Compare · ModelsLive · 2 picked · head to head
DeepSeek V3.2 vs GLM 5.2
Side by side · benchmarks, pricing, and signals you can act on.
Winner summary
GLM 5.2 wins on 21/23 benchmarks
GLM 5.2 wins 21 of 23 shared benchmarks. Leads in speed · agentic · reasoning.
Category leads
speed·GLM 5.2agentic·GLM 5.2reasoning·GLM 5.2arena·GLM 5.2knowledge·GLM 5.2general·GLM 5.2coding·GLM 5.2language·GLM 5.2math·GLM 5.2
Hype vs Reality
Attention vs performance
DeepSeek V3.2
#146 by perf·no signal
GLM 5.2
#75 by perf·#3 by attention
Best value
DeepSeek V3.2
9.1x better value than GLM 5.2
DeepSeek V3.2
150.9 pts/$
$0.32/M
GLM 5.2
16.5 pts/$
$3.40/M
Vendor risk
Mixed exposure
One or more vendors flagged
DeepSeek
$3.4B·Tier 1
z-ai
private · undisclosed
Head to head
23 benchmarks · 2 models
DeepSeek V3.2GLM 5.2
Artificial Analysis · Agentic Index
DeepSeek V3.2 leads by +9.9
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?"
DeepSeek V3.2
52.9
GLM 5.2
43.1
Artificial Analysis · Coding Index
GLM 5.2 leads by +32.1
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.
DeepSeek V3.2
36.7
GLM 5.2
68.8
Artificial Analysis · Quality Index
GLM 5.2 leads by +9.4
DeepSeek V3.2
41.7
GLM 5.2
51.1
APEX-Agents
GLM 5.2 leads by +23.9
APEX-Agents · evaluates AI agents on complex, multi-step tasks requiring planning, tool use, and autonomous decision-making in realistic environments.
DeepSeek V3.2
21.3
GLM 5.2
45.2
ARC-AGI
GLM 5.2 leads by +20.0
ARC-AGI · the original Abstraction and Reasoning Corpus, testing whether AI can solve novel visual pattern recognition tasks without memorization.
DeepSeek V3.2
57.0
GLM 5.2
77.0
ARC-AGI-2
GLM 5.2 leads by +18.8
ARC-AGI-2 · the second iteration of the Abstraction and Reasoning Corpus, testing novel pattern recognition and abstract reasoning without prior training data.
DeepSeek V3.2
4.0
GLM 5.2
22.8
Chatbot Arena Elo · Coding
GLM 5.2 leads by +268.4
DeepSeek V3.2
1324.8
GLM 5.2
1593.3
Chatbot Arena Elo · Overall
GLM 5.2 leads by +46.2
DeepSeek V3.2
1424.8
GLM 5.2
1471.0
Chess Puzzles
GLM 5.2 leads by +8.4
Chess Puzzles · tests strategic and tactical reasoning by having models solve chess puzzle positions, evaluating lookahead and pattern recognition abilities.
DeepSeek V3.2
9.5
GLM 5.2
17.9
Dtbench
GLM 5.2 leads by +13.3
DeepSeek V3.2
76.0
GLM 5.2
89.3
GPQA diamond
GLM 5.2 leads by +11.7
Graduate-Level Google-Proof QA (Diamond set) · expert-crafted questions in physics, biology, and chemistry that are difficult even for domain PhDs.
