[whatmodel.sh](../../README.md) / GLM-4.6 # GLM-4.6 Z.ai · released 2025-09 · 357B params (32B active) · mit **score 65.3** ±0.8 · #139 of 732 · from 31 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-4.6 among every model on that task or test, worst to best. 
task            vs the others         rank  counts  tests
agent tasks     ▁▁▃▆█▄●▅▄▄▂▁   #122 of 313     38%     13
coding          ▁▁▁▃▅▇█▇●▄▃▁   #126 of 469     31%      6
chat & writing  ▁▁▁▃▆▄▆█▆●▇▃   #88 of 402*    7.0%      1
math            ▁▁▃▅█▆▄●▄▂▁▁   #100 of 431     15%      5
web search      ▁●▁▅▄█▃▄·▁·▁     #78 of 84    9.2%      4
agent tasks · #122 of 313 · 38% of its score Beats [o3-pro](o3-pro.md), [o3](o3.md), [GPT-5 mini](gpt-5-mini.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)           68.0%  ▁▁▁▁▁▁▂▄●▆▁▁ beats 72/138  hard · avg 24%          7.9%  sharp · at its level · small run · seve…
Terminal-Bench 2.0 with Terminus …   24.5%  ▃··▃·▃▃●█▅▃▅ beats 6/14    very hard · avg 6.1%    5.2%  sharp · at its level
Terminal-Bench Hard (maker)          23.6%  ▂▅▆▂▂▆▂▂●▅▅▂ beats 17/27   very hard · avg 4.7%    3.8%  sharp · small run
BrowseComp (maker)                   45.1%  ▂·▃▁▂▂●▅█▂▂▄ beats 18/46   hard · avg 20%          3.5%  at its level · small run · several runs
BFCL V4 (December 2025 archive)      72.4%  ▁▁▁▂██▅▅▆▂▂● beats 75/77   hard · avg 31%          3.3%  at its level
Terminal-Bench 2.0 (maker)           24.5%  ▂▂●▅████▆▂▃▃ beats 4/36    very hard · avg 8.8%    2.7%  at its level · small run
BrowseComp-ZH (maker)                49.5%  ▁▁▁▁▁▃·▁▄●▄█ beats 13/23   hard · avg 20%          2.5%  at its level · small run
SWE-bench Multilingual (maker)       53.8%  ▁··▁▃▁●██▂▂▁ beats 14/65   hard · avg 25%          2.4%  at its level · small run
τ²-bench aggregate (maker)           75.2%  ▁▁▁·▁▁▃▃●▄█▁ beats 18/39   medium · avg 46%        2.3%  small run
Humanity's Last Exam with tools (…   30.4%  ▁·▁▂▂▁●▄▇█▄▅ beats 14/62   very hard · avg 14%     1.7%  at its level · small run
Terminal-Bench (pre-2.0 release) …   40.5%  ▃·▃···▃▃▅·▅● beats 9/10    hard · avg 16%          1.6%  at its level · small run
BrowseComp with context managemen…   57.5%  ●·▄▂·█▂·▆▄·▂ beats 0/15    medium · avg 38%        1.5%  at its level · small run
τ²-bench overall (weighted) (make…   75.9%  █··········● beats 1/1                                —  not counted yet: no other model in the …
coding · #126 of 469 · 31% of its score Beats [Qwen3.5 Plus](qwen3-5-plus.md), [Qwen3-Coder-Next (80B-A3B)](qwen3-coder-next.md), [Gemini 2.5 Pro](gemini-2-5-pro.md) here, though they rank higher overall. Trails [Llama 3.1 Nemotron Ultra 253B v1](llama-3-1-nemotron-ultra-253b.md), [Solar Pro 4](solar-pro-4.md), [Nemotron 3 Super 120B-A12B](nemotron-3-super-120b-a12b.md) here, though they rank lower overall. 
