Model profile
GLM-4.6
Evidence coverage
14 of 323 tracked benchmarks are published. 3 are verified and 11 provisional. 6 of 8 categories are measured.
- Published / tracked
- 14 / 323
- Verified
- 3
- Provisional
- 11
- Categories with evidence
- 6 / 8
Evidence by category
- Agentic1 benchmarkReported
- Coding2 benchmarksMixed evidence
- Reasoning2 benchmarksReported
- Knowledge6 benchmarksReported
- Math2 benchmarksVerified
- Multilingual0 benchmarksNot measured
- Multimodal0 benchmarksNot measured
- Inst. Following1 benchmarkReported
GLM-4.6 ranks #85 out of 200 models on the public leaderboard with an overall score of 55.12/100. It also ranks #50 out of 99 on the verified leaderboard. While not a frontier model, it offers specific advantages depending on the use case.
GLM-4.6 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 14 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 Coding (#53). This performance profile makes it particularly well-suited for software development and code generation tasks.
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 55.0–55.47
- DeepSeek V4 Pro (High)DeepSeekCompare#8155.47DeepSeek V4 Pro (High) is #81 with a score of 55.47.
- DeepSeek V3.2DeepSeekCompare#8255.4DeepSeek V3.2 is #82 with a score of 55.4.
- Gemini 3.1 ProGoogleCompare#8355.3Gemini 3.1 Pro is #83 with a score of 55.3.
- GPT-5 (medium)OpenAICompare#8455.15GPT-5 (medium) is #84 with a score of 55.15.
- GLM-4.6Current modelZ.AI#8555.12GLM-4.6 is #85 with a score of 55.12.
- Step 3.5 FlashStepFunCompare#8655.1Step 3.5 Flash is #86 with a score of 55.1.
- Kimi K2.7 CodeMoonshot AICompare#8755.0Kimi K2.7 Code is #87 with a score of 55.0.
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.
- Coding57%Eligible cohort rank #53 of 122Category score 51.9
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 |
|---|---|---|---|---|---|---|
| AgenticWeight 22%1 benchmarkReported | Score pending | Not ranked | Not available | 22% | 1 benchmark | Reported |
| CodingRank #53 of 122Percentile 57thWeight 20%2 benchmarksMixed sources | 51.9 | #53 of 122 | 57th | 20% | 2 benchmarks | Mixed sources |
| ReasoningWeight 17%2 benchmarksReported | Score pending | Not ranked | Not available | 17% | 2 benchmarks | Reported |
| KnowledgeWeight 12%6 benchmarksReported | Score pending | Not ranked | Not available | 12% | 6 benchmarks | Reported |
| MathRank Not rankedWeight 5%2 benchmarksVerified | 27.7 | Not ranked | Not available | 5% | 2 benchmarks | Verified |
| MultilingualWeight 7%0 benchmarksNot measured | Not measured | Not ranked | Not available | 7% | 0 benchmarks | Not measured |
| MultimodalWeight 12%0 benchmarksNot measured | Not measured | Not ranked | Not available | 12% | 0 benchmarks | Not measured |
| 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 | 1425 | ±3.9 | 35,613 |
| Coding | 1459 | ±7.3 | 7,476 |
| Math | 1420 | ±12.8 | 2,106 |
| Instruction Following | 1415 | ±6.4 | 9,997 |
| Creative Writing | 1402 | ±8.6 | 5,082 |
| Multi-turn | 1421 | ±8.2 | 5,664 |
| Hard Prompts | 1442 | ±5.0 | 19,072 |
| Hard Prompts (English) | 1448 | ±6.5 | 9,608 |
| Longer Query | 1433 | ±6.7 | 8,746 |
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.
Agentic1 benchmark
τ²-Bench Tool-Agent-User Evaluation
Coding2 benchmarks
Vibe Code Bench v1.1
Artificial Analysis SciCode
Reasoning2 benchmarks
Artificial Analysis Long Context Reasoning
Critical Physics Tasks
Knowledge6 benchmarks
Artificial Analysis GPQA Diamond
Artificial Analysis Humanity's Last Exam
Artificial Analysis Omniscience Index
Artificial Analysis Omniscience Accuracy
Artificial Analysis Omniscience Hallucination Rate
Math2 benchmarks
FrontierMath v2 Tiers 1-3
FrontierMath v2 Tier 4
Inst. Following1 benchmark
Artificial Analysis IFBench
Frequently Asked Questions
How does GLM-4.6 perform overall in AI benchmarks?
GLM-4.6 has 14 published benchmark scores on BenchLM, but it does not yet have enough non-generated coverage to receive a global overall rank.
Is GLM-4.6 good for knowledge and understanding?
GLM-4.6 has visible benchmark coverage in knowledge and understanding, but BenchLM does not currently assign it a global category rank there.
Is GLM-4.6 good for coding and programming?
GLM-4.6 ranks #53 out of 122 models in coding and programming benchmarks with an average score of 51.9. There are stronger options in this category.
Is GLM-4.6 good for mathematics?
GLM-4.6 has visible benchmark coverage in mathematics, but BenchLM does not currently assign it a global category rank there.
Is GLM-4.6 good for reasoning and logic?
GLM-4.6 has visible benchmark coverage in reasoning and logic, but BenchLM does not currently assign it a global category rank there.
Is GLM-4.6 good for agentic tool use and computer tasks?
GLM-4.6 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-4.6 good for instruction following?
GLM-4.6 has visible benchmark coverage in instruction following, but BenchLM does not currently assign it a global category rank there.
Is GLM-4.6 open source?
Yes, GLM-4.6 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-4.6 have full benchmark coverage on BenchLM?
Not yet. GLM-4.6 currently has 14 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-4.6?
GLM-4.6 has a published context window of 200K, which determines how much text it can process in a single interaction.
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