[whatmodel.sh](../../README.md) / GLM-4.5 # GLM-4.5 Z.ai · released 2025-07 · 355B params (32B active) · mit **score 64.1** ±0.6 · #155 of 732 · from 52 benchmark results across 9 sources Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only. ## What it's good at ● marks GLM-4.5 among every model on that task or test, worst to best. 
task            vs the others         rank  counts  tests
agent tasks     ▁▁▃▆█▄●▅▄▄▂▁   #135 of 313     18%     11
coding          ▁▁▁▃▅▇█●▅▄▃▁   #153 of 469    9.5%      6
chat & writing  ▁▁▁▃▆▄▆█▆●▇▃   #107 of 402    5.6%      2
math            ▁▁▃▅█▆▄●▄▂▁▁   #116 of 431     26%      4
web search      ●▁▁▅▄█▃▄·▁·▁    #82 of 84*    1.9%      1
agent tasks · #135 of 313 · 18% of its score Beats [o3](o3.md), [Gemini 2.5 Pro](gemini-2-5-pro.md), [Gemini 3.1 Flash-Lite](gemini-3-1-flash-lite.md) here, though they rank higher overall. Trails [SmolLM3 3B](smollm3-3b.md), [Jamba 1.5 Large](jamba-1-5-large.md), [Devstral 2 (2512)](devstral-2.md) here, though they rank lower overall. 
test                                result  vs the others in its run    difficulty            counts  why
SWE-bench Verified (maker)           64.2%  ▁▁▁▁▁▁▂▄●▆▁▁ beats 65/138   hard · avg 24%          4.2%  sharp · at its level · small run · seve…
τ²-bench Retail                      69.3%  █···█·●·██·█ beats 2/5      hard · avg 35%          2.9%  at its level
BrowseComp (maker)                   26.4%  ▂·▃▁●▂▆▅█▂▂▄ beats 8/46     very hard · avg 13%     1.9%  at its level · small run · several runs
τ²-bench Telecom                     56.1%  █··██·●·█··█ beats 3/5      very hard · avg 9.6%    1.7%  sharp · at its level · noisy
Terminal-Bench (pre-2.0 release) …   37.5%  ▃·▃···▃▃▅·●█ beats 7/10     hard · avg 16%          1.7%  at its level · small run · several runs
τ-bench Retail (maker)               79.7%  ▁····▃·▂▇█▂● beats 25/28    medium · avg 46%        1.5%  small run
τ²-bench Airline                     66.0%  ▄·····█··▄·● beats 4/5      hard · avg 28%          1.4%  at its level
τ-bench Airline (maker)              60.4%  ▁▁▁·▅▄▁█▇▁▁● beats 27/28    hard · avg 30%          1.1%  at its level · small run
BFCL V4 (2025-11-03 archive, mode…   72.0%  ▃··▅▃▃·▅·▃·● beats 10/10    hard · avg 33%          0.8%  at its level
BFCL v3                              79.1%  ▁▁▁▁▁▃▅█▆▅▄● beats 171/171  medium · avg 60%        0.8%  blunt · at its level · small run · seve…
τ²-bench overall (weighted) (make…   67.5%  ●··········█ beats 0/1                                 —  not counted yet: no other model in the …
coding · #153 of 469 · 9.5% of its score Beats [Qwen3-Coder-Next (80B-A3B)](qwen3-coder-next.md), [Gemini 2.5 Pro](gemini-2-5-pro.md), [Gemini 3.1 Flash-Lite](gemini-3-1-flash-lite.md) here, though they rank higher overall. Trails [Ministral 3 8B Reasoning 2512](ministral-3-8b-reasoning.md), [Phi-4-reasoning 14B](phi-4-reasoning.md), [Llama 3.1 Nemotron Ultra 253B v1](llama-3-1-nemotron-ultra-253b.md) here, though they rank lower overall. 
test                                result  vs the others in its run   difficulty        counts  why
SWE-bench Verified (maker)           64.2%  ▁▁▁▁▁▁▂▄●▆▁▁ beats 65/138  hard · avg 24%      4.2%  sharp · at its level · small run · seve…
LiveCodeBench (version unspecifie…   61.1%  ●······▄█▄·▄ beats 0/5     medium · avg 41%    2.1%  at its level
LiveCodeBench v6 (Aug 2024–May 20…   64.5%  ▄▄·●·····█·▄ beats 2/5     medium · avg 45%    1.2%  at its level
LiveCodeBench v6 (window unspecif…   63.3%  ▂▃▄▄▅●█▆▄▇▄▆ beats 27/67   medium · avg 40%    1.1%  at its level · small run
SciCode (level unspecified) (make…   41.7%  ▁····▃▃▄▅█●▂ beats 30/40   hard · avg 18%      0.9%  at its level · small run
LiveCodeBench 2024-07 to 2025-01 …   72.9%  █··········● beats 1/1                            —  not counted yet: no other model in the …
chat & writing · #107 of 402 · 5.6% of its score Beats [LongCat 2.0 (1.6T-A48B)](longcat-2-0.md), [Inkling Small (266B-A12B)](inkling-small.md), [GPT-5.4 nano](gpt-5-4-nano.md) here, though they rank higher overall. Trails [Mistral Large 3 (675B-A41B)](mistral-large-3.md), [Gemma 4 12B](gemma-4-12b.md), [GPT-4.1](gpt-4-1.md) here, though they rank lower overall. 
