[whatmodel.sh](../../README.md) / GLM-4.7 # GLM-4.7 Z.ai · released 2025-12 · 357B params (32B active) · mit **score 67.6** ±0.7 · #108 of 732 · from 48 benchmark results across 11 sources Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only. ## What it's good at ● marks GLM-4.7 among every model on that task or test, worst to best. 
task            vs the others         rank  counts  tests
agent tasks     ▁▁▃▆█▄▅●▄▄▂▁    #99 of 313     35%     11
coding          ▁▁▁▃▅▇█▇▅●▃▁    #82 of 469     18%      4
chat & writing  ▁▁▁▃▆▄▆█▆▇●▃   #66 of 402*    4.6%      1
math            ▁▁▃▅█▆▄█●▂▁▁    #67 of 431     15%      7
web search      ▁▁▁●▄█▃▄·▁·▁     #73 of 84     10%      4
agent tasks · #99 of 313 · 35% of its score Beats [Grok 4.20 (0309 reasoning)](grok-4-20.md), [GPT-5.1](gpt-5-1.md), [o3-pro](o3-pro.md) here, though they rank higher overall. Trails [Mistral Medium 3.5 (128B)](mistral-medium-3-5.md), [Hy3 preview (295B-A21B)](hy3-preview.md), [MiniMax-M2.7](minimax-m2-7.md) here, though they rank lower overall. 
test                                result  vs the others in its run   difficulty            counts  why
MCP-Atlas (public set) (maker)       52.0%  ●······▄▄▄▄█ beats 0/6     hard · avg 26%          5.8%  at its level · several runs
SWE-bench Verified (maker)           73.8%  ▁▁▁▁▁▁▂▄●▆▁▁ beats 99/138  hard · avg 25%          5.1%  sharp · at its level · small run · seve…
Terminal-Bench 2.0 (maker)           41.0%  ▂▂▅▅█●██▆▂▃▃ beats 14/36   very hard · avg 8.8%    3.6%  at its level · small run · several runs
BrowseComp-ZH (maker)                66.6%  ▁▁▁▁▁▃·▁▄▅▄● beats 18/23   hard · avg 25%          3.4%  at its level · small run · several runs
SWE-bench Multilingual (maker)       66.7%  ▁··▁▃▁▅●█▂▂▁ beats 29/65   hard · avg 28%          3.2%  at its level · small run · several runs
τ²-bench aggregate (maker)           87.4%  ▁▁▁·▁▁▃▃▃▄●▁ beats 36/39   medium · avg 54%        3.1%  small run · several runs
Terminal-Bench Hard (maker)          33.3%  ▂▅▆▂▂▆▂▂█●▅▂ beats 22/27   very hard · avg 4.7%    2.6%  sharp · at its level · small run
BrowseComp (maker)                   52.0%  ▂·▃▁▂▂▆●█▂▂▄ beats 21/46   hard · avg 20%          2.5%  at its level · small run · several runs
Humanity's Last Exam with tools (…   42.8%  ▁·▁▂▂▁▄▄●█▄▅ beats 24/62   hard · avg 16%          2.4%  at its level · small run · several runs
BrowseComp with context managemen…   67.5%  ▄·●▂·█▂·▆▄·▂ beats 3/15    medium · avg 38%        1.9%  at its level · small run · several runs
Toolathlon (Tool Decathlon) (make…   23.8%  ▂●▄▂▄▄▆▂█▆██ beats 1/27    very hard · avg 11%     1.0%  at its level · small run
coding · #82 of 469 · 18% of its score Beats [GPT-5.2](gpt-5-2.md), [GPT-5](gpt-5.md), [Grok 4.20 (0309 reasoning)](grok-4-20.md) here, though they rank higher overall. Trails [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
Arena WebDev (Code Arena)           1435.2  ▁▁▃▂▅●▃▆▅▅▁▂ beats 55/129  avg 981             8.1%  sharp
SWE-bench Verified (maker)           73.8%  ▁▁▁▁▁▁▂▄●▆▁▁ beats 99/138  hard · avg 25%      5.1%  sharp · at its level · small run · seve…
SWE-bench Multilingual (maker)       66.7%  ▁··▁▃▁▅●█▂▂▁ beats 29/65   hard · avg 28%      3.2%  at its level · small run · several runs
LiveCodeBench v6 (window unspecif…   84.9%  ▂▃▄▄▅██▆▄●▄▆ beats 52/67   medium · avg 48%    1.9%  small run
chat & writing · #66 of 402 · 4.6% of its score Beats [Claude Haiku 5.5](claude-haiku-5-5.md), [DeepSeek V4 Flash (0731)](deepseek-v4-flash.md), [GPT-5.2](gpt-5-2.md) here, though they rank higher overall. Trails [Nemotron Cascade 2 30B-A3B](nemotron-cascade-2-30b-a3b.md), [Gemma 4 31B](gemma-4-31b.md), [Gemini 2.5 Pro](gemini-2-5-pro.md) here, though they rank lower overall. 
