Model profile
GLM-5.2
Evidence coverage
43 of 323 tracked benchmarks are published. 18 are verified and 25 provisional. 7 of 8 categories are measured.
- Published / tracked
- 43 / 323
- Verified
- 18
- Provisional
- 25
- Categories with evidence
- 7 / 8
Evidence by category
- Agentic17 benchmarksMixed evidence
- Coding7 benchmarksMixed evidence
- Reasoning2 benchmarksMixed evidence
- Knowledge11 benchmarksMixed evidence
- Math4 benchmarksVerified
- Multilingual0 benchmarksNot measured
- Multimodal1 benchmarkReported
- Inst. Following1 benchmarkReported
GLM-5.2 ranks #37 out of 200 models on the public leaderboard with an overall score of 63.96/100. It does not yet have enough sourced coverage for BenchLM's verified leaderboard. While not a frontier model, it offers specific advantages depending on the use case.
GLM-5.2 is a open weight model with a 1M token context window. It uses explicit chain-of-thought reasoning, which typically improves performance on math and complex reasoning tasks at the cost of higher latency and token usage.
GLM-5.2 sits inside the GLM-5 family alongside GLM-5, GLM-5.1, GLM-5 (Reasoning), GLM-5-Turbo, GLM-5V-Turbo. BenchLM links it directly to GLM-5.1 as the earlier related model in that lineage. This profile currently has 43 of 323 tracked benchmarks. BenchLM only exposes non-generated benchmark rows publicly, so missing categories stay blank until a sourced evaluation is available.
Its strongest category is Knowledge (#6), while its weakest is Agentic (#24). This performance profile makes it particularly effective for knowledge-intensive tasks like research, analysis, and factual Q&A.
Peer position
Exact provisional scores and ranks for the closest listed peers. A score can appear before a model clears the evidence threshold for a rank, so equal scores can have different rank states.
Range 63.5–64.75
- Gemini 3.5 FlashGoogleCompare#3364.75Gemini 3.5 Flash is #33 with a score of 64.75.
- Claude Opus 4.5AnthropicCompare#3464.22Claude Opus 4.5 is #34 with a score of 64.22.
- Claude Opus 4.6 (Adaptive)AnthropicCompare#3564.18Claude Opus 4.6 (Adaptive) is #35 with a score of 64.18.
- MiniMax M2.7MiniMaxCompare#3664.11MiniMax M2.7 is #36 with a score of 64.11.
- GLM-5.2Current modelZ.AI#3763.96GLM-5.2 is #37 with a score of 63.96.
- GPT-5.5 ProOpenAICompare#3863.69GPT-5.5 Pro is #38 with a score of 63.69.
- GLM-5V-TurboZ.AICompare#3963.5GLM-5V-Turbo is #39 with a score of 63.5.
Category percentile
More
Relative position among models eligible for each sourced category. A higher percentile means a stronger position within that category's ranked cohort; 100 is highest.
- Knowledge90%Eligible cohort rank #6 of 52Category score 87.0
- Coding91%Eligible cohort rank #12 of 122Category score 65.1
- Agentic81%Eligible cohort rank #24 of 119Category score 54.6
Category evidence
Scores and ranks appear only where this model has published benchmark evidence. Categories without displayable source records remain not measured.
| Category | Score | Rank | Percentile | Weight | Benchmarks | Evidence |
|---|---|---|---|---|---|---|
| AgenticRank #24 of 119Percentile 81stWeight 22%17 benchmarksMixed sources | 54.6 | #24 of 119 | 81st | 22% | 17 benchmarks | Mixed sources |
| CodingRank #12 of 122Percentile 91stWeight 20%7 benchmarksMixed sources | 65.1 | #12 of 122 | 91st | 20% | 7 benchmarks | Mixed sources |
| ReasoningWeight 17%2 benchmarksMixed sources | Score pending | Not ranked | Not available | 17% | 2 benchmarks | Mixed sources |
| KnowledgeRank #6 of 52Percentile 90thWeight 12%11 benchmarksMixed sources | 87.0 | #6 of 52 | 90th | 12% | 11 benchmarks | Mixed sources |
| MathRank Not rankedWeight 5%4 benchmarksVerified | 81.4 | Not ranked | Not available | 5% | 4 benchmarks | Verified |
| MultilingualWeight 7%0 benchmarksNot measured | Not measured | Not ranked | Not available | 7% | 0 benchmarks | Not measured |
| MultimodalWeight 12%1 benchmarkReported | Score pending | Not ranked | Not available | 12% | 1 benchmark | Reported |
| Inst. FollowingWeight 5%1 benchmarkReported | Score pending | Not ranked | Not available | 5% | 1 benchmark | Reported |
Chatbot Arena performance
Scroll horizontally to inspect confidence intervals and vote counts.