test                        result  vs the others in its run    difficulty      counts  why
LMArena (text)              1410.9  ▁▁▁▂▃▃▃▆▆●█▃ beats 256/385  avg 1260          3.7%  blunt
IFEval prompt-level strict   85.4%  ●██··█·█···█ beats 0/5      easy · avg 73%    1.9%  several runs
math · #116 of 431 · 26% of its score Beats [Claude Sonnet 4.6](claude-sonnet-4-6.md), [MiniMax-M3](minimax-m3.md), [MiMo-V2.5 (310B-A15B)](mimo-v2-5.md) here, though they rank higher overall. Trails [Olmo 3.1 32B Think](olmo-3-1-32b-think.md), [gpt-oss-120b](gpt-oss-120b.md), [Qwen3-VL 32B Thinking](qwen3-vl-32b-thinking.md) here, though they rank lower overall. 
test                             result  vs the others in its run  difficulty           counts  why
AIME 2025                         83.3%  █·█···█●█··█ beats 3/5    medium · avg 50%        10%  sharp · several runs
AIME 2024                         85.8%  █···██●·█··█ beats 3/5    medium · avg 61%        10%  sharp · several runs
HMMT 2025 (edition unspecified)   76.3%  ▄··●··▄·▄··█ beats 1/5    hard · avg 31%         3.5%  sharp · at its level
MATH-500                          97.0%  ▄······▄▄●·█ beats 3/5    very easy · avg 92%    2.5%  maxed out · small run · several runs
web search · #82 of 84 · 1.9% of its score Trails [GPT-4.1 nano](gpt-4-1-nano-2025-04-14.md), [GPT-4o mini (2024-07-18)](gpt-4o-mini-2024-07-18.md), [Mistral Small 3.1 24B (2503)](mistral-small-3-1.md) here, though they rank lower overall. 
test                result  vs the others in its run  difficulty           counts  why
BrowseComp (maker)   26.4%  ▂·▃▁●▂▆▅█▂▂▄ beats 8/46   very hard · avg 13%    1.9%  at its level · small run · several runs
other tests (17) 
test                                result  vs the others in its run    difficulty            counts  why
GPQA Diamond                         77.0%  █··██·●█···█ beats 3/5      hard · avg 41%           14%  sharp · at its level · several runs
Humanity's Last Exam                 14.8%  ▄····▄·●·▄·▄ beats 3/5      very hard · avg 4.1%    6.5%  several runs
GPQA (subset unspecified) (maker)    79.1%  ▂▁▂▂▄▃▆▃▅█●▃ beats 95/106   hard · avg 47%          4.5%  sharp · at its level · small run · seve…
MMLU-Pro                             81.5%  ●·▄···█·▄··▄ beats 0/5      medium · avg 55%        4.3%  several runs
MMLU-Redux                           89.9%  █●██··█····█ beats 1/5      medium · avg 73%        2.5%  typical
EQ-Bench 3 (emotional intelligenc…  1229.2  ▁·▂▁▄▅▅●█▄▁▁ beats 36/62    avg 1034                2.3%  typical
ZebraLogic                           90.9%  ●█··█·██···█ beats 0/5      easy · avg 75%          2.1%  maxed out
MMLU (maker)                         90.0%  ▁▁▁▁▁▂▂▅██▆● beats 210/214  medium · avg 69%        1.7%  small run
OJBench                              19.0%  ●······▄▄·▄█ beats 0/5      very hard · avg 12%     1.5%  at its level
VitaBench                            26.8%  █····██·●█·█ beats 3/5      very hard · avg 8.2%    1.4%  typical
RPC-Bench overall, text input (Z.…   25.7%  ▄··▄●·▄▄█·▄▄ beats 2/8      very hard · avg 12%     1.3%  typical
SimpleQA (maker)                     26.4%  ▄▄▄▆█▆██●·▂█ beats 25/32    very hard · avg 7.5%    1.1%  small run
BeyondAIME                           66.0%  █··●····▄··█ beats 2/5      medium · avg 42%        1.0%  at its level
MultiChallenge (maker)               52.8%  ▁·▁·▁▃▆▅●▂█▅ beats 16/30    hard · avg 27%          0.8%  at its level · small run
Vectara hallucination leaderboard…   96.3%  ▁▁▂▃▄▄█●▃▂▂▁ beats 93/140   very easy · avg 94%     0.1%  blunt · maxed out · noisy
miniF2F (pass@1)                     10.9%  ●█······▄··▄ beats 0/5      very hard · avg 14%     0.1%  blunt · at its level · noisy
SysBench (ISR) (maker)               81.0%  █··········● beats 1/1                                 —  not counted yet: no other model in the …
## 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
Meituan LongCat-Flash-Thinking model ca…       18   41% ▇▇▇▇▇
