Compare · ModelsLive · 2 picked · head to head
GLM 5.2 vs o3
Side by side · benchmarks, pricing, and signals you can act on.
Winner summary
GLM 5.2 wins on 14/17 benchmarks
GLM 5.2 wins 14 of 17 shared benchmarks. Leads in speed · agentic · reasoning.
Category leads
speed·GLM 5.2agentic·GLM 5.2reasoning·GLM 5.2knowledge·o3general·GLM 5.2math·GLM 5.2coding·GLM 5.2
Hype vs Reality
Attention vs performance
GLM 5.2
#75 by perf·#3 by attention
o3
#121 by perf·no signal
Best value
GLM 5.2
1.6x better value than o3
GLM 5.2
16.5 pts/$
$3.40/M
o3
10.3 pts/$
$5.00/M
Vendor risk
Who is behind the model
z-ai
private · undisclosed
OpenAI
$840.0B·Tier 1
Head to head
17 benchmarks · 2 models
GLM 5.2o3
Artificial Analysis · Agentic Index
GLM 5.2 leads by +7.0
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
o3
36.1
Artificial Analysis · Coding Index
GLM 5.2 leads by +30.4
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
o3
38.4
Artificial Analysis · Quality Index
GLM 5.2 leads by +30.9
GLM 5.2
51.1
o3
20.2
APEX-Agents
GLM 5.2 leads by +28.0
APEX-Agents · evaluates AI agents on complex, multi-step tasks requiring planning, tool use, and autonomous decision-making in realistic environments.
GLM 5.2
45.2
o3
17.2
ARC-AGI
GLM 5.2 leads by +16.2
ARC-AGI · the original Abstraction and Reasoning Corpus, testing whether AI can solve novel visual pattern recognition tasks without memorization.
GLM 5.2
77.0
o3
60.8
ARC-AGI-2
GLM 5.2 leads by +16.3
ARC-AGI-2 · the second iteration of the Abstraction and Reasoning Corpus, testing novel pattern recognition and abstract reasoning without prior training data.
GLM 5.2
22.8
o3
6.5
Chess Puzzles
o3 leads by +15.8
Chess Puzzles · tests strategic and tactical reasoning by having models solve chess puzzle positions, evaluating lookahead and pattern recognition abilities.
GLM 5.2
17.9
o3
33.7
Dtbench
GLM 5.2 leads by +14.7
GLM 5.2
89.3
o3
74.7
FrontierMath-Tiers-1-3-v2-Private
GLM 5.2 leads by +27.2
GLM 5.2
62.3
o3
35.1
GPQA diamond
GLM 5.2 leads by +17.1
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
92.9
o3
75.8
Lmca
GLM 5.2 leads by +7.2
GLM 5.2
53.9
o3
46.7
Mystery Game Puzzles
o3 leads by +11.0
GLM 5.2
10.8
o3
21.8
OTIS Mock AIME 2024-2025
GLM 5.2 leads by +1.9
OTIS Mock AIME 2024-2025 · simulated American Invitational Mathematics Examination problems testing advanced problem-solving skills.
GLM 5.2
86.4
o3
84.4
SimpleBench
GLM 5.2 leads by +6.8
SimpleBench · tests fundamental reasoning capabilities with straightforward problems designed to expose gaps in basic logical and spatial thinking.
GLM 5.2
50.6
o3
43.7
SimpleQA Verified
o3 leads by +18.8
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
o3
53.0
SWE-Bench verified
GLM 5.2 leads by +16.4
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. The benchmark remains the most-cited evaluation for code-generation capability.
GLM 5.2
78.7
o3
62.3
WeirdML
GLM 5.2 leads by +17.7
WeirdML · tests models on unusual and adversarial machine learning tasks that require creative problem-solving beyond standard patterns.
GLM 5.2
70.1
o3
52.4
Full benchmark table
| Benchmark | GLM 5.2 | o3 |
|---|---|---|
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 | 36.1 |
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 | 38.4 |
Artificial Analysis · Quality Index | 51.1 | 20.2 |
APEX-Agents APEX-Agents · evaluates AI agents on complex, multi-step tasks requiring planning, tool use, and autonomous decision-making in realistic environments. | 45.2 | 17.2 |
ARC-AGI ARC-AGI · the original Abstraction and Reasoning Corpus, testing whether AI can solve novel visual pattern recognition tasks without memorization. | 77.0 | 60.8 |
ARC-AGI-2 ARC-AGI-2 · the second iteration of the Abstraction and Reasoning Corpus, testing novel pattern recognition and abstract reasoning without prior training data. | 22.8 | 6.5 |
Chess Puzzles Chess Puzzles · tests strategic and tactical reasoning by having models solve chess puzzle positions, evaluating lookahead and pattern recognition abilities. | 17.9 | 33.7 |
Dtbench | 89.3 | 74.7 |
FrontierMath-Tiers-1-3-v2-Private | 62.3 | 35.1 |
GPQA diamond Graduate-Level Google-Proof QA (Diamond set) · expert-crafted questions in physics, biology, and chemistry that are difficult even for domain PhDs. | 92.9 | 75.8 |
Lmca | 53.9 | 46.7 |
Mystery Game Puzzles | 10.8 | 21.8 |
OTIS Mock AIME 2024-2025 OTIS Mock AIME 2024-2025 · simulated American Invitational Mathematics Examination problems testing advanced problem-solving skills. | 86.4 | 84.4 |
SimpleBench SimpleBench · tests fundamental reasoning capabilities with straightforward problems designed to expose gaps in basic logical and spatial thinking. | 50.6 | 43.7 |
SimpleQA Verified SimpleQA Verified · short factual questions with verified answers, measuring factual accuracy and the tendency to hallucinate or provide incorrect information. | 34.2 | 53.0 |
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. The benchmark remains the most-cited evaluation for code-generation capability. | 78.7 | 62.3 |
WeirdML WeirdML · tests models on unusual and adversarial machine learning tasks that require creative problem-solving beyond standard patterns. | 70.1 | 52.4 |
Pricing · per 1M tokens · projected $/mo at 10M tokens