GLM-5
According to BenchLM.ai, GLM-5 ranks #40 out of 119 models on the provisional leaderboard with an overall score of 67/100. It also ranks #21 out of 28 on the verified leaderboard. While not a frontier model, it offers specific advantages depending on the use case.
GLM-5 is a open weight model with a 200K token context window. It processes queries without explicit chain-of-thought reasoning, offering faster response times and lower token usage.
GLM-5 sits inside the GLM-5 family alongside GLM-5.1, GLM-5 (Reasoning), GLM-5V-Turbo, GLM-5-Turbo. This profile currently has 51 of 225 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 (#13), while its weakest is Multimodal & Grounded (#59). This performance profile makes it particularly effective for knowledge-intensive tasks like research, analysis, and factual Q&A.
Ranking Distribution
Category rank across 8 benchmark categories — sorted by best rank
Category Performance
Scores across all benchmark categories (0-100 scale)
Category Breakdown
Agentic
#47Coding
#28Reasoning
#33Knowledge
#13Math
#13Multilingual
#29Multimodal
#59Inst. Following
#24Chatbot Arena Performance
Benchmark Details
Only benchmark rows with an attached exact-source record are shown here. Source-unverified manual rows and generated rows are hidden from model pages.
Compare This Model
See how GLM-5 stacks up against similar models
Frequently Asked Questions
How does GLM-5 perform overall in AI benchmarks?
GLM-5 currently ranks #40 out of 119 models on BenchLM's provisional leaderboard with an overall score of 67. It also ranks #21 out of 28 on the verified leaderboard. It is created by Z.AI and features a 200K context window.
Is GLM-5 good for knowledge and understanding?
GLM-5 ranks #13 out of 119 models in knowledge and understanding benchmarks with an average score of 82.7. There are stronger options in this category.
Is GLM-5 good for coding and programming?
GLM-5 ranks #28 out of 119 models in coding and programming benchmarks with an average score of 76.5. There are stronger options in this category.
Is GLM-5 good for mathematics?
GLM-5 ranks #13 out of 119 models in mathematics benchmarks with an average score of 91.3. There are stronger options in this category.
Is GLM-5 good for reasoning and logic?
GLM-5 ranks #33 out of 119 models in reasoning and logic benchmarks with an average score of 60. There are stronger options in this category.
Is GLM-5 good for agentic tool use and computer tasks?
GLM-5 ranks #47 out of 119 models in agentic tool use and computer tasks benchmarks with an average score of 50.2. There are stronger options in this category.
Is GLM-5 good for instruction following?
GLM-5 ranks #24 out of 119 models in instruction following benchmarks with an average score of 84.3. There are stronger options in this category.
Is GLM-5 good for multilingual tasks?
GLM-5 ranks #29 out of 119 models in multilingual tasks benchmarks with an average score of 73.2. There are stronger options in this category.
Is GLM-5 open source?
Yes, GLM-5 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?
GLM-5 belongs to the GLM-5 family. Related variants on BenchLM include GLM-5.1, GLM-5 (Reasoning), GLM-5V-Turbo, GLM-5-Turbo.
Does GLM-5 have full benchmark coverage on BenchLM?
Not yet. GLM-5 currently has 51 published benchmark scores out of the 225 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?
GLM-5 has a context window of 200K, which determines how much text it can process in a single interaction.
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