Model profile
GLM-4.7
Evidence coverage
30 of 321 tracked benchmarks are published. 10 are verified and 20 provisional. 7 of 8 categories are measured.
- Published / tracked
- 30 / 321
- Verified
- 10
- Provisional
- 20
- Categories with evidence
- 7 / 8
Evidence by category
- Agentic8 benchmarksMixed evidence
- Coding6 benchmarksMixed evidence
- Reasoning2 benchmarksReported
- Knowledge9 benchmarksMixed evidence
- Math3 benchmarksMixed evidence
- Multilingual0 benchmarksNot measured
- Multimodal1 benchmarkReported
- Inst. Following1 benchmarkReported
GLM-4.7 ranks #42 out of 200 models on the public leaderboard with an overall score of 61.16/100. It also ranks #32 out of 99 on the verified leaderboard. While not a frontier model, it offers specific advantages depending on the use case.
GLM-4.7 is a open weight model with a 200K 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.
This profile currently has 30 of 321 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 Agentic (#40), while its weakest is Coding (#44). This performance profile makes it particularly useful for coding agents, browser research, and computer-use workflows.
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 60.56–61.31
- Gemini 3 Pro Deep ThinkGoogleCompare#4161.31Gemini 3 Pro Deep Think is #41 with a score of 61.31.
- GLM-4.7Current modelZ.AI#4261.16GLM-4.7 is #42 with a score of 61.16.
- Gemma 4 31BGoogleCompare#4361.08Gemma 4 31B is #43 with a score of 61.08.
- GPT-5.4 ProOpenAICompare#4460.89GPT-5.4 Pro is #44 with a score of 60.89.
- Qwen3.5-27BAlibabaCompare#4560.7Qwen3.5-27B is #45 with a score of 60.7.
- DeepSeek V4 ProDeepSeekCompare#4660.66DeepSeek V4 Pro is #46 with a score of 60.66.
- Qwen3.5-122B-A10BAlibabaCompare#4760.56Qwen3.5-122B-A10B is #47 with a score of 60.56.
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.
- Agentic67%Eligible cohort rank #40 of 119Category score 51.7
- Coding64%Eligible cohort rank #44 of 122Category score 53.1
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 #40 of 119Percentile 67thWeight 22%8 benchmarksMixed sources | 51.7 | #40 of 119 | 67th | 22% | 8 benchmarks | Mixed sources |
| CodingRank #44 of 122Percentile 64thWeight 20%6 benchmarksMixed sources | 53.1 | #44 of 122 | 64th | 20% | 6 benchmarks | Mixed sources |
| ReasoningRank Not rankedWeight 17%2 benchmarksReported | 78.6 | Not ranked | Not available | 17% | 2 benchmarks | Reported |
| KnowledgeRank Not rankedWeight 12%9 benchmarksMixed sources | 34.3 | Not ranked | Not available | 12% | 9 benchmarks | Mixed sources |
| MathRank Not rankedWeight 5%3 benchmarksMixed sources | 26.1 | Not ranked | Not available | 5% | 3 benchmarks | Mixed sources |
| MultilingualWeight 7%0 benchmarksNot measured | Not measured | Not ranked | Not available | 7% | 0 benchmarks | Not measured |
| MultimodalRank Not rankedWeight 12%1 benchmarkReported | 70.0 | Not ranked | Not available | 12% | 1 benchmark | Reported |
| Inst. FollowingRank Not rankedWeight 5%1 benchmarkReported | 88.0 | 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 | 1442 | ±6.1 | 12,098 |
| Coding | 1485 | ±12.1 | 2,410 |
| Math | 1428 | ±21.0 | 703 |
| Instruction Following | 1428 | ±10.4 | 3,209 |
| Creative Writing | 1405 | ±13.4 | 1,916 |
| Multi-turn | 1459 | ±13.6 | 1,919 |
| Hard Prompts | 1463 | ±7.8 | 6,598 |
| Hard Prompts (English) | 1473 | ±10.7 | 3,092 |
| Longer Query | 1452 | ±10.6 | 3,088 |
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.
Agentic8 benchmarks
Artificial Analysis Agentic Index
τ²-Bench Tool-Agent-User Evaluation
Gert Labs Composite Game Benchmark
GDPval-AA normalized
Coding6 benchmarks
LiveCodeBench: Holistic and Contamination Free Evaluation of Large Language Models for Code
Software Engineering Benchmark Verified
Artificial Analysis Coding Index
Artificial Analysis SciCode
Artificial Analysis LiveCodeBench
Reasoning2 benchmarks
Artificial Analysis Long Context Reasoning
Critical Physics Tasks
Knowledge9 benchmarks
Humanity's Last Exam
Massive Multitask Language Understanding Professional
Graduate-Level Google-Proof Q&A
Artificial Analysis GPQA Diamond
Artificial Analysis Humanity's Last Exam
Artificial Analysis Omniscience Index
Artificial Analysis Omniscience Accuracy
Artificial Analysis Omniscience Hallucination Rate
Math3 benchmarks
FrontierMath v2 Tiers 1-3
FrontierMath v2 Tier 4
American Invitational Mathematics Examination 2025
Multimodal1 benchmark
Design Arena Website Elo
Inst. Following1 benchmark
Artificial Analysis IFBench
Frequently Asked Questions
How does GLM-4.7 perform overall in AI benchmarks?
GLM-4.7 currently ranks #42 out of 200 models on BenchLM's provisional leaderboard with an overall score of 61.16. It also ranks #32 out of 99 on the verified leaderboard. It is created by Z.AI. Its published context window is 200K.
Is GLM-4.7 good for knowledge and understanding?
GLM-4.7 has visible benchmark coverage in knowledge and understanding, but BenchLM does not currently assign it a global category rank there.
Is GLM-4.7 good for coding and programming?
GLM-4.7 ranks #44 out of 122 models in coding and programming benchmarks with an average score of 53.1. There are stronger options in this category.
Is GLM-4.7 good for mathematics?
GLM-4.7 has visible benchmark coverage in mathematics, but BenchLM does not currently assign it a global category rank there.
Is GLM-4.7 good for reasoning and logic?
GLM-4.7 has visible benchmark coverage in reasoning and logic, but BenchLM does not currently assign it a global category rank there.
Is GLM-4.7 good for agentic tool use and computer tasks?
GLM-4.7 ranks #40 out of 119 models in agentic tool use and computer tasks benchmarks with an average score of 51.7. There are stronger options in this category.
Is GLM-4.7 good for multimodal and grounded tasks?
GLM-4.7 has visible benchmark coverage in multimodal and grounded tasks, but BenchLM does not currently assign it a global category rank there.
Is GLM-4.7 good for instruction following?
GLM-4.7 has visible benchmark coverage in instruction following, but BenchLM does not currently assign it a global category rank there.
Is GLM-4.7 open source?
Yes, GLM-4.7 is an open weight model created by Z.AI, meaning it can be downloaded and run locally or fine-tuned for specific use cases.
Does GLM-4.7 have full benchmark coverage on BenchLM?
Not yet. GLM-4.7 currently has 30 published benchmark scores out of the 321 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-4.7?
GLM-4.7 has a published context window of 200K, which determines how much text it can process in a single interaction.
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