Model profile
GLM-5.1
Evidence coverage
36 of 323 tracked benchmarks are published. 20 are verified and 16 provisional. 7 of 8 categories are measured.
- Published / tracked
- 36 / 323
- Verified
- 20
- Provisional
- 16
- Categories with evidence
- 7 / 8
Evidence by category
- Agentic12 benchmarksMixed evidence
- Coding6 benchmarksMixed evidence
- Reasoning2 benchmarksReported
- Knowledge8 benchmarksMixed evidence
- Math6 benchmarksVerified
- Multilingual0 benchmarksNot measured
- Multimodal1 benchmarkReported
- Inst. Following1 benchmarkReported
GLM-5.1 ranks #18 out of 200 models on the public leaderboard with an overall score of 67.74/100. It also ranks #16 out of 99 on the verified leaderboard. This places it in the mid-tier of AI models, with strengths in specific benchmark categories.
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-5-Turbo, GLM-5V-Turbo. BenchLM links it directly to GLM-5 as the earlier related model in that lineage. This profile currently has 36 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 Mathematics (#3), while its weakest is Agentic (#54). This performance profile makes it particularly strong for mathematical reasoning, scientific computing, and quantitative analysis.
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 67.01–67.78
- MiMo-V2-ProXiaomiCompare#1767.78MiMo-V2-Pro is #17 with a score of 67.78.
- GLM-5.1Current modelZ.AI#1867.74GLM-5.1 is #18 with a score of 67.74.
- Gemini 3 ProGoogleCompare#1967.73Gemini 3 Pro is #19 with a score of 67.73.
- InklingThinking Machines LabCompare#2067.54Inkling is #20 with a score of 67.54.
- Qwen3.7 PlusAlibabaCompare#2167.22Qwen3.7 Plus is #21 with a score of 67.22.
- GPT-5.6 LunaOpenAICompare#2267.17GPT-5.6 Luna is #22 with a score of 67.17.
- GPT-5.2 ProOpenAICompare#2367.01GPT-5.2 Pro is #23 with a score of 67.01.
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.
- Math67%Eligible cohort rank #3 of 7Category score 64.6
- Coding76%Eligible cohort rank #30 of 122Category score 57.5
- Agentic55%Eligible cohort rank #54 of 119Category score 48.6
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 #54 of 119Percentile 55thWeight 22%12 benchmarksMixed sources | 48.6 | #54 of 119 | 55th | 22% | 12 benchmarks | Mixed sources |
| CodingRank #30 of 122Percentile 76thWeight 20%6 benchmarksMixed sources | 57.5 | #30 of 122 | 76th | 20% | 6 benchmarks | Mixed sources |
| ReasoningRank Not rankedWeight 17%2 benchmarksReported | 66.3 | Not ranked | Not available | 17% | 2 benchmarks | Reported |
| KnowledgeRank Not rankedWeight 12%8 benchmarksMixed sources | 82.3 | Not ranked | Not available | 12% | 8 benchmarks | Mixed sources |
| MathRank #3 of 7Percentile 67thWeight 5%6 benchmarksVerified | 64.6 | #3 of 7 | 67th | 5% | 6 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. FollowingRank Not rankedWeight 5%1 benchmarkReported | 92.6 | Not ranked | Not available | 5% | 1 benchmark | Reported |
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
Chatbot Arena performance
Scroll horizontally to inspect confidence intervals and vote counts.
| View | Elo | Confidence interval | Votes |
|---|---|---|---|
| Text Overall | 1470 | ±4.6 | 29,919 |
| Coding | 1521 | ±7.4 | 8,422 |
| Math | 1481 | ±15.3 | 1,559 |
| Instruction Following | 1464 | ±6.8 | 10,098 |
| Creative Writing | 1453 | ±9.2 | 5,126 |
| Multi-turn | 1483 | ±9.2 | 4,945 |
| Hard Prompts | 1492 | ±5.5 | 19,738 |
| Hard Prompts (English) | 1503 | ±7.0 | 9,647 |
| Longer Query | 1484 | ±6.5 | 13,101 |
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.
Agentic12 benchmarks
τ³-Bench Tool-Agent-User Evaluation
Artificial Analysis Agentic Index
τ²-Bench Tool-Agent-User Evaluation
GDPval-AA normalized
Gert Labs Composite Game Benchmark
Coding6 benchmarks
Vibe Code Bench v1.1
Artificial Analysis Coding Index
Artificial Analysis SciCode
Reasoning2 benchmarks
Artificial Analysis Long Context Reasoning
Critical Physics Tasks
Knowledge8 benchmarks
Humanity's Last Exam
GPQA Diamond
Artificial Analysis GPQA Diamond
Artificial Analysis Humanity's Last Exam
Artificial Analysis Omniscience Index
Artificial Analysis Omniscience Accuracy
Artificial Analysis Omniscience Hallucination Rate
Math6 benchmarks
FrontierMath v2 Tiers 1-3
AIME 2026
Harvard-MIT Mathematics Tournament February 2026
FrontierMath v2 Tier 4
Harvard-MIT Mathematics Tournament November 2025
Multimodal1 benchmark
Design Arena Website Elo
Inst. Following1 benchmark
Artificial Analysis IFBench
GLM-5 Family
snapshot · 5.1
Frequently Asked Questions
How does GLM-5.1 perform overall in AI benchmarks?
GLM-5.1 currently ranks #18 out of 200 models on BenchLM's provisional leaderboard with an overall score of 67.74. It also ranks #16 out of 99 on the verified leaderboard. It is created by Z.AI. Its published context window is 203K.
Is GLM-5.1 good for knowledge and understanding?
GLM-5.1 has visible benchmark coverage in knowledge and understanding, but BenchLM does not currently assign it a global category rank there.
Is GLM-5.1 good for coding and programming?
GLM-5.1 ranks #30 out of 122 models in coding and programming benchmarks with an average score of 57.5. There are stronger options in this category.
Is GLM-5.1 good for mathematics?
GLM-5.1 ranks #3 out of 7 models in mathematics benchmarks with an average score of 64.6. It is among the top performers in this category.
Is GLM-5.1 good for reasoning and logic?
GLM-5.1 has visible benchmark coverage in reasoning and logic, but BenchLM does not currently assign it a global category rank there.
Is GLM-5.1 good for agentic tool use and computer tasks?
GLM-5.1 ranks #54 out of 119 models in agentic tool use and computer tasks benchmarks with an average score of 48.6. There are stronger options in this category.
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 has visible benchmark coverage in instruction following, but BenchLM does not currently assign it a global category rank there.
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-5-Turbo, GLM-5V-Turbo.
Does GLM-5.1 have full benchmark coverage on BenchLM?
Not yet. GLM-5.1 currently has 36 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-5.1?
GLM-5.1 has a published context window of 203K, which determines how much text it can process in a single interaction.
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