| View | Elo | Confidence interval | Votes |
|---|---|---|---|
| Text Overall | 1470 | ±5.9 | 17,103 |
| Coding | 1511 | ±9.3 | 4,838 |
| Math | 1475 | ±20.2 | 829 |
| Instruction Following | 1464 | ±8.5 | 5,859 |
| Creative Writing | 1447 | ±11.5 | 3,049 |
| Multi-turn | 1471 | ±11.7 | 2,814 |
| Hard Prompts | 1490 | ±7.0 | 11,254 |
| Hard Prompts (English) | 1490 | ±8.8 | 5,407 |
| Longer Query | 1481 | ±8.0 | 7,693 |
Benchmark Details
Rows below have a displayable published verification record. Each source link and provenance note remains in the page HTML while its category is closed. Source-unverified manual rows and generated rows stay hidden.
Agentic17 benchmarks
Artificial Analysis Agentic Index
τ²-Bench Tool-Agent-User Evaluation
GDPval-AA normalized
Artificial Analysis Briefcase
Artificial Analysis AutomationBench
Artificial Analysis EnterpriseOps-Gym
Artificial Analysis Harvey LAB-AA
Artificial Analysis ITBench-AA
Artificial Analysis Tau3-Banking
Coding7 benchmarks
ProgramBench: Can Language Models Rebuild Programs From Scratch?
Artificial Analysis Coding Index
Artificial Analysis SciCode
Reasoning2 benchmarks
Critical Physics Tasks
Artificial Analysis Long Context Reasoning
Knowledge11 benchmarks
Humanity's Last Exam
Graduate-Level Google-Proof Q&A
GPQA Diamond
Humanity's Last Exam without tools
Artificial Analysis GPQA Diamond
Artificial Analysis Humanity's Last Exam
Artificial Analysis Omniscience Index
Artificial Analysis Omniscience Accuracy
Artificial Analysis Omniscience Hallucination Rate
Artificial Analysis Openness Index
Math4 benchmarks
AIME 2026
Harvard-MIT Mathematics Tournament February 2026
Harvard-MIT Mathematics Tournament November 2025
Multimodal1 benchmark
Design Arena Website Elo
Inst. Following1 benchmark
Artificial Analysis IFBench
GLM-5 Family
flagship · 5.2
Frequently Asked Questions
How does GLM-5.2 perform overall in AI benchmarks?
GLM-5.2 currently ranks #37 out of 200 models on BenchLM's provisional leaderboard with an overall score of 63.96. It is created by Z.AI. Its published context window is 1M.
Is GLM-5.2 good for knowledge and understanding?
GLM-5.2 ranks #6 out of 52 models in knowledge and understanding benchmarks with an average score of 87. It is among the top performers in this category.
Is GLM-5.2 good for coding and programming?
GLM-5.2 ranks #12 out of 122 models in coding and programming benchmarks with an average score of 65.1. There are stronger options in this category.
Is GLM-5.2 good for mathematics?
GLM-5.2 has visible benchmark coverage in mathematics, but BenchLM does not currently assign it a global category rank there.
Is GLM-5.2 good for reasoning and logic?
GLM-5.2 has visible benchmark coverage in reasoning and logic, but BenchLM does not currently assign it a global category rank there.
Is GLM-5.2 good for agentic tool use and computer tasks?
GLM-5.2 ranks #24 out of 119 models in agentic tool use and computer tasks benchmarks with an average score of 54.6. There are stronger options in this category.
Is GLM-5.2 good for multimodal and grounded tasks?
GLM-5.2 has visible benchmark coverage in multimodal and grounded tasks, but BenchLM does not currently assign it a global category rank there.
Is GLM-5.2 good for instruction following?
GLM-5.2 has visible benchmark coverage in instruction following, but BenchLM does not currently assign it a global category rank there.
Is GLM-5.2 open source?
Yes, GLM-5.2 is an open weight model created by Z.AI, meaning it can be downloaded and run locally or fine-tuned for specific use cases.
Which sibling models are related to GLM-5.2?
GLM-5.2 belongs to the GLM-5 family. Related variants on BenchLM include GLM-5, GLM-5.1, GLM-5 (Reasoning), GLM-5-Turbo, GLM-5V-Turbo.
Does GLM-5.2 have full benchmark coverage on BenchLM?
Not yet. GLM-5.2 currently has 43 published benchmark scores out of the 323 benchmarks BenchLM tracks. BenchLM only exposes non-generated public benchmark rows, so missing categories stay blank until a sourced evaluation is available.
What is the context window size of GLM-5.2?
GLM-5.2 has a published context window of 1M, which determines how much text it can process in a single interaction.
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