Compare · ModelsLive · 2 picked · head to head
DeepSeek V3.2 vs GLM 4.7
Side by side · benchmarks, pricing, and signals you can act on.
Winner summary
GLM 4.7 wins on 14/24 benchmarks
GLM 4.7 wins 14 of 24 shared benchmarks. Leads in arena · knowledge · reasoning.
Category leads
agentic·DeepSeek V3.2arena·GLM 4.7knowledge·GLM 4.7math·DeepSeek V3.2coding·DeepSeek V3.2reasoning·GLM 4.7language·GLM 4.7
Hype vs Reality
Attention vs performance
DeepSeek V3.2
#108 by perf·no signal
GLM 4.7
#117 by perf·no signal
Best value
DeepSeek V3.2
4.1x better value than GLM 4.7
DeepSeek V3.2
191.0 pts/$
$0.27/M
GLM 4.7
47.0 pts/$
$1.07/M
Vendor risk
Mixed exposure
One or more vendors flagged
DeepSeek
$3.4B·Tier 1
z-ai
private · undisclosed
Head to head
24 benchmarks · 2 models
DeepSeek V3.2GLM 4.7
APEX-Agents
DeepSeek V3.2 leads by +3.9
APEX-Agents · evaluates AI agents on complex, multi-step tasks requiring planning, tool use, and autonomous decision-making in realistic environments.
DeepSeek V3.2
7.0
GLM 4.7
3.1
Chatbot Arena Elo · Coding
GLM 4.7 leads by +108.1
DeepSeek V3.2
1331.9
GLM 4.7
1440.0
Chatbot Arena Elo · Overall
GLM 4.7 leads by +17.6
DeepSeek V3.2
1425.0
GLM 4.7
1442.6
Chess Puzzles
DeepSeek V3.2 leads by +8.0
Chess Puzzles · tests strategic and tactical reasoning by having models solve chess puzzle positions, evaluating lookahead and pattern recognition abilities.
DeepSeek V3.2
14.0
GLM 4.7
6.0
FrontierMath-2025-02-28-Private
DeepSeek V3.2 leads by +19.7
FrontierMath (Feb 2025) · original research-level math problems created by mathematicians, testing capabilities at the boundary of current AI mathematical reasoning.
DeepSeek V3.2
22.1
GLM 4.7
2.4
FrontierMath-Tier-4-2025-07-01-Private
DeepSeek V3.2 leads by +2.1
FrontierMath Tier 4 (Jul 2025) · the most challenging tier of frontier mathematics, containing problems that push the absolute limits of AI mathematical reasoning.
DeepSeek V3.2
2.1
GLM 4.7
0.0
GPQA diamond
DeepSeek V3.2 leads by +0.1
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
77.9
GLM 4.7
77.8
LiveBench · Agentic Coding
DeepSeek V3.2 leads by +5.0
DeepSeek V3.2
46.7
GLM 4.7
41.7
LiveBench · Coding
DeepSeek V3.2 leads by +2.6
DeepSeek V3.2
75.7
GLM 4.7
73.1
LiveBench · Data Analysis
GLM 4.7 leads by +10.1
DeepSeek V3.2
45.0
GLM 4.7
55.2
LiveBench · If
GLM 4.7 leads by +12.6
DeepSeek V3.2
23.1
GLM 4.7
35.7
LiveBench · Language
GLM 4.7 leads by +1.0
DeepSeek V3.2
64.2
GLM 4.7
65.2
LiveBench · Mathematics
GLM 4.7 leads by +12.1
DeepSeek V3.2
64.0
GLM 4.7
76.0
LiveBench · Overall
GLM 4.7 leads by +6.3
DeepSeek V3.2
51.8
GLM 4.7
58.1
LiveBench · Reasoning
GLM 4.7 leads by +15.5
DeepSeek V3.2
44.3
GLM 4.7
59.7
OpenCompass · AIME2025
GLM 4.7 leads by +2.4
DeepSeek V3.2
93.0
GLM 4.7
95.4
OpenCompass · GPQA-Diamond
GLM 4.7 leads by +2.3
DeepSeek V3.2
84.6
GLM 4.7
86.9
OpenCompass · HLE
GLM 4.7 leads by +2.2
DeepSeek V3.2
23.2
GLM 4.7
25.4
OpenCompass · IFEval
GLM 4.7 leads by +0.5
DeepSeek V3.2
89.7
GLM 4.7
90.2
OpenCompass · LiveCodeBenchV6
GLM 4.7 leads by +8.4
DeepSeek V3.2
75.4
GLM 4.7
83.8
OpenCompass · MMLU-Pro
DeepSeek V3.2 leads by +1.8
DeepSeek V3.2
85.8
GLM 4.7
84.0
OTIS Mock AIME 2024-2025
DeepSeek V3.2 leads by +4.5
OTIS Mock AIME 2024-2025 · simulated American Invitational Mathematics Examination problems testing advanced problem-solving skills.
