Compare · ModelsLive · 3 picked · head to head
GLM 5.2 vs Kimi K2.7 Code vs MiniMax M3
Side by side · benchmarks, pricing, and signals you can act on.
Winner summary
GLM 5.2 wins on 10/13 benchmarks
GLM 5.2 wins 10 of 13 shared benchmarks. Leads in speed · coding · language.
Category leads
speed·GLM 5.2coding·GLM 5.2reasoning·MiniMax M3language·GLM 5.2math·GLM 5.2knowledge·GLM 5.2arena·GLM 5.2
Hype vs Reality
Attention vs performance
GLM 5.2
#11 by perf·no signal
Kimi K2.7 Code
#42 by perf·no signal
MiniMax M3
#19 by perf·no signal
Best value
MiniMax M3
2.7x better value than Kimi K2.7 Code
GLM 5.2
30.5 pts/$
$2.50/M
Kimi K2.7 Code
34.3 pts/$
$1.84/M
MiniMax M3
93.3 pts/$
$0.75/M
Vendor risk
Mixed exposure
One or more vendors flagged
z-ai
private · undisclosed
moonshotai
private · undisclosed
MiniMax
$4.0B·Tier 1
Head to head
13 benchmarks · 3 models
GLM 5.2Kimi K2.7 CodeMiniMax M3
Artificial Analysis · Agentic Index
GLM 5.2 leads by +7.7
Artificial Analysis Agentic Index · a composite score measuring how well a model performs in agentic workflows · multi-step tool use, planning, error recovery, and autonomous task completion. Aggregates results from multiple agentic benchmarks including SWE-bench, tool-use tests, and planning evaluations. The canonical single-number metric for "how good is this model as an agent?"
GLM 5.2
43.1
Kimi K2.7 Code
29.6
MiniMax M3
35.4
Artificial Analysis · Coding Index
GLM 5.2 leads by +8.0
Artificial Analysis Coding Index · a composite score that aggregates performance across multiple coding benchmarks into a single index. Tracks code generation quality, debugging ability, multi-language competence, and real-world software engineering tasks. Used by Artificial Analysis to rank model coding capability in a normalized, comparable format. Useful for developers choosing between models for coding-heavy workloads.
GLM 5.2
68.8
Kimi K2.7 Code
60.8
MiniMax M3
58.6
Artificial Analysis · Quality Index
GLM 5.2 leads by +6.7
GLM 5.2
51.1
Kimi K2.7 Code
42.0
MiniMax M3
44.4
LiveBench · Agentic Coding
GLM 5.2 leads by +3.3
GLM 5.2
73.3
Kimi K2.7 Code
70.0
MiniMax M3
60.0
LiveBench · Coding
GLM 5.2 leads by +5.7
GLM 5.2
79.7
Kimi K2.7 Code
74.0
MiniMax M3
68.2
LiveBench · Data Analysis
MiniMax M3 leads by +2.4
GLM 5.2
73.7
Kimi K2.7 Code
62.7
MiniMax M3
76.2
LiveBench · If
GLM 5.2 leads by +4.8
GLM 5.2
62.3
Kimi K2.7 Code
56.3
MiniMax M3
57.5
LiveBench · Language
Kimi K2.7 Code leads by +1.1
GLM 5.2
76.2
Kimi K2.7 Code
77.9
MiniMax M3
76.8
LiveBench · Mathematics
GLM 5.2 leads by +10.2
GLM 5.2
89.8
Kimi K2.7 Code
79.6
MiniMax M3
77.0
LiveBench · Overall
GLM 5.2 leads by +4.3
GLM 5.2
76.2
Kimi K2.7 Code
71.9
MiniMax M3
70.0
LiveBench · Reasoning
Kimi K2.7 Code leads by +4.2
GLM 5.2
78.6
Kimi K2.7 Code
82.8
MiniMax M3
74.5
Chatbot Arena Elo · Coding
GLM 5.2 leads by +88.3
GLM 5.2
1593.3
MiniMax M3
1504.9
Chatbot Arena Elo · Overall
GLM 5.2 leads by +22.8
GLM 5.2
1471.0
MiniMax M3
1448.2
Full benchmark table
| Benchmark | GLM 5.2 | Kimi K2.7 Code | MiniMax M3 |
|---|---|---|---|
Artificial Analysis · Agentic Index Artificial Analysis Agentic Index · a composite score measuring how well a model performs in agentic workflows · multi-step tool use, planning, error recovery, and autonomous task completion. Aggregates results from multiple agentic benchmarks including SWE-bench, tool-use tests, and planning evaluations. The canonical single-number metric for "how good is this model as an agent?" | 43.1 | 29.6 | 35.4 |
Artificial Analysis · Coding Index Artificial Analysis Coding Index · a composite score that aggregates performance across multiple coding benchmarks into a single index. Tracks code generation quality, debugging ability, multi-language competence, and real-world software engineering tasks. Used by Artificial Analysis to rank model coding capability in a normalized, comparable format. Useful for developers choosing between models for coding-heavy workloads. | 68.8 | 60.8 | 58.6 |
Artificial Analysis · Quality Index | 51.1 | 42.0 | 44.4 |
LiveBench · Agentic Coding | 73.3 | 70.0 | 60.0 |
LiveBench · Coding | 79.7 | 74.0 | 68.2 |
LiveBench · Data Analysis | 73.7 | 62.7 | 76.2 |
LiveBench · If | 62.3 | 56.3 | 57.5 |
LiveBench · Language | 76.2 | 77.9 | 76.8 |
LiveBench · Mathematics | 89.8 | 79.6 | 77.0 |
LiveBench · Overall | 76.2 | 71.9 | 70.0 |
LiveBench · Reasoning | 78.6 | 82.8 | 74.5 |
Chatbot Arena Elo · Coding | 1593.3 | — | 1504.9 |
Chatbot Arena Elo · Overall | 1471.0 | — | 1448.2 |
Pricing · per 1M tokens · projected $/mo at 10M tokens
| Model | Input | Output | Context | Projected $/mo |
|---|---|---|---|---|
| $1.00 | $4.00 | 1.0M tokens (~524 books) | $17.50 | |
| $0.61 | $3.07 | 262K tokens (~131 books) | $12.26 | |
| $0.30 | $1.20 | 1.0M tokens (~524 books) | $5.25 |