[whatmodel.sh](../../README.md) / GLM-5.3 # GLM-5.3 Z.ai · released 2026-08 · 753B params (40B active) · glm-5.3-license **score 74.5** ±0.7 · #38 of 732 · from 13 benchmark results across 4 sources Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only. ## What it's good at ● marks GLM-5.3 among every model on that task or test, worst to best. 
task            vs the others         rank  counts  tests
agent tasks     ▁▁▃▆█▄▅▅▄●▂▁   #27 of 313*     18%      2
coding          ▁▁▁▃▅▇█▇▅▄●▁   #25 of 469*     13%      2
chat & writing  ▁▁▁▃▆▄▆█▆▇▇●   #22 of 402*    5.1%      1
math            ▁▁▃▅█▆▄█●▂▁▁    #32 of 431     44%      3
agent tasks · #27 of 313 · 18% of its score Beats [Hy4 preview (770B-A49B)](hy4-preview.md), [GPT-5.6 Terra](gpt-5-6-terra.md), [Qwen3.8 Max (2.4T-A95B)](qwen3-8-2-4t-a95b.md) here, though they rank higher overall. Trails [Grok 4.7](grok-4-7.md) here, though they rank lower overall. 
test                                    result  vs the others in its run  difficulty            counts  why
Arena Agent (October 2026 aggrega…  0.0091 IPS  ▁▁▁▃▃▂●▁▂▂▁▁ beats 27/59  avg -0.379 IPS           13%  sharp
Terminal-Bench 4.0                       41.8%  █▅▅▅▅·▅●▃▅▅▅ beats 13/20  very hard · avg 0.5%    4.9%  sharp
coding · #25 of 469 · 13% of its score Beats [GPT-5.6 Sol](gpt-5-6-sol.md), [GPT-5.5](gpt-5-5.md), [GPT-5.6 Terra](gpt-5-6-terra.md) here, though they rank higher overall. Trails [Grok 4.7](grok-4-7.md), [Qwen3.8-Flash-Next](qwen3-8-flash-next.md), [Qwen3.8 Max (0902)](qwen3-8-max-0902.md) here, though they rank lower overall. 
test                                result  vs the others in its run    difficulty      counts  why
Arena WebDev (Code Arena)           1622.8  ▁▁▃▂▅█▃▆●▅▁▂ beats 105/129  avg 981           9.1%  sharp
Luau Leetcode fully solved (Torpe…   69.7%  ▁▁▁▁·▁▂▃▅●█▇ beats 23/49    hard · avg 17%    3.5%  sharp
chat & writing · #22 of 402 · 5.1% of its score Beats [GPT-6 Astra](gpt-6-astra.md), [Claude Sonnet 5.5](claude-sonnet-5-5.md), [GPT-6 Sol](gpt-6-sol.md) here, though they rank higher overall. Trails [Gemini 3.6 Flash](gemini-3-6-flash.md), [Muse Spark 1.2](muse-spark-1-2.md), [Muse Spark 1.1](muse-spark-1-1.md) here, though they rank lower overall. 
test            result  vs the others in its run    difficulty  counts  why
LMArena (text)  1480.6  ▁▁▁▂▃▃▃▆▆▇█● beats 361/385  avg 1260      5.1%  blunt
math · #32 of 431 · 44% of its score Beats [Gemini 3.8 Flash](gemini-3-8-flash.md), [DeepSeek V4.1 Flash](deepseek-v4-1-flash.md), [DeepSeek V4 Pro (0813)](deepseek-v4-pro.md) here, though they rank higher overall. Trails [Qwen3.7 Max](qwen3-7-max.md), [Muse Spark Open](muse-spark-open.md), [Qwen3.8 Max (0902)](qwen3-8-max-0902.md) here, though they rank lower overall. 
