[whatmodel.sh](../../README.md) / GLM-4.6V # GLM-4.6V Z.ai · released 2025-12 · 106B params · mit **score 62.0** ±1.6 · #184 of 732 · from 10 benchmark results across 3 sources Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only. ## What it's good at ● marks GLM-4.6V among every model on that task or test, worst to best. 
task                vs the others          rank  counts  tests
chat & writing      ▁▁▁▃▆▄▆█●▇▇▃   #135 of 402*     28%      1
vision & documents  ▁▁▂▂▁▃▄●▆▇█▄     #94 of 153     60%      7
chat & writing · #135 of 402 · 28% of its score Beats [LongCat 2.0 (1.6T-A48B)](longcat-2-0.md), [o3-mini](o3-mini.md), [K-EXAONE (236B-A23B)](k-exaone-236b-a23b.md) here, though they rank higher overall. Trails [LFM2.5 8B-A1B](lfm2-5-8b-a1b.md), [LFM2.5 2.6B](lfm2-5-2-6b.md), [Nemotron-Cascade-8B](nemotron-cascade-8b.md) here, though they rank lower overall. 
test            result  vs the others in its run    difficulty  counts  why
LMArena (text)  1378.4  ▁▁▁▂▃▃▃▆●▇█▃ beats 219/385  avg 1260       28%  blunt
vision & documents · #94 of 153 · 60% of its score Beats [DeepSeek V4 Flash Vision (Exp)](deepseek-v4-flash-vision-exp.md) here, though they rank higher overall. Trails [Mistral Small 4 (119B-A6.5B)](mistral-small-4.md), [Qwen3-VL 32B Instruct](qwen3-vl-32b-instruct.md), [Qwen3.5 4B](qwen3-5-4b.md) here, though they rank lower overall. 
test                                result  vs the others in its run   difficulty        counts  why
Arena vision (style controlled)     1161.4  ▁▁▂▂▂▂▃●▅▅█▇ beats 56/152  avg 1061             19%  blunt
CharXiv (reasoning questions) (ma…   63.2%  ▂▅▃▅▅▆●▅▆█▆▂ beats 48/101  medium · avg 36%    9.3%  at its level · small run
SciDocBench scientific-document w…   42.4%  ▃●···█▃▅▃·▃▅ beats 1/11    hard · avg 25%      9.1%  blunt · at its level
MMMU (val) (maker)                   76.0%  ▁····▁▁▃█▆●▅ beats 42/53   medium · avg 57%    7.9%  at its level · small run
MMMU Pro (maker)                     66.0%  ▂▃▁▃▆▇▃●▇██▅ beats 26/52   medium · avg 46%    7.5%  at its level · small run
MMStar (maker)                       75.9%  ▃▅▂▃▂▂▆█●▆█▅ beats 23/35   medium · avg 62%    5.9%  at its level · small run
OCRBench (maker)                     86.5%  ▁·▃▁▆▅▂●▅█▁▃ beats 21/37   easy · avg 82%      1.8%  blunt · small run
other tests (2) 
test                  result  vs the others in its run  difficulty        counts  why
LVBench (maker)        59.5%  ▇·█●▅▄▄··▃▁▁ beats 12/28  hard · avg 48%      5.2%  at its level · small run
AndroidWorld (maker)   57.0%  ▁·▁▁▂▃●▅▂▂·▂ beats 11/24  medium · avg 40%    4.8%  at its level · small run
## How the score was worked out Each result implies a score on its own, after adjusting for how hard that test is and who ran it. The model's score is their weighted average, with a small pull toward the middle for thinly tested models. Each test's weight is its *counts* column above; here it is added up by who ran the tests. [Method](../method.md) 
source                                    results  share of the score
LMArena leaderboard dataset                     2   47% ▇▇▇▇▇▇
Z.ai GLM-V GitHub repository benchmark …        7   42% ▇▇▇▇▇
SciDocBench: A Workflow-Centered Benchm…        1    9% ▇
prior: pull toward the middle                        1% ▇
weighted average                                   = 62.0
Licences: LMArena leaderboard dataset ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · Z.ai GLM-V GitHub repository benchmark image ([Apache-2.0](https://github.com/zai-org/GLM-V/blob/9013a771cfe578d8a0c7dfc4548156f3a23e4890/LICENSE)) · SciDocBench: A Workflow-Centered Benchmark and Data Pipeline for Scientific Document Understanding ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) ## How to run it ### Through an API api (Z.ai) · $0.45/M blended · $0.3 in / $0.9 out per 1M tokens · [docs.z.ai](https://docs.z.ai/guides/vlm/glm-4.6v.md) [price source](https://docs.z.ai/guides/overview/pricing): USD per 1M tokens, Z.ai API pricing page, Vision Models table, GLM-4.6V: input $0.3, output $0.9; cached input $0.05 excluded. Currency: USD. Z.ai official pricing page, observed 2026-09-28. · [context source](https://huggingface.co/zai-org/GLM-4.6V/blob/4e2d47eb0b41c5280d8294b17cef9e94fdcfff46/README.md): Context length stated on the official model card. ### llama.cpp (recommended) 1. Install llama.cpp. Homebrew and winget installs name the commands llama-cli and llama-server instead. [Other ways to install](https://github.com/ggml-org/llama.cpp/blob/master/docs/install.md) ```sh # macOS / Linux curl -LsSf https://llama.app/install.sh | sh # Windows irm https://llama.app/install.ps1 | iex ``` 2. Run GLM-4.6V (ud-q4_k_xl, needs ~67.22 GB, from [huggingface.co/unsloth](https://huggingface.co/unsloth/GLM-4.6V-GGUF/tree/7bea17af309193ebd3dd31edbc40a7ddc301cf35)) ```sh llama cli -hf unsloth/GLM-4.6V-GGUF:UD-Q4_K_XL ``` 3. Or start a local server and open the address it prints (http://localhost:9931) ```sh llama serve -hf unsloth/GLM-4.6V-GGUF:UD-Q4_K_XL ``` Other versions: ud-q2_k_xl (~47.55 GB) `llama cli -hf unsloth/GLM-4.6V-GGUF:UD-Q2_K_XL` ### LM Studio 1. Install LM Studio. Open the app once before using the lms command. [Download it (macOS / Windows / Linux)](https://lmstudio.ai/download) 2. Run GLM-4.6V (ud-q4_k_xl, needs ~67.22 GB, from [huggingface.co/unsloth](https://huggingface.co/unsloth/GLM-4.6V-GGUF/tree/7bea17af309193ebd3dd31edbc40a7ddc301cf35)) ```sh lms get https://huggingface.co/unsloth/GLM-4.6V-GGUF@UD-Q4_K_XL -y ``` 3. Then run lms ls to see its name, and lms chat followed by that name Other versions: ud-q2_k_xl (~47.55 GB) `lms get https://huggingface.co/unsloth/GLM-4.6V-GGUF@UD-Q2_K_XL -y` Model repository and documentation: [huggingface.co/zai-org](https://huggingface.co/zai-org/GLM-4.6V) [interactive version](https://whatmodel.sh/model/glm-4-6v) · [JSON](https://whatmodel.sh/api?format=json&all=1)