Compare · ModelsLive · 2 picked · head to head
GLM 5.2 vs GPT-4.1
Side by side · benchmarks, pricing, and signals you can act on.
Winner summary
GLM 5.2 wins on 12/12 benchmarks
GLM 5.2 wins 12 of 12 shared benchmarks. Leads in reasoning · knowledge · general.
Category leads
reasoning·GLM 5.2knowledge·GLM 5.2general·GLM 5.2math·GLM 5.2coding·GLM 5.2
Hype vs Reality
Attention vs performance
GLM 5.2
#75 by perf·#3 by attention
GPT-4.1
#201 by perf·no signal
Best value
GLM 5.2
2.1x better value than GPT-4.1
GLM 5.2
16.5 pts/$
$3.40/M
GPT-4.1
7.9 pts/$
$5.00/M
Vendor risk
Who is behind the model
z-ai
private · undisclosed
OpenAI
$840.0B·Tier 1
Head to head
12 benchmarks · 2 models
GLM 5.2GPT-4.1
ARC-AGI
GLM 5.2 leads by +71.5
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
GPT-4.1
5.5
ARC-AGI-2
GLM 5.2 leads by +22.4
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
GPT-4.1
0.4
Chess Puzzles
GLM 5.2 leads by +11.6
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
GPT-4.1
6.4
Dtbench
GLM 5.2 leads by +42.2
GLM 5.2
89.3
GPT-4.1
47.1
FrontierMath-Tiers-1-3-v2-Private
GLM 5.2 leads by +56.0
GLM 5.2
62.3
GPT-4.1
6.3
GPQA diamond
GLM 5.2 leads by +37.0
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
GPT-4.1
55.9
Lmca
GLM 5.2 leads by +23.8
GLM 5.2
53.9
GPT-4.1
30.1
OTIS Mock AIME 2024-2025
GLM 5.2 leads by +48.1
OTIS Mock AIME 2024-2025 · simulated American Invitational Mathematics Examination problems testing advanced problem-solving skills.
GLM 5.2
86.4
GPT-4.1
38.3
SimpleBench
GLM 5.2 leads by +38.2
SimpleBench · tests fundamental reasoning capabilities with straightforward problems designed to expose gaps in basic logical and spatial thinking.
GLM 5.2
50.6
GPT-4.1
12.4
SimpleQA Verified
GLM 5.2 leads by +3.1
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
GPT-4.1
31.1
SWE-Bench verified
GLM 5.2 leads by +30.2
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
GPT-4.1
48.5
WeirdML
GLM 5.2 leads by +31.1
WeirdML · tests models on unusual and adversarial machine learning tasks that require creative problem-solving beyond standard patterns.
GLM 5.2
70.1
GPT-4.1
39.0
Full benchmark table
| Benchmark | GLM 5.2 | GPT-4.1 |
|---|---|---|
ARC-AGI ARC-AGI · the original Abstraction and Reasoning Corpus, testing whether AI can solve novel visual pattern recognition tasks without memorization. | 77.0 | 5.5 |
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 | 0.4 |
Chess Puzzles Chess Puzzles · tests strategic and tactical reasoning by having models solve chess puzzle positions, evaluating lookahead and pattern recognition abilities. | 17.9 | 6.4 |
Dtbench | 89.3 | 47.1 |
FrontierMath-Tiers-1-3-v2-Private | 62.3 | 6.3 |
GPQA diamond Graduate-Level Google-Proof QA (Diamond set) · expert-crafted questions in physics, biology, and chemistry that are difficult even for domain PhDs. | 92.9 | 55.9 |
Lmca | 53.9 | 30.1 |
OTIS Mock AIME 2024-2025 OTIS Mock AIME 2024-2025 · simulated American Invitational Mathematics Examination problems testing advanced problem-solving skills. | 86.4 | 38.3 |
SimpleBench SimpleBench · tests fundamental reasoning capabilities with straightforward problems designed to expose gaps in basic logical and spatial thinking. | 50.6 | 12.4 |
SimpleQA Verified SimpleQA Verified · short factual questions with verified answers, measuring factual accuracy and the tendency to hallucinate or provide incorrect information. | 34.2 | 31.1 |
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. The benchmark remains the most-cited evaluation for code-generation capability. | 78.7 | 48.5 |
WeirdML WeirdML · tests models on unusual and adversarial machine learning tasks that require creative problem-solving beyond standard patterns. | 70.1 | 39.0 |
Pricing · per 1M tokens · projected $/mo at 10M tokens