test            result  vs the others in its run    difficulty  counts  why
LMArena (text)  1441.3  ▁▁▁▂▃▃▃▆▆▇●▃ beats 305/385  avg 1260      4.6%  blunt
math · #67 of 431 · 15% of its score Beats [Qwen3.6 Max Preview](qwen3-6-max-preview.md), [Claude Sonnet 4.6](claude-sonnet-4-6.md), [Claude Opus 4.5](claude-opus-4-5.md) here, though they rank higher overall. Trails [INTELLECT-3](intellect-3.md), [GPT-5 mini](gpt-5-mini.md), [Qwen3-Coder-Next (80B-A3B)](qwen3-coder-next.md) here, though they rank lower overall. 
test                                result  vs the others in its run    difficulty            counts  why
IMO-AnswerBench (maker)              82.0%  ▁▁·▁▁▁▄▆●▄▂▃ beats 29/44    medium · avg 38%        4.2%  small run · several runs
HMMT February 2025 (maker)           97.1%  ▁▁▁▃▁▅▅▃██●▄ beats 28/31    easy · avg 80%          3.1%  maxed out · small run · several runs
AIME 2025 (maker)                    95.7%  ▁·▁▁▁▂▅▆█▇▅● beats 130/133  medium · avg 63%        2.8%  sharp · maxed out · small run
OTIS mock AIME 2024–2025             83.3%  ▃▃▄▃▃▃▇●█▃▂▅ beats 120/197  very hard · avg 7.2%    2.2%  sharp · noisy
HMMT November 2025 (maker)           93.5%  ▁▁····▁▅▃█●▁ beats 18/23    easy · avg 77%          2.1%  maxed out · small run · several runs
FormalProofBench (200 graduate-le…    4.5%  ▃●·▃··▅▃█··▃ beats 2/10     very hard · avg 3.1%    0.7%  noisy
AIME 2026 I (maker)                  92.9%  █··········● beats 1/1                                 —  not counted yet: no other model in the …
web search · #73 of 84 · 10% of its score Beats [DeepSeek V4 Flash (Preview, Apr 2026)](deepseek-v4-flash-preview.md), [Nemotron 3 Ultra 550B-A55B](nemotron-3-ultra-550b-a55b.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-ZH (maker)                66.6%  ▁▁▁▁▁▃·▁▄▅▄● beats 18/23  hard · avg 25%      3.4%  at its level · small run · several runs
BrowseComp (maker)                   52.0%  ▂·▃▁▂▂▆●█▂▂▄ beats 21/46  hard · avg 20%      2.5%  at its level · small run · several runs
Humanity's Last Exam with tools (…   42.8%  ▁·▁▂▂▁▄▄●█▄▅ beats 24/62  hard · avg 16%      2.4%  at its level · small run · several runs
BrowseComp with context managemen…   67.5%  ▄·●▂·█▂·▆▄·▂ beats 3/15   medium · avg 38%    1.9%  at its level · small run · several runs
other tests (13) 
test                                result  vs the others in its run    difficulty            counts  why
GPQA Diamond                         83.3%  ▁▁▁▁▂▄▆▄▅●█▅ beats 137/211  hard · avg 39%           13%  sharp · at its level · several runs
Humanity's Last Exam (maker)         24.8%  ▁▁▁▄▆▆▇●█▆▁▃ beats 62/111   very hard · avg 4.9%    3.6%  small run · several runs
Medmarks-V weighted mean win rate…   62.0%  ▂▃▅▂▆▃█▅█▅▃● beats 31/34    medium · avg 41%        3.5%  blunt · at its level
EQ-Bench 3 (emotional intelligenc…  1271.3  ▁·▂▁▄▅▅▃●▄▁▁ beats 39/62    avg 1034                2.8%  typical
Chess Puzzles (Epoch)                 6.0%  █●▁▁▁▂▃▃▃▂▁▁ beats 47/137   very hard · avg 5.8%    2.3%  sharp · noisy
LongBench v2 (maker)                 59.1%  ▂·▁·▂▃▁▄▄▃●▄ beats 23/33    very hard · avg 35%     1.8%  at its level · small run
RPC-Bench overall, text input (Z.…   28.8%  ▄··▄▄·●▄█·▄▄ beats 3/8      very hard · avg 12%     1.8%  at its level
ResearchRubrics                      62.0%  ███··██·●··█ beats 5/6      hard · avg 33%          1.8%  at its level
MMLU-Pro (maker)                     84.3%  ▁▁·▁▁▁▃▂▄▅●▁ beats 163/188  medium · avg 55%        1.6%  small run
