[whatmodel.sh](../../README.md) / GLM-4 9B Chat # GLM-4 9B Chat Zhipu AI (Z.ai) · released 2024-06 · 9.4B params · glm-4 **score 48.4** ±1.7 · #404 of 732 · from 8 benchmark results across 7 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 9B Chat among every model on that task or test, worst to best. 
task         vs the others         rank  counts  tests
agent tasks  ▁▁●▆█▄▅▅▄▄▂▁   #297 of 313     19%      3
agent tasks · #297 of 313 · 19% of its score Trails [Granite 4.0 H 350M](granite-4-0-h-350m.md), [ChatGLM3-6B](chatglm3-6b.md), [Qwen3 0.6B](qwen3-0-6b.md) here, though they rank lower overall. 
test                                result  vs the others in its run   difficulty        counts  why
BFCL V4 (2025-08-26 archive, mode…   21.2%  ▄●····█▄···▄ beats 1/5     hard · avg 26%      7.2%  typical
BFCL v3                              39.1%  ▁▁▁▁●▃▅█▅▄▁▁ beats 12/114  medium · avg 51%    6.5%  blunt · at its level
BFCL (version not stated) (maker)    81.0%  █·········●█ beats 2/5     easy · avg 77%      5.5%  blunt
other tests (5) 
test                                          result  vs the others in its run    difficulty           counts  why
WildBench v2 WB-Score (task macro)  39.0990 WB-Score  ▁·▁▁·▃█▄●▆▅▆ beats 28/52    avg 43.019 WB-Score     31%  sharp
CPTUB average (SpeakLeash, 0-5 ju…             59.0%  ▁▂▂▂▃●▃▆▅▄▂▃ beats 28/95    medium · avg 65%        22%  at its level
Polish EQ-Bench v2 (SpeakLeash)                61.8%  ▁··▁▁▂▂▆▅●█▆ beats 43/70    medium · avg 62%        18%  at its level
Open PT-LLM Leaderboard average (…             75.0%  ▃▁▁▁▁▁▁▁▂█●▁ beats 225/276  medium · avg 72%       6.9%  at its level · noisy
Vectara hallucination leaderboard…             98.7%  ▁▁▂▃▄▄█▆▃▂●▁ beats 133/140  very easy · avg 94%    2.0%  blunt · maxed out · noisy
## 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
WildBench v2 leaderboard data                   1   31% ▇▇▇▇
SpeakLeash CPTUB                                1   22% ▇▇▇
SpeakLeash Polish EQ-Bench leaderboard          1   18% ▇▇
Berkeley Function Calling Leaderboard r…        2   14% ▇▇
Open Portuguese LLM Leaderboard                 1    7% ▇
ChatGLM: A Family of Large Language Mod…        1    5% ▇
Vectara hallucination leaderboard               1    2% ▇
prior: pull toward the middle                        1% ▇
weighted average                                   = 48.4
Licences: WildBench v2 leaderboard data ([Apache-2.0](https://github.com/allenai/WildBench/blob/d6b8dcaf377d173d031980f97c16e1a82618c03d/LICENSE)) · SpeakLeash CPTUB ([MIT](../../licenses/hf-leaderboard-spaces-MIT.txt)) · SpeakLeash Polish EQ-Bench leaderboard ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt)) · Berkeley Function Calling Leaderboard result archive ([Apache-2.0](../../licenses/bfcl-result-archive-v4-Apache-2.0.txt)) · Open Portuguese LLM Leaderboard ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt)) · ChatGLM: A Family of Large Language Models from GLM-130B to GLM-4 All Tools ([CC BY 4.0](http://creativecommons.org/licenses/by/4.0/)) · Vectara hallucination leaderboard ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0)) ## How to run it ### 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 9B Chat (Q4_K_M, needs ~6.3 GB, from [huggingface.co/bartowski](https://huggingface.co/bartowski/glm-4-9b-chat-GGUF/tree/74a33eafa149550a60bff0fdafcdd34caa6df9f0)) ```sh llama cli -hf bartowski/glm-4-9b-chat-GGUF:Q4_K_M ``` 3. Or start a local server and open the address it prints (http://localhost:9931) ```sh llama serve -hf bartowski/glm-4-9b-chat-GGUF:Q4_K_M ``` Other versions: Q8_0 (~10 GB) `llama cli -hf bartowski/glm-4-9b-chat-GGUF:Q8_0` ### 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 9B Chat (Q4_K_M, needs ~6.3 GB, from [huggingface.co/bartowski](https://huggingface.co/bartowski/glm-4-9b-chat-GGUF/tree/74a33eafa149550a60bff0fdafcdd34caa6df9f0)) ```sh lms get https://huggingface.co/bartowski/glm-4-9b-chat-GGUF@Q4_K_M -y ``` 3. Then run lms ls to see its name, and lms chat followed by that name Other versions: Q8_0 (~10 GB) `lms get https://huggingface.co/bartowski/glm-4-9b-chat-GGUF@Q8_0 -y` ### Ollama 1. Install Ollama. [Download it (macOS / Windows)](https://ollama.com/download) ```sh # Linux curl -fsSL https://ollama.com/install.sh | sh ``` 2. Run GLM-4 9B Chat (Q4_K_M, needs ~6.14 GB, from [ollama.com](https://ollama.com/library/glm4/tags)) ```sh ollama run glm4:9b-chat-q4_K_M ``` Other versions: Q8_0 (~11.78 GB) `ollama run glm4:9b-chat-q8_0` Model repository and documentation: [huggingface.co/zai-org](https://huggingface.co/zai-org/glm-4-9b-chat) [interactive version](https://whatmodel.sh/model/glm-4-9b-chat) · [JSON](https://whatmodel.sh/api?format=json&all=1)