[whatmodel.sh](../../README.md) / GLM-5.2
# GLM-5.2
Z.ai · released 2026-06 · 753B params (40B active) · mit
**score 72.2** ±0.5 · #59 of 732 · from 52 benchmark results across 12 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks GLM-5.2 among every model on that task or test, worst to best.
task vs the others rank counts tests
agent tasks ▁▁▃▆█▄▅▅▄●▂▁ #42 of 313 24% 14
coding ▁▁▁▃▅▇█▇▅▄●▁ #31 of 469 14% 7
chat & writing ▁▁▁▃▆▄▆█▆▇●▃ #31 of 402* 2.5% 1
math ▁▁▃▅█▆▄█●▂▁▁ #46 of 431 49% 18
web search ▁▁▁▅▄●▃▄·▁·▁ #42 of 84 2.1% 2
agent tasks · #42 of 313 · 24% of its score
Beats [GPT-5.6 Luna](gpt-5-6-luna.md), [Gemini 3.7 Flash](gemini-3-7-flash.md), [Muse Spark Open](muse-spark-open.md) here, though they rank higher overall.
test result vs the others in its run difficulty counts why
Arena Agent (October 2026 aggrega… 0.0126 IPS ▁▁▁▃▃▂●▁▂▂▁▁ beats 32/59 avg -0.379 IPS 6.3% sharp
SWE-bench Verified 78.7% ▁··▁·▁▂▁▄█●▁ beats 26/29 hard · avg 19% 4.4% sharp
Terminal-Bench 2.1 (maker) 81.0% ▁·▁·▁▁▄▃▆█●█ beats 45/63 hard · avg 25% 2.2% sharp · small run · several runs
GameLogicBench solve rate (72 God… 37.5% █▄·█·▄·●█▄·▄ beats 7/11 very hard · avg 5.1% 1.6% at its level
τ³-bench banking_knowledge (v1.0.… 37.1% ▃▃▂▃·▆▃▂●▅▅▂ beats 15/25 very hard · avg 4.3% 1.6% at its level
E2E-SWE (build a working codebase… 22.2% ▃··●··▃█▃▅▃▅ beats 1/12 very hard · avg 3.7% 1.4% sharp · at its level
MCP-Atlas (public set) (maker) 76.8% █··█·██·██●█ beats 6/7 hard · avg 30% 1.4% at its level
Humanity's Last Exam with tools (… 54.7% ▁·▁▂▂▁▄▄▇●▄▅ beats 44/62 hard · avg 16% 1.3% at its level · small run · several runs
Legal Research Bench (agentic, al… 31.2% ▄█··▄▄▄█●·▄█ beats 9/12 very hard · avg 6.3% 0.8% at its level
EEGAgentBench overall (tool-based… 45.5% ▂·▁▁··▇●█▂·▁ beats 10/25 hard · avg 28% 0.7% blunt · at its level
Toolathlon (Tool Decathlon) (make… 48.2% ▂▂▄▂▄▄▆▂●▆██ beats 13/27 very hard · avg 13% 0.6% at its level · small run
Agents' Last Exam (metric/harness… 20.4% ▂·●·█▄▂·▂▂▄▄ beats 1/14 very hard · avg 7.0% 0.5% at its level · small run
DeepSWE v1.1 (resolved) (maker) 46.2% ▂····▂●▁▆███ beats 4/31 hard · avg 20% 0.5% at its level · small run
SWE-bench Pro (maker) 62.1% ▄▂▂▂▄▂▆█·●·▂ beats 15/16 hard · avg 32% 0.3% blunt · at its level · small run
coding · #31 of 469 · 14% of its score
Beats [GPT-5.5](gpt-5-5.md), [GPT-5.6 Terra](gpt-5-6-terra.md), [Claude Opus 4.8](claude-opus-4-8.md) here, though they rank higher overall.
