GLM-5.1
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
According to BenchLM.ai, GLM-5.1 ranks #34 out of 124 models on the provisional leaderboard with an overall score of 74/100. It also ranks #30 out of 33 on the verified leaderboard. While not a frontier model, it offers specific advantages depending on the use case.
GLM-5.1 is a open weight model with a 203K 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-5.1 sits inside the GLM-5 family alongside GLM-5, GLM-5.2, GLM-5 (Reasoning), GLM-5V-Turbo, GLM-5-Turbo. BenchLM links it directly to GLM-5 as the earlier related model in that lineage. This profile currently has 34 of 249 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 Instruction Following (#9), while its weakest is Reasoning (#32). This performance profile makes it a well-rounded choice across a range of tasks.
Ranking Distribution
Category rank across 6 benchmark categories — sorted by best rank
Category Performance
Scores across all benchmark categories (0-100 scale)
Category Breakdown
Agentic
Coding
#20Reasoning
#32Knowledge
#12Math
#13Multilingual
Multimodal
Inst. Following
#9Chatbot 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.
GLM-5 Family
snapshot · 5.1
Compare This Model
See how GLM-5.1 stacks up against similar models
Frequently Asked Questions
How does GLM-5.1 perform overall in AI benchmarks?
GLM-5.1 currently ranks #34 out of 124 models on BenchLM's provisional leaderboard with an overall score of 74. It also ranks #30 out of 33 on the verified leaderboard. It is created by Z.AI and features a 203K context window.
Is GLM-5.1 good for knowledge and understanding?
GLM-5.1 ranks #12 out of 124 models in knowledge and understanding benchmarks with an average score of 83. There are stronger options in this category.
Is GLM-5.1 good for coding and programming?
GLM-5.1 ranks #20 out of 124 models in coding and programming benchmarks with an average score of 81.3. There are stronger options in this category.
Is GLM-5.1 good for mathematics?
GLM-5.1 ranks #13 out of 124 models in mathematics benchmarks with an average score of 91.3. There are stronger options in this category.
Is GLM-5.1 good for reasoning and logic?
GLM-5.1 ranks #32 out of 124 models in reasoning and logic benchmarks with an average score of 60.5. There are stronger options in this category.
Is GLM-5.1 good for agentic tool use and computer tasks?
GLM-5.1 has visible benchmark coverage in agentic tool use and computer tasks, but BenchLM does not currently assign it a global category rank there.
Is GLM-5.1 good for multimodal and grounded tasks?
GLM-5.1 has visible benchmark coverage in multimodal and grounded tasks, but BenchLM does not currently assign it a global category rank there.
Is GLM-5.1 good for instruction following?
GLM-5.1 ranks #9 out of 124 models in instruction following benchmarks with an average score of 93.8. It is among the top performers in this category.
Is GLM-5.1 open source?
Yes, GLM-5.1 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-5.1?
GLM-5.1 belongs to the GLM-5 family. Related variants on BenchLM include GLM-5, GLM-5.2, GLM-5 (Reasoning), GLM-5V-Turbo, GLM-5-Turbo.
Does GLM-5.1 have full benchmark coverage on BenchLM?
Not yet. GLM-5.1 currently has 34 published benchmark scores out of the 249 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-5.1?
GLM-5.1 has a context window of 203K, which determines how much text it can process in a single interaction.
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