Compare · ModelsLive · 2 picked · head to head
GLM 5.2 vs Qwen3.6 Plus
Side by side · benchmarks, pricing, and signals you can act on.
Winner summary
GLM 5.2 wins on 20/22 benchmarks
GLM 5.2 wins 20 of 22 shared benchmarks. Leads in speed · arena · knowledge.
Category leads
speed·GLM 5.2arena·GLM 5.2knowledge·GLM 5.2general·GLM 5.2math·GLM 5.2coding·GLM 5.2reasoning·GLM 5.2language·GLM 5.2
Hype vs Reality
Attention vs performance
GLM 5.2
#67 by perf·#3 by attention
Qwen3.6 Plus
#94 by perf·#2 by attention
Best value
Qwen3.6 Plus
2.1x better value than GLM 5.2
GLM 5.2
22.6 pts/$
$2.50/M
Qwen3.6 Plus
47.0 pts/$
$1.14/M
Vendor risk
Who is behind the model
z-ai
private · undisclosed
Alibaba (Qwen)
$293.0B·Tier 1
Head to head
22 benchmarks · 2 models
GLM 5.2Qwen3.6 Plus
Artificial Analysis · Agentic Index
GLM 5.2 leads by +15.5
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
Qwen3.6 Plus
27.6
Artificial Analysis · Coding Index
GLM 5.2 leads by +14.2
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
Qwen3.6 Plus
54.5
Artificial Analysis · Quality Index
GLM 5.2 leads by +11.5
GLM 5.2
51.1
Qwen3.6 Plus
39.6
Chatbot Arena Elo · Coding
GLM 5.2 leads by +132.5
GLM 5.2
1593.3
Qwen3.6 Plus
1460.7
Chatbot Arena Elo · Overall
GLM 5.2 leads by +27.6
GLM 5.2
1471.0
Qwen3.6 Plus
1443.4
Chess Puzzles
GLM 5.2 leads by +4.2
Chess Puzzles · tests strategic and tactical reasoning by having models solve chess puzzle positions, evaluating lookahead and pattern recognition abilities.
GLM 5.2
16.9
Qwen3.6 Plus
12.7
Dtbench
GLM 5.2 leads by +19.5
GLM 5.2
89.3
Qwen3.6 Plus
69.8
FrontierMath-Tiers-1-3-v2-Private
GLM 5.2 leads by +21.0
GLM 5.2
59.2
Qwen3.6 Plus
38.3
GPQA diamond
GLM 5.2 leads by +4.6
Graduate-Level Google-Proof QA (Diamond set) · expert-crafted questions in physics, biology, and chemistry that are difficult even for domain PhDs.
GLM 5.2
89.1
Qwen3.6 Plus
84.5
LiveBench · Agentic Coding
GLM 5.2 leads by +18.3
GLM 5.2
73.3
Qwen3.6 Plus
55.0
LiveBench · Coding
GLM 5.2 leads by +1.5
GLM 5.2
79.7
Qwen3.6 Plus
78.2
LiveBench · Data Analysis
GLM 5.2 leads by +3.8
GLM 5.2
73.7
Qwen3.6 Plus
69.9
LiveBench · If
GLM 5.2 leads by +3.9
GLM 5.2
62.3
Qwen3.6 Plus
58.3
LiveBench · Language
GLM 5.2 leads by +1.3
GLM 5.2
76.2
Qwen3.6 Plus
75.0
LiveBench · Mathematics
GLM 5.2 leads by +6.1
GLM 5.2
89.8
Qwen3.6 Plus
83.7
LiveBench · Overall
GLM 5.2 leads by +5.4
GLM 5.2
76.2
Qwen3.6 Plus
70.8
LiveBench · Reasoning
GLM 5.2 leads by +2.8
GLM 5.2
78.6
Qwen3.6 Plus
75.8
Lmca
GLM 5.2 leads by +15.0
GLM 5.2
53.9
Qwen3.6 Plus
38.9
Mystery Game Puzzles
GLM 5.2 leads by +7.7
GLM 5.2
10.8
Qwen3.6 Plus
3.1
OTIS Mock AIME 2024-2025
Qwen3.6 Plus leads by +7.0
OTIS Mock AIME 2024-2025 · simulated American Invitational Mathematics Examination problems testing advanced problem-solving skills.