test result vs the others in its run difficulty counts why
SWE-bench Verified 78.7% ▁··▁·▁▂▁▄█●▁ beats 26/29 hard · avg 19% 4.4% sharp
Arena WebDev (Code Arena) 1605.6 ▁▁▃▂▅█▃▆●▅▁▂ beats 100/129 avg 981 4.4% sharp
Luau Leetcode fully solved (Torpe… 74.0% ▁▁▁▁·▁▂▃▅●█▇ beats 25/49 hard · avg 17% 1.9% sharp · at its level
GameLogicBench solve rate (72 God… 37.5% █▄·█·▄·●█▄·▄ beats 7/11 very hard · avg 5.1% 1.6% at its level
E2E-SWE (build a working codebase… 22.2% ▃··●··▃█▃▅▃▅ beats 1/12 very hard · avg 3.7% 1.4% sharp · at its level
DeepSWE v1.1 (resolved) (maker) 46.2% ▂····▂●▁▆███ beats 4/31 hard · avg 20% 0.5% at its level · small run
SWE-bench Pro (maker) 62.1% ▄▂▂▂▄▂▆█·●·▂ beats 15/16 hard · avg 32% 0.3% blunt · at its level · small run
chat & writing · #31 of 402 · 2.5% of its score
Beats [GPT-6 Sol](gpt-6-sol.md), [GPT-5.6 Terra](gpt-5-6-terra.md), [MiMo-V2.6-Flash (309B-A15B)](mimo-v2-6-flash.md) here, though they rank higher overall.
test result vs the others in its run difficulty counts why
LMArena (text) 1475.3 ▁▁▁▂▃▃▃▆▆▇●▃ beats 353/385 avg 1260 2.5% blunt
math · #46 of 431 · 49% of its score
Beats [Grok 4.7](grok-4-7.md), [GLM-5.3-Flash](glm-5-3-flash.md), [Gemini 3.6 Flash](gemini-3-6-flash.md) here, though they rank higher overall. Trails [Kimi K2.6](kimi-k2-6.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
FrontierMath tiers 1–3 v2 59.2% ▂··▁▁▃▄▄●▆▄▃ beats 46/86 very hard · avg 1.2% 12% sharp · at its level
FrontierMath tier 4 v2 29.3% ▁▂▁▂●▅▂▃▂▁▁▁ beats 33/67 very hard · avg 0.1% 8.2% sharp
MathArena ArXivMath June 2026 (48… 46.5% ▂···▄·●█▆▆▄▂ beats 3/19 very hard · avg 5.1% 5.7% sharp · at its level
MathArena ArXivMath April 2026 (4… 47.5% ▁·····▅●█▄·▁ beats 6/20 very hard · avg 7.6% 5.2% sharp · at its level
MathArena ArXivMath February 2026… 46.1% ▁▁▁▁█▇●▃▁▃·▃ beats 17/24 very hard · avg 9.4% 3.0% at its level
MathArena ArXivMath March 2026 (3… 61.7% █▃▅·█●▃·▅·▃▃ beats 9/14 hard · avg 16% 2.3% at its level
MathArena ArXivMath May 2026 (40 … 56.7% ▁··▁▄●▄▁·▃·▁ beats 9/17 very hard · avg 11% 1.8% at its level
MathArena BrokenArXiv April 2026 … 18.4% ▂▂●█▃▃▂▂▂··▂ beats 4/18 very hard · avg 0.8% 1.6% sharp · at its level · noisy
HMMT February 2026 (maker) 92.5% ▃▃▂▅·████▅●▅ beats 32/35 hard · avg 33% 1.2% sharp · small run
MathArena BrokenArXiv June 2026 (… 14.2% ▄··●▂▂▂▆▄··▂ beats 4/14 very hard · avg 1.3% 1.2% sharp · at its level · noisy
MathArena ArXivLean June 2026 (46… 8.7% ▄····●▄███·▄ beats 1/9 very hard · avg 0.9% 1.1% sharp
MathArena BrokenArXiv May 2026 (5… 10.5% █●·▄▄▄····▄▄ beats 2/8 very hard · avg 1.9% 1.1% sharp
IMO-AnswerBench (maker) 91.0% ▁▁·▁▁▁▄▆█▄▂● beats 41/44 medium · avg 40% 1.0% small run
MathArena BrokenArXiv March 2026 … 12.1% █·●█▂·▂▂···▂ beats 5/14 very hard · avg 1.0% 0.9% sharp · noisy
AIME 2026 (maker) 99.2% ▁▁▁▂▂▂▆█▆▂·● beats 52/52 easy · avg 68% 0.9% sharp · maxed out · small run