DeepSeek V3.2
87.8
GLM 4.7
83.3
SimpleQA Verified
GLM 4.7 leads by +4.0
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 4.7
31.5
Terminal Bench
DeepSeek V3.2 leads by +6.2
Terminal-Bench 2.0 · evaluates AI agents on real terminal-based coding tasks · writing scripts, debugging, running tests, and managing projects entirely through command-line interaction. Tests both code quality and terminal fluency. Claude Opus 4.7 scores 69.4%, demonstrating significant agentic terminal competence.
DeepSeek V3.2
39.6
GLM 4.7
33.4
Full benchmark table
| Benchmark | DeepSeek V3.2 | GLM 4.7 |
|---|---|---|
APEX-Agents APEX-Agents · evaluates AI agents on complex, multi-step tasks requiring planning, tool use, and autonomous decision-making in realistic environments. | 7.0 | 3.1 |
Chatbot Arena Elo · Coding | 1331.9 | 1440.0 |
Chatbot Arena Elo · Overall | 1425.0 | 1442.6 |
Chess Puzzles Chess Puzzles · tests strategic and tactical reasoning by having models solve chess puzzle positions, evaluating lookahead and pattern recognition abilities. | 14.0 | 6.0 |
FrontierMath-2025-02-28-Private FrontierMath (Feb 2025) · original research-level math problems created by mathematicians, testing capabilities at the boundary of current AI mathematical reasoning. | 22.1 | 2.4 |
FrontierMath-Tier-4-2025-07-01-Private FrontierMath Tier 4 (Jul 2025) · the most challenging tier of frontier mathematics, containing problems that push the absolute limits of AI mathematical reasoning. | 2.1 | 0.0 |
GPQA diamond Graduate-Level Google-Proof QA (Diamond set) · expert-crafted questions in physics, biology, and chemistry that are difficult even for domain PhDs. | 77.9 | 77.8 |
LiveBench · Agentic Coding | 46.7 | 41.7 |
LiveBench · Coding | 75.7 | 73.1 |
LiveBench · Data Analysis | 45.0 | 55.2 |
LiveBench · If | 23.1 | 35.7 |
LiveBench · Language | 64.2 | 65.2 |
LiveBench · Mathematics | 64.0 | 76.0 |
LiveBench · Overall | 51.8 | 58.1 |
LiveBench · Reasoning | 44.3 | 59.7 |
OpenCompass · AIME2025 | 93.0 | 95.4 |
OpenCompass · GPQA-Diamond | 84.6 | 86.9 |
OpenCompass · HLE | 23.2 | 25.4 |
OpenCompass · IFEval | 89.7 | 90.2 |
OpenCompass · LiveCodeBenchV6 | 75.4 | 83.8 |
OpenCompass · MMLU-Pro | 85.8 | 84.0 |
OTIS Mock AIME 2024-2025 OTIS Mock AIME 2024-2025 · simulated American Invitational Mathematics Examination problems testing advanced problem-solving skills. | 87.8 | 83.3 |
SimpleQA Verified SimpleQA Verified · short factual questions with verified answers, measuring factual accuracy and the tendency to hallucinate or provide incorrect information. | 27.5 | 31.5 |
Terminal Bench Terminal-Bench 2.0 · evaluates AI agents on real terminal-based coding tasks · writing scripts, debugging, running tests, and managing projects entirely through command-line interaction. Tests both code quality and terminal fluency. Claude Opus 4.7 scores 69.4%, demonstrating significant agentic terminal competence. | 39.6 | 33.4 |
Pricing · per 1M tokens · projected $/mo at 10M tokens
| Model | Input | Output | Context | Projected $/mo |
|---|---|---|---|---|
| $0.21 | $0.32 | 131K tokens (~66 books) | $2.41 | |
| $0.40 | $1.75 | 203K tokens (~101 books) | $7.38 |