DeepSeek V3.2
81.1
GLM 5.2
92.9
LiveBench · Agentic Coding
GLM 5.2 leads by +26.7
DeepSeek V3.2
46.7
GLM 5.2
73.3
LiveBench · Coding
GLM 5.2 leads by +4.0
DeepSeek V3.2
75.7
GLM 5.2
79.7
LiveBench · Data Analysis
GLM 5.2 leads by +28.7
DeepSeek V3.2
45.0
GLM 5.2
73.7
LiveBench · If
GLM 5.2 leads by +39.2
DeepSeek V3.2
23.1
GLM 5.2
62.3
LiveBench · Language
GLM 5.2 leads by +12.0
DeepSeek V3.2
64.2
GLM 5.2
76.2
LiveBench · Mathematics
GLM 5.2 leads by +25.8
DeepSeek V3.2
64.0
GLM 5.2
89.8
LiveBench · Overall
GLM 5.2 leads by +24.4
DeepSeek V3.2
51.8
GLM 5.2
76.2
LiveBench · Reasoning
GLM 5.2 leads by +34.4
DeepSeek V3.2
44.3
GLM 5.2
78.6
Lmca
GLM 5.2 leads by +20.0
DeepSeek V3.2
33.9
GLM 5.2
53.9
OTIS Mock AIME 2024-2025
DeepSeek V3.2 leads by +1.4
OTIS Mock AIME 2024-2025 · simulated American Invitational Mathematics Examination problems testing advanced problem-solving skills.
DeepSeek V3.2
87.8
GLM 5.2
86.4
Proofbench
GLM 5.2 leads by +27.0
DeepSeek V3.2
8.0
GLM 5.2
35.0
SimpleQA Verified
GLM 5.2 leads by +6.7
SimpleQA Verified · short factual questions with verified answers, measuring factual accuracy and the tendency to hallucinate or provide incorrect information.
DeepSeek V3.2
27.5
GLM 5.2
34.2
Full benchmark table
| Benchmark | DeepSeek V3.2 | GLM 5.2 |
|---|---|---|
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?" | 52.9 | 43.1 |
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. | 36.7 | 68.8 |
Artificial Analysis · Quality Index | 41.7 | 51.1 |
APEX-Agents APEX-Agents · evaluates AI agents on complex, multi-step tasks requiring planning, tool use, and autonomous decision-making in realistic environments. | 21.3 | 45.2 |
ARC-AGI ARC-AGI · the original Abstraction and Reasoning Corpus, testing whether AI can solve novel visual pattern recognition tasks without memorization. | 57.0 | 77.0 |
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. | 4.0 | 22.8 |
Chatbot Arena Elo · Coding | 1324.8 | 1593.3 |
Chatbot Arena Elo · Overall | 1424.8 | 1471.0 |
Chess Puzzles Chess Puzzles · tests strategic and tactical reasoning by having models solve chess puzzle positions, evaluating lookahead and pattern recognition abilities. | 9.5 | 17.9 |
Dtbench | 76.0 | 89.3 |
GPQA diamond Graduate-Level Google-Proof QA (Diamond set) · expert-crafted questions in physics, biology, and chemistry that are difficult even for domain PhDs. | 81.1 | 92.9 |
LiveBench · Agentic Coding | 46.7 | 73.3 |
LiveBench · Coding | 75.7 | 79.7 |
LiveBench · Data Analysis | 45.0 | 73.7 |
LiveBench · If | 23.1 | 62.3 |
LiveBench · Language | 64.2 | 76.2 |
LiveBench · Mathematics | 64.0 | 89.8 |
LiveBench · Overall | 51.8 | 76.2 |
LiveBench · Reasoning | 44.3 | 78.6 |
Lmca | 33.9 | 53.9 |
OTIS Mock AIME 2024-2025 OTIS Mock AIME 2024-2025 · simulated American Invitational Mathematics Examination problems testing advanced problem-solving skills. | 87.8 | 86.4 |
Proofbench | 8.0 | 35.0 |
SimpleQA Verified SimpleQA Verified · short factual questions with verified answers, measuring factual accuracy and the tendency to hallucinate or provide incorrect information. | 27.5 | 34.2 |
Pricing · per 1M tokens · projected $/mo at 10M tokens
| Model | Input | Output | Context | Projected $/mo |
|---|---|---|---|---|
| $0.26 | $0.38 | 164K tokens (~82 books) | $2.90 | |
| $0.01 | $6.80 | 1.0M tokens (~524 books) | $17.05 |