MathArena BrokenArXiv February 20… 10.5% ▃▂▂●█▃▂·▃··▂ beats 5/16 very hard · avg 0.9% 0.9% sharp · noisy
HMMT November 2025 (maker) 94.4% ▁▁····▁▅▃█●▁ beats 21/23 easy · avg 73% 0.6% maxed out · small run
OTIS mock AIME 2024–2025 86.4% ▃▃▄▃▃▃▇▇●▃▂▅ beats 128/197 very hard · avg 7.2% 0.5% sharp · maxed out · noisy
web search · #42 of 84 · 2.1% of its score
Beats [Gemini 3.1 Pro (Preview)](gemini-3-1-pro.md), [Gemini 3.5 Flash](gemini-3-5-flash.md), [Qwen3.7 Max](qwen3-7-max.md) here, though they rank higher overall. Trails [GPT-4o mini (2024-07-18)](gpt-4o-mini-2024-07-18.md), [Mistral Small 3.1 24B (2503)](mistral-small-3-1.md), [Llama 4 Scout (109B-A17B)](llama-4-scout.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
Humanity's Last Exam with tools (… 54.7% ▁·▁▂▂▁▄▄▇●▄▅ beats 44/62 hard · avg 16% 1.3% at its level · small run · several runs
Legal Research Bench (agentic, al… 31.2% ▄█··▄▄▄█●·▄█ beats 9/12 very hard · avg 6.3% 0.8% at its level
other tests (14)
test result vs the others in its run difficulty counts why
GPQA Diamond 91.9% ▁▁▁▁▂▄▆▄▅▇●▅ beats 188/211 hard · avg 39% 4.0% sharp · several runs
EBR-bench (Epoch) 9.5% ▄▂·●█▂█▆▄▆·▆ beats 5/25 very hard · avg 0.3% 3.5% sharp
Mystery Game Puzzles (Epoch) 15.0% █··▂●▇▄█▂▄▂▁ beats 22/77 very hard · avg 9.4% 3.0% sharp · noisy
Chess Puzzles (Epoch) 21.0% █▁▁▁▁▂●▃▃▂▁▁ beats 86/137 very hard · avg 5.8% 2.0% sharp · noisy
Humanity's Last Exam (maker) 40.5% ▁▁▁▄▆▆▇██●▁▃ beats 96/111 very hard · avg 6.0% 1.0% at its level · small run
Legal Research Bench (single-shot… 3.4% ▃▃█●·▅···█▃▃ beats 5/12 very hard · avg 0.8% 1.0% too hard for it
SimpleQA Verified 34.2% ▁▁▃▂▃▃●▆█▄▂▅ beats 28/81 very hard · avg 8.0% 1.0% at its level
NL2Repo (maker) 48.9% ▂▂▂·▃▃▅█●▂▅▃ beats 16/22 very hard · avg 12% 0.8% at its level · small run
RobloxQA v2.0 multiple choice (To… 89.0% ▁▁▃▁●▄▃▃▆▂▂▂ beats 9/49 easy · avg 77% 0.7% typical
AutomationBench v1.0.6 (maker) 26.2% ▃···▃·●▃··█▅ beats 2/8 very hard · avg 7.4% 0.6% at its level · small run
CritPt (maker) 20.9% ▄···▄▄▄▄█▄▄● beats 9/9 very hard · avg 1.7% 0.4% noisy · small run
WMT25 Terminology Translation (te… 78.2% ▂·▂··●·▄▂·█▆ beats 2/12 medium · avg 63% 0.3% noisy
WMT24++ 78.8% █·▃·▅●▃···▃▃ beats 7/10 easy · avg 70% 0.2% blunt
WMT26 General MT (GEMBA-ESA, LLM … 76.5% ▄·▄▄▄·▄●█▄█▄ beats 5/12 medium · avg 65% 0.2% blunt
source results share of the score
Epoch AI 9 36% ▇▇▇▇
MathArena ArXivMath / BrokenArXiv / ArX… 11 25% ▇▇▇
LMArena leaderboard dataset 3 13% ▇▇
zai-org/GLM-5.2 model card (submitted /… 13 12% ▇
zai-org/GLM-5.3-Flash model card (submi… 5 3% ▇
Roblox LLM Leaderboard 2 3% ▇
Legal Research Bench 2 2% ▇
GameLogicBench: Evaluating Coding Agent… 1 2% ▇
τ²/τ³-bench leaderboard: Sierra's own e… 1 2% ▇
E2E-SWE: Benchmarking LLMs on Building … 1 1% ▇
EEGAgentBench: Benchmarking LLM Agents … 1 1% ▇
Cohere 3 1% ▇
prior: pull toward the middle 0% ▇
weighted average = 72.2
