Compare · ModelsLive · 3 picked · head to head
GLM 5.2 vs GPT-5.2-Codex vs Qwen3.7 Max
Side by side · benchmarks, pricing, and signals you can act on.
Winner summary
GLM 5.2 wins on 6/11 benchmarks
GLM 5.2 wins 6 of 11 shared benchmarks. Leads in coding · math · knowledge.
Category leads
coding·GLM 5.2reasoning·GPT-5.2-Codexlanguage·Qwen3.7 Maxmath·GLM 5.2knowledge·GLM 5.2speed·GLM 5.2
Hype vs Reality
Attention vs performance
GLM 5.2
#11 by perf·no signal
GPT-5.2-Codex
#18 by perf·no signal
Qwen3.7 Max
#28 by perf·no signal
Best value
GLM 5.2
1.1x better value than Qwen3.7 Max
GLM 5.2
30.5 pts/$
$2.50/M
GPT-5.2-Codex
9.0 pts/$
$7.88/M
Qwen3.7 Max
27.2 pts/$
$2.50/M
Vendor risk
Who is behind the model
z-ai
private · undisclosed
OpenAI
$840.0B·Tier 1
Alibaba (Qwen)
$293.0B·Tier 1
Head to head
11 benchmarks · 3 models
GLM 5.2GPT-5.2-CodexQwen3.7 Max
LiveBench · Agentic Coding
GLM 5.2 leads by +21.7
GLM 5.2
73.3
GPT-5.2-Codex
51.7
Qwen3.7 Max
51.7
LiveBench · Coding
GPT-5.2-Codex leads by +4.0
GLM 5.2
79.7
GPT-5.2-Codex
83.6
Qwen3.7 Max
74.2
LiveBench · Data Analysis
GPT-5.2-Codex leads by +4.5
GLM 5.2
73.7
GPT-5.2-Codex
78.2
Qwen3.7 Max
71.8
LiveBench · If
Qwen3.7 Max leads by +7.6
GLM 5.2
62.3
GPT-5.2-Codex
66.5
Qwen3.7 Max
74.0
LiveBench · Language
Qwen3.7 Max leads by +3.5
GLM 5.2
76.2
GPT-5.2-Codex
73.7
Qwen3.7 Max
79.7
LiveBench · Mathematics
GLM 5.2 leads by +1.0
GLM 5.2
89.8
GPT-5.2-Codex
88.8
Qwen3.7 Max
85.3
LiveBench · Overall
GLM 5.2 leads by +1.9
GLM 5.2
76.2
GPT-5.2-Codex
74.3
Qwen3.7 Max
74.3
LiveBench · Reasoning
Qwen3.7 Max leads by +4.7
GLM 5.2
78.6
GPT-5.2-Codex
77.7
Qwen3.7 Max
83.3
Artificial Analysis · Agentic Index
GLM 5.2 leads by +12.5
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
Qwen3.7 Max
30.6
Artificial Analysis · Coding Index
GLM 5.2 leads by +2.8
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
Qwen3.7 Max
66.0
Artificial Analysis · Quality Index
GLM 5.2 leads by +5.1
GLM 5.2
51.1
Qwen3.7 Max
46.0
Full benchmark table
| Benchmark | GLM 5.2 | GPT-5.2-Codex | Qwen3.7 Max |
|---|---|---|---|
LiveBench · Agentic Coding | 73.3 | 51.7 | 51.7 |
LiveBench · Coding | 79.7 | 83.6 | 74.2 |
LiveBench · Data Analysis | 73.7 | 78.2 | 71.8 |
LiveBench · If | 62.3 | 66.5 | 74.0 |
LiveBench · Language | 76.2 | 73.7 | 79.7 |
LiveBench · Mathematics | 89.8 | 88.8 | 85.3 |
LiveBench · Overall | 76.2 | 74.3 | 74.3 |
LiveBench · Reasoning | 78.6 | 77.7 | 83.3 |
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 | — | 30.6 |
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 | — | 66.0 |
Artificial Analysis · Quality Index | 51.1 | — | 46.0 |
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 | |
| $1.75 | $14.00 | 400K tokens (~200 books) | $48.13 | |
| $1.25 | $3.75 | 1.0M tokens (~500 books) | $18.75 |