test                                result  vs the others in its run   difficulty        counts  why
Arena WebDev (Code Arena)           1341.2  ▁▁▃▂●█▃▆▅▅▁▂ beats 26/129  avg 981              12%  sharp
SWE-bench Verified (maker)           68.0%  ▁▁▁▁▁▁▂▄●▆▁▁ beats 72/138  hard · avg 24%      7.9%  sharp · at its level · small run · seve…
LiveCodeBench v6 (window unspecif…   82.8%  ▂▃▄▄▅██▆▄●▄▆ beats 49/67   medium · avg 48%    5.1%  small run · several runs
WebApp1K-Duo-React pass@1 (onekq-…   57.8%  ▁··▁·▁▁▁●█▇█ beats 10/45   hard · avg 29%      3.7%  at its level
SWE-bench Multilingual (maker)       53.8%  ▁··▁▃▁●██▂▂▁ beats 14/65   hard · avg 25%      2.4%  at its level · small run
LiveCodeBench v6 (window unspecif…   84.5%  no other model yet                                —  not counted yet: no other model in the …
chat & writing · #88 of 402 · 7.0% of its score Beats [LongCat 2.0 (1.6T-A48B)](longcat-2-0.md), [Inkling Small (266B-A12B)](inkling-small.md), [Qwen3.5 122B-A10B](qwen3-5-122b-a10b.md) here, though they rank higher overall. Trails [Gemma 4 12B](gemma-4-12b.md), [Gemma 4 26B-A4B](gemma-4-26b-a4b.md), [Mistral Medium 3.5 (128B)](mistral-medium-3-5.md) here, though they rank lower overall. 
test            result  vs the others in its run    difficulty  counts  why
LMArena (text)  1424.3  ▁▁▁▂▃▃▃▆▆●█▃ beats 277/385  avg 1260      7.0%  blunt
math · #100 of 431 · 15% of its score Beats [Claude Sonnet 4.6](claude-sonnet-4-6.md), [MiniMax-M3](minimax-m3.md), [Nemotron 3 Ultra 550B-A55B](nemotron-3-ultra-550b-a55b.md) here, though they rank higher overall. Trails [Gemini 2.5 Flash](gemini-2-5-flash.md), [GLM-4.5-Air](glm-4-5-air.md), [Granite 4.2 30B](granite-4-2-30b.md) here, though they rank lower overall. 
test                          result  vs the others in its run    difficulty        counts  why
AIME 2025 (maker)              93.9%  ▁·▁▁▁▂▅▆█▇●▁ beats 128/133  medium · avg 63%    8.1%  sharp · maxed out · small run · several…
IMO-AnswerBench (maker)        73.5%  ▁▁·▁▁▁▄●█▄▂▃ beats 13/44    medium · avg 36%    3.3%  small run
HMMT February 2025 (maker)     89.2%  ▁▁▁▃▁▅▅▃●█▁▄ beats 18/31    easy · avg 68%      2.1%  small run
HMMT November 2025 (maker)     87.7%  ▁▁····▁▅●█▇▁ beats 9/23     easy · avg 70%      1.7%  small run
AIME 2025 with tools (maker)   98.6%  no other model yet                                 —  not counted yet: no other model in the …
web search · #78 of 84 · 9.2% of its score Beats [Gemini 3.1 Flash-Lite](gemini-3-1-flash-lite.md) here, though they rank higher overall. 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)                   45.1%  ▂·▃▁▂▂●▅█▂▂▄ beats 18/46  hard · avg 20%         3.5%  at its level · small run · several runs
BrowseComp-ZH (maker)                49.5%  ▁▁▁▁▁▃·▁▄●▄█ beats 13/23  hard · avg 20%         2.5%  at its level · small run
Humanity's Last Exam with tools (…   30.4%  ▁·▁▂▂▁●▄▇█▄▅ beats 14/62  very hard · avg 14%    1.7%  at its level · small run
BrowseComp with context managemen…   57.5%  ●·▄▂·█▂·▆▄·▂ beats 0/15   medium · avg 38%       1.5%  at its level · small run
other tests (7) 
test                                result  vs the others in its run    difficulty            counts  why
GPQA Diamond (maker)                 81.0%  ▁▁▁▁▂▄▅▅█●█▄ beats 208/322  hard · avg 43%          5.6%  sharp · at its level · small run
Humanity's Last Exam (maker)         17.2%  ▁▁▁▄▆▆●██▆▁▃ beats 42/111   very hard · avg 4.3%    5.4%  small run · several runs
GPQA (subset unspecified) (maker)    81.0%  ▂▁▂▂▄▃▆▃▅█●▃ beats 97/106   hard · avg 47%          4.2%  sharp · at its level · small run
