +1.4
GLM 4.7
76.0
MiniMax M2.5
77.4
LiveBench · Overall
MiniMax M2.5 leads by +2.1
GLM 4.7
58.1
MiniMax M2.5
60.1
LiveBench · Reasoning
GLM 4.7 leads by +0.4
GLM 4.7
59.7
MiniMax M2.5
59.3
OpenCompass · AIME2025
GLM 4.7 leads by +9.2
GLM 4.7
95.4
MiniMax M2.5
86.2
OpenCompass · GPQA-Diamond
GLM 4.7 leads by +2.3
GLM 4.7
86.9
MiniMax M2.5
84.6
OpenCompass · HLE
GLM 4.7 leads by +3.2
GLM 4.7
25.4
MiniMax M2.5
22.2
OpenCompass · IFEval
MiniMax M2.5 leads by +0.9
GLM 4.7
90.2
MiniMax M2.5
91.1
OpenCompass · LiveCodeBenchV6
GLM 4.7 leads by +10.2
GLM 4.7
83.8
MiniMax M2.5
73.6
OpenCompass · MMLU-Pro
GLM 4.7 leads by +2.3
GLM 4.7
84.0
MiniMax M2.5
81.7
PostTrainBench
MiniMax M2.5 leads by +2.0
GLM 4.7
7.5
MiniMax M2.5
9.5
Proofbench
GLM 4.7 leads by +2.0
GLM 4.7
6.0
MiniMax M2.5
4.0
Terminal Bench
MiniMax M2.5 leads by +9.3
Terminal-Bench 2.0 · evaluates AI agents on real terminal-based coding tasks · writing scripts, debugging, running tests, and managing projects entirely through command-line interaction. Tests both code quality and terminal fluency. Claude Opus 4.7 scores 69.4%, demonstrating significant agentic terminal competence.
GLM 4.7
33.4
MiniMax M2.5
42.7
Full benchmark table
| Benchmark | GLM 4.7 | MiniMax M2.5 |
|---|---|---|
APEX-Agents APEX-Agents · evaluates AI agents on complex, multi-step tasks requiring planning, tool use, and autonomous decision-making in realistic environments. | 3.1 | 6.2 |
Chatbot Arena Elo · Coding | 1434.5 | 1386.3 |
Chatbot Arena Elo · Overall | 1441.5 | 1390.8 |
Cl Bench | 15.9 | 11.4 |
Cl Bench Life | 10.9 | 6.3 |
LiveBench · Agentic Coding | 41.7 | 51.7 |
LiveBench · Coding | 73.1 | 70.7 |
LiveBench · Data Analysis | 55.2 | 49.6 |
LiveBench · If | 35.7 | 57.2 |
LiveBench · Language | 65.2 | 55.1 |
LiveBench · Mathematics | 76.0 | 77.4 |
LiveBench · Overall | 58.1 | 60.1 |
LiveBench · Reasoning | 59.7 | 59.3 |
OpenCompass · AIME2025 | 95.4 | 86.2 |
OpenCompass · GPQA-Diamond | 86.9 | 84.6 |
OpenCompass · HLE | 25.4 | 22.2 |
OpenCompass · IFEval | 90.2 | 91.1 |
OpenCompass · LiveCodeBenchV6 | 83.8 | 73.6 |
OpenCompass · MMLU-Pro | 84.0 | 81.7 |
PostTrainBench | 7.5 | 9.5 |
Proofbench | 6.0 | 4.0 |
Terminal Bench Terminal-Bench 2.0 · evaluates AI agents on real terminal-based coding tasks · writing scripts, debugging, running tests, and managing projects entirely through command-line interaction. Tests both code quality and terminal fluency. Claude Opus 4.7 scores 69.4%, demonstrating significant agentic terminal competence. | 33.4 | 42.7 |
Pricing · per 1M tokens · projected $/mo at 10M tokens
| Model | Input | Output | Context | Projected $/mo |
|---|---|---|---|---|
| $0.60 | $2.20 | 205K tokens (~102 books) | $10.00 | |
| $0.27 | $1.08 | 205K tokens (~102 books) | $4.72 |
People also compared