Licences: Epoch AI ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · MathArena ArXivMath / BrokenArXiv / ArXivLean model-output datasets ([CC BY-SA 4.0](../../licenses/matharena-sharealike-NOTICE.txt)) · LMArena leaderboard dataset ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · zai-org/GLM-5.2 model card ([mit](../../licenses/zai-org--GLM-5.2.txt)) · zai-org/GLM-5.3-Flash model card ([MIT](../../licenses/zai-org--GLM-5.3-Flash.txt)) · Roblox LLM Leaderboard ([MIT](../../licenses/roblox-llm-leaderboard-MIT.txt)) · Legal Research Bench ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)) · GameLogicBench: Evaluating Coding Agents on Runtime Game Logic with Tick-Level State Assertions ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · τ²/τ³-bench leaderboard: Sierra's own evaluation runs ([MIT](https://opensource.org/license/mit)) · E2E-SWE: Benchmarking LLMs on Building Working Codebases from Scratch ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · EEGAgentBench: Benchmarking LLM Agents on Short- and Long-Horizon EEG Analysis ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · Cohere ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/))
## 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-5.2.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-5.2.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-5.2 (ud-q4_k_xl, needs ~452.3 GB, from [huggingface.co/unsloth](https://huggingface.co/unsloth/GLM-5.2-GGUF/tree/main))
```sh
llama cli -hf unsloth/GLM-5.2-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-5.2-GGUF:UD-Q4_K_XL
```
Other versions: ud-q2_k_xl (~226.4 GB) `llama cli -hf unsloth/GLM-5.2-GGUF:UD-Q2_K_XL` · ud-iq1_s (~113.45 GB) `llama cli -hf unsloth/GLM-5.2-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-5.2 (ud-q4_k_xl, needs ~452.3 GB, from [huggingface.co/unsloth](https://huggingface.co/unsloth/GLM-5.2-GGUF/tree/main))
```sh
lms get https://huggingface.co/unsloth/GLM-5.2-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-5.2-GGUF@UD-Q2_K_XL -y` · ud-iq1_s (~113.45 GB) `lms get https://huggingface.co/unsloth/GLM-5.2-GGUF@UD-IQ1_S -y` · 4bit (~418.3 GB, Mac only) `lms get https://huggingface.co/mlx-community/GLM-5.2-4bit -y` · 8bit (~790 GB, Mac only) `lms get https://huggingface.co/pipenetwork/GLM-5.2-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-5.2 (4bit, needs ~418.3 GB, Mac only, from [huggingface.co/mlx-community](https://huggingface.co/mlx-community/GLM-5.2-4bit/tree/6832ff4c65e23ed5737d244de196d5a6024fe55b))
```sh
mlx_lm.chat --model mlx-community/GLM-5.2-4bit
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model mlx-community/GLM-5.2-4bit
```
Other versions: 8bit (~790 GB, Mac only) `mlx_lm.chat --model pipenetwork/GLM-5.2-MLX-8bit`
Model repository and documentation: [huggingface.co/zai-org](https://huggingface.co/zai-org/GLM-5.2)
[interactive version](https://whatmodel.sh/model/glm-5-2) · [JSON](https://whatmodel.sh/api?format=json&all=1)