GLM-4.5 Team (submitted / maker-reporte…       16   20% ▇▇
Prime Intellect INTELLECT-3 model card:…        6   19% ▇▇
zai-org/GLM-4.6 model card (submitted /…        7   10% ▇
LMArena leaderboard dataset                     1    4% ▇
EQ-Bench 3 canonical leaderboard results        1    2% ▇
RPC-Bench leaderboard (submitted / make…        1    1% ▇
Berkeley Function Calling Leaderboard r…        1    1% ▇
Vectara hallucination leaderboard               1    0% ▇
prior: pull toward the middle                        0% ▇
weighted average                                   = 64.1
Licences: Meituan LongCat-Flash-Thinking model card ([MIT](https://huggingface.co/meituan-longcat/LongCat-Flash-Thinking/blob/7c9aad1e2daceaeab3a5a03deedb8e11e7a7819f/LICENSE)) · GLM-4.5 Team ([CC BY 4.0](../../licenses/report-2508.06471v1.txt)) · Prime Intellect INTELLECT-3 model card: Evaluation table ([MIT](https://huggingface.co/PrimeIntellect/INTELLECT-3/blob/ff39d4a4688989f3f28868923d030c28e1b7d81c/README.md)) · zai-org/GLM-4.6 model card ([MIT](../../licenses/zai-org--GLM-4.6.txt)) · LMArena leaderboard dataset ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · EQ-Bench 3 canonical leaderboard results ([MIT](../../licenses/eqbench3-MIT.txt)) · RPC-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)) · Vectara hallucination leaderboard ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0))
Not counted (3) 
source                  test                            score  why
GLM-4.5 Team            LiveCodeBench 2024-07 to 2025…  72.9%  too few models have it yet
GLM-4.5 Team            SysBench (ISR)                  81.0%  too few models have it yet
zai-org/GLM-4.6 model…  τ²-bench overall (weighted)     67.5%  too few models have it yet
## How to run it ### Through an API api (Z.AI) · $1.00/M blended · $0.6 in / $2.2 out per 1M tokens · [docs.z.ai](https://docs.z.ai/guides/overview/pricing) [price source](https://github.com/anomalyco/models.dev/blob/a668908cfe7073faa0363699486fc41a3ff52412/providers/zai/models/glm-4.5.toml): Standard base token rate from models.dev. Provider-specific tiers, long-context surcharges, caching and promotions may differ. · [context source](https://github.com/anomalyco/models.dev/blob/a668908cfe7073faa0363699486fc41a3ff52412/models/zhipuai/glm-4.5.toml) ### 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-4.5 (ud-q4_k_xl, needs ~213.5 GB, from [huggingface.co/unsloth](https://huggingface.co/unsloth/GLM-4.5-GGUF/tree/main)) ```sh llama cli -hf unsloth/GLM-4.5-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-4.5-GGUF:UD-Q4_K_XL ``` Other versions: ud-q2_k_xl (~107 GB) `llama cli -hf unsloth/GLM-4.5-GGUF:UD-Q2_K_XL` · ud-iq1_s (~53.75 GB) `llama cli -hf unsloth/GLM-4.5-GGUF:UD-IQ1_S` ### 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-4.5 (ud-q4_k_xl, needs ~213.5 GB, from [huggingface.co/unsloth](https://huggingface.co/unsloth/GLM-4.5-GGUF/tree/main)) ```sh lms get https://huggingface.co/unsloth/GLM-4.5-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 (~107 GB) `lms get https://huggingface.co/unsloth/GLM-4.5-GGUF@UD-Q2_K_XL -y` · ud-iq1_s (~53.75 GB) `lms get https://huggingface.co/unsloth/GLM-4.5-GGUF@UD-IQ1_S -y` · 4bit (~198.6 GB, Mac only) `lms get https://huggingface.co/mlx-community/GLM-4.5-4bit -y` ### MLX (Mac) 1. Install MLX (Mac). Apple silicon Macs only, text only. ```sh # with uv uv tool install mlx-lm # or pip install mlx-lm ``` 2. Run GLM-4.5 (4bit, needs ~198.6 GB, Mac only, from [huggingface.co/mlx-community](https://huggingface.co/mlx-community/GLM-4.5-4bit/tree/61cf8ee56baf09205b3885f56fd1653df4a60a9f)) ```sh mlx_lm.chat --model mlx-community/GLM-4.5-4bit ``` 3. Or start an OpenAI-compatible server at http://localhost:8080/v1 ```sh mlx_lm.server --model mlx-community/GLM-4.5-4bit ``` Model repository and documentation: [huggingface.co/zai-org](https://huggingface.co/zai-org/GLM-4.5) [interactive version](https://whatmodel.sh/model/glm-4-5) · [JSON](https://whatmodel.sh/api?format=json&all=1)