test                       result  vs the others in its run    difficulty            counts  why
FrontierMath tiers 1–3 v2   68.8%  ▂··▁▁▃▄▄█●▄▃ beats 58/86    very hard · avg 1.2%     22%  sharp · at its level
FrontierMath tier 4 v2      29.3%  ▁▂▁▂●▅▂▃▂▁▁▁ beats 35/67    very hard · avg 0.1%     22%  sharp · at its level
OTIS mock AIME 2024–2025    91.1%  ▃▃▄▃▃▃▇▇●▃▂▅ beats 148/197  very hard · avg 7.2%    0.7%  sharp · maxed out · noisy
other tests (5) 
test                                result  vs the others in its run    difficulty            counts  why
Mystery Game Puzzles (Epoch)         33.0%  █··▂▃▇▄●▂▄▂▁ beats 52/77    very hard · avg 9.4%    8.1%  sharp · noisy
Chess Puzzles (Epoch)                21.0%  █▁▁▁▁▂●▃▃▂▁▁ beats 87/137   very hard · avg 5.8%    4.8%  sharp · at its level · noisy
GPQA Diamond                         90.9%  ▁▁▁▁▂▄▆▄▅▇●▅ beats 182/211  hard · avg 39%          3.4%  sharp
SimpleQA Verified                    41.0%  ▁▁▃▂▃▃▆●█▄▂▅ beats 36/81    very hard · avg 8.0%    2.0%  at its level
RobloxQA v2.0 multiple choice (To…   93.9%  ▁▁▃▁█▄●▃▆▂▂▂ beats 25/49    easy · avg 77%          1.2%  maxed out
## 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
Epoch AI                                        7   63% ▇▇▇▇▇▇▇▇
LMArena leaderboard dataset                     3   27% ▇▇▇
Terminal-Bench 4.0 official leaderboard…        1    5% ▇
Roblox LLM Leaderboard                          2    5% ▇
prior: pull toward the middle                        0% ▇
weighted average                                   = 74.5
Licences: Epoch AI ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · LMArena leaderboard dataset ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · Terminal-Bench 4.0 official leaderboard submissions ([Apache-2.0](../../licenses/terminal-bench-leaderboard-submissions-Apache-2.0.txt)) · Roblox LLM Leaderboard ([MIT](../../licenses/roblox-llm-leaderboard-MIT.txt)) ## How to run it ### Through an API api (Z.AI) · $2.15/M blended · $1.4 in / $4.4 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-5.3.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-5.3.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-5.3 (ud-q4_k_xl, needs ~452.3 GB, from [huggingface.co/unsloth](https://huggingface.co/unsloth/GLM-5.3-GGUF/tree/main)) ```sh llama cli -hf unsloth/GLM-5.3-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-5.3-GGUF:UD-Q4_K_XL ``` Other versions: ud-q2_k_xl (~226.4 GB) `llama cli -hf unsloth/GLM-5.3-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-5.3 (ud-q4_k_xl, needs ~452.3 GB, from [huggingface.co/unsloth](https://huggingface.co/unsloth/GLM-5.3-GGUF/tree/main)) ```sh lms get https://huggingface.co/unsloth/GLM-5.3-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 (~226.4 GB) `lms get https://huggingface.co/unsloth/GLM-5.3-GGUF@UD-Q2_K_XL -y` · 4bit (~458.9 GB, Mac only) `lms get https://huggingface.co/orcarouter/GLM-5.3-MLX -y` · 8bit (~790.1 GB, Mac only) `lms get https://huggingface.co/pipenetwork/GLM-5.3-MLX-8bit -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-5.3 (4bit, needs ~458.9 GB, Mac only, from [huggingface.co/orcarouter](https://huggingface.co/orcarouter/GLM-5.3-MLX/tree/4dd4802ad4d8f370a8af22d4eae0173f1cee2fdd)) ```sh mlx_lm.chat --model orcarouter/GLM-5.3-MLX ``` 3. Or start an OpenAI-compatible server at http://localhost:8080/v1 ```sh mlx_lm.server --model orcarouter/GLM-5.3-MLX ``` Other versions: 8bit (~790.1 GB, Mac only) `mlx_lm.chat --model pipenetwork/GLM-5.3-MLX-8bit` Model repository and documentation: [huggingface.co/zai-org](https://huggingface.co/zai-org/GLM-5.3) [interactive version](https://whatmodel.sh/model/glm-5-3) · [JSON](https://whatmodel.sh/api?format=json&all=1)