GLM 5.2
86.4
Qwen3.6 Plus
93.3
SimpleQA Verified
Qwen3.6 Plus leads by +9.9
SimpleQA Verified · short factual questions with verified answers, measuring factual accuracy and the tendency to hallucinate or provide incorrect information.
GLM 5.2
34.2
Qwen3.6 Plus
44.1
SWE-Bench verified
GLM 5.2 leads by +20.9
SWE-bench Verified · 500 human-validated tasks from 12 real Python repositories (Django, Flask, scikit-learn, sympy, and others). Each task requires the model to produce a git patch that resolves a real GitHub issue and passes the test suite. The verified subset eliminates ambiguous tasks from the original SWE-bench. Claude Mythos Preview leads at 93.9%, crossing 90% for the first time in 2026. Opus 4.6 scores 80.8%. The benchmark remains the most-cited evaluation for code-generation capability.
GLM 5.2
78.7
Qwen3.6 Plus
57.9
Full benchmark table
| Benchmark | GLM 5.2 | Qwen3.6 Plus |
|---|---|---|
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 | 27.6 |
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 | 54.5 |
Artificial Analysis · Quality Index | 51.1 | 39.6 |
Chatbot Arena Elo · Coding | 1593.3 | 1460.7 |
Chatbot Arena Elo · Overall | 1471.0 | 1443.4 |
Chess Puzzles Chess Puzzles · tests strategic and tactical reasoning by having models solve chess puzzle positions, evaluating lookahead and pattern recognition abilities. | 16.9 | 12.7 |
Dtbench | 89.3 | 69.8 |
FrontierMath-Tiers-1-3-v2-Private | 59.2 | 38.3 |
GPQA diamond Graduate-Level Google-Proof QA (Diamond set) · expert-crafted questions in physics, biology, and chemistry that are difficult even for domain PhDs. | 89.1 | 84.5 |
LiveBench · Agentic Coding | 73.3 | 55.0 |
LiveBench · Coding | 79.7 | 78.2 |
LiveBench · Data Analysis | 73.7 | 69.9 |
LiveBench · If | 62.3 | 58.3 |
LiveBench · Language | 76.2 | 75.0 |
LiveBench · Mathematics | 89.8 | 83.7 |
LiveBench · Overall | 76.2 | 70.8 |
LiveBench · Reasoning | 78.6 | 75.8 |
Lmca | 53.9 | 38.9 |
Mystery Game Puzzles | 10.8 | 3.1 |
OTIS Mock AIME 2024-2025 OTIS Mock AIME 2024-2025 · simulated American Invitational Mathematics Examination problems testing advanced problem-solving skills. | 86.4 | 93.3 |
SimpleQA Verified SimpleQA Verified · short factual questions with verified answers, measuring factual accuracy and the tendency to hallucinate or provide incorrect information. | 34.2 | 44.1 |
SWE-Bench verified SWE-bench Verified · 500 human-validated tasks from 12 real Python repositories (Django, Flask, scikit-learn, sympy, and others). Each task requires the model to produce a git patch that resolves a real GitHub issue and passes the test suite. The verified subset eliminates ambiguous tasks from the original SWE-bench. Claude Mythos Preview leads at 93.9%, crossing 90% for the first time in 2026. Opus 4.6 scores 80.8%. The benchmark remains the most-cited evaluation for code-generation capability. | 78.7 | 57.9 |
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 | |
| $0.33 | $1.95 | 1.0M tokens (~500 books) | $7.31 |