MMLU-Pro (maker)                     83.2%  ▁▁·▁▁▁▃▂▄▅●▁ beats 154/188  medium · avg 55%        2.6%  small run
Vectara hallucination leaderboard…   95.7%  ▁▁▂▃▄▄●▆▃▂▂▁ beats 79/140   very easy · avg 94%     0.2%  blunt · maxed out · noisy
Vectara hallucination leaderboard…   90.5%  ▁▂▁▂▅●▂▃▄▃▁▁ beats 49/98    very easy · avg 90%     0.0%  blunt
GPQA (subset unspecified) with to…   82.9%  no other model yet                                     —  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
zai-org/GLM-4.7 model card (submitted /…       17   48% ▇▇▇▇▇▇
zai-org/GLM-4.6 model card (submitted /…        7   20% ▇▇
LMArena leaderboard dataset                     2   19% ▇▇
Terminal-Bench: Benchmarking Agents on …        1    5% ▇
WebApp1K models leaderboard                     1    4% ▇
Berkeley Function Calling Leaderboard r…        1    3% ▇
Vectara hallucination leaderboard               2    0% ▇
prior: pull toward the middle                        0% ▇
weighted average                                   = 65.3
Licences: zai-org/GLM-4.7 model card ([MIT](../../licenses/zai-org--GLM-4.7.txt)) · 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/)) · Terminal-Bench: Benchmarking Agents on Hard ([CC BY 4.0](../../licenses/paper-2601.11868v1-CC-BY-4.0.txt)) · WebApp1K models leaderboard ([MIT](../../licenses/webapp1k-leaderboard-MIT.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 (5) 
source                  test                            score  why
zai-org/GLM-4.6 model…  AIME 2025 with tools            98.6%  too few models have it yet
zai-org/GLM-4.6 model…  GPQA (subset unspecified) wit…  82.9%  too few models have it yet
zai-org/GLM-4.6 model…  LiveCodeBench v6 (window unsp…  84.5%  too few models have it yet
zai-org/GLM-4.6 model…  τ²-bench overall (weighted)     75.9%  too few models have it yet
zai-org/GLM-4.6 model…  Humanity's Last Exam with too…  30.4%  only model in this source's run
## 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-4.6.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-4.6.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-4.6 (ud-q4_k_xl, needs ~214.7 GB, from [huggingface.co/unsloth](https://huggingface.co/unsloth/GLM-4.6-GGUF/tree/main)) ```sh llama cli -hf unsloth/GLM-4.6-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-4.6-GGUF:UD-Q4_K_XL ``` Other versions: ud-q2_k_xl (~107.6 GB) `llama cli -hf unsloth/GLM-4.6-GGUF:UD-Q2_K_XL` · ud-iq1_s (~54.05 GB) `llama cli -hf unsloth/GLM-4.6-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-4.6 (ud-q4_k_xl, needs ~214.7 GB, from [huggingface.co/unsloth](https://huggingface.co/unsloth/GLM-4.6-GGUF/tree/main)) ```sh lms get https://huggingface.co/unsloth/GLM-4.6-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 (~107.6 GB) `lms get https://huggingface.co/unsloth/GLM-4.6-GGUF@UD-Q2_K_XL -y` · ud-iq1_s (~54.05 GB) `lms get https://huggingface.co/unsloth/GLM-4.6-GGUF@UD-IQ1_S -y` · 4bit (~198.6 GB, Mac only) `lms get https://huggingface.co/mlx-community/GLM-4.6-4bit -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-4.6 (4bit, needs ~198.6 GB, Mac only, from [huggingface.co/mlx-community](https://huggingface.co/mlx-community/GLM-4.6-4bit/tree/dfcf4c549df3cd03bd61a7766899cffc79ffe0af)) ```sh mlx_lm.chat --model mlx-community/GLM-4.6-4bit ``` 3. Or start an OpenAI-compatible server at http://localhost:8080/v1 ```sh mlx_lm.server --model mlx-community/GLM-4.6-4bit ``` Model repository and documentation: [huggingface.co/zai-org](https://huggingface.co/zai-org/GLM-4.6) [interactive version](https://whatmodel.sh/model/glm-4-6) · [JSON](https://whatmodel.sh/api?format=json&all=1)