SimpleQA Verified                    32.2%  ▁▁▃▂▃▃●▆█▄▂▅ beats 22/81    very hard · avg 8.0%    1.6%  at its level
GAIA (no-file subset)                61.9%  ●▃····█▃···▃ beats 0/6      medium · avg 49%        0.9%  at its level
CyberGym (maker)                     23.5%  ●···▄··▄█▄█▄ beats 0/8      hard · avg 19%          0.8%  at its level · small run
Vectara hallucination leaderboard…   88.3%  ▁▂▁▂●█▂▃▄▃▁▁ beats 29/98    very easy · avg 90%     0.0%  blunt
## 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   31% ▇▇▇▇
GLM-5 Team (submitted / maker-reported)        10   19% ▇▇
zai-org/GLM-5 model card (submitted / m…        8   14% ▇▇
LMArena leaderboard dataset                     2   13% ▇▇
Epoch AI                                        4   12% ▇
Medmarks: A Comprehensive Open-Source L…        1    4% ▇
EQ-Bench 3 canonical leaderboard results        1    3% ▇
stepfun-ai/Step-3.5-Flash model card            2    3% ▇
RPC-Bench leaderboard (submitted / make…        1    2% ▇
FormalProofBench                                1    1% ▇
Vectara hallucination leaderboard               1    0% ▇
prior: pull toward the middle                        0% ▇
weighted average                                   = 67.6
Licences: zai-org/GLM-4.7 model card ([MIT](../../licenses/zai-org--GLM-4.7.txt)) · GLM-5 Team ([CC BY 4.0](../../licenses/report-2602.15763v2.txt)) · zai-org/GLM-5 model card ([MIT](../../licenses/zai-org--GLM-5.txt)) · LMArena leaderboard dataset ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · Epoch AI ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · Medmarks: A Comprehensive Open-Source LLM Benchmark Suite for Medical Tasks ([CC BY 4.0](../../licenses/paper-2605.01417v1-CC-BY-4.0.txt)) · EQ-Bench 3 canonical leaderboard results ([MIT](../../licenses/eqbench3-MIT.txt)) · stepfun-ai/Step-3.5-Flash model card ([Apache-2.0](../../licenses/stepfun-ai--Step-3.5-Flash.txt)) · RPC-Bench leaderboard ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt)) · FormalProofBench ([CC-BY-4.0](https://creativecommons.org/licenses/by/4.0/)) · Vectara hallucination leaderboard ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0))
Not counted (1) 
source                  test         score  why
zai-org/GLM-5 model c…  AIME 2026 I  92.9%  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-4.7.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-4.7.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-4.7 (ud-q4_k_xl, needs ~214.7 GB, from [huggingface.co/unsloth](https://huggingface.co/unsloth/GLM-4.7-GGUF/tree/main)) ```sh llama cli -hf unsloth/GLM-4.7-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-4.7-GGUF:UD-Q4_K_XL ``` Other versions: ud-q2_k_xl (~107.6 GB) `llama cli -hf unsloth/GLM-4.7-GGUF:UD-Q2_K_XL` · ud-iq1_s (~54.05 GB) `llama cli -hf unsloth/GLM-4.7-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-4.7 (ud-q4_k_xl, needs ~214.7 GB, from [huggingface.co/unsloth](https://huggingface.co/unsloth/GLM-4.7-GGUF/tree/main)) ```sh lms get https://huggingface.co/unsloth/GLM-4.7-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-4.7-GGUF@UD-Q2_K_XL -y` · ud-iq1_s (~54.05 GB) `lms get https://huggingface.co/unsloth/GLM-4.7-GGUF@UD-IQ1_S -y` · 4bit (~198.6 GB, Mac only) `lms get https://huggingface.co/mlx-community/GLM-4.7-4bit -y` · 8bit (~374.9 GB, Mac only) `lms get https://huggingface.co/mlx-community/GLM-4.7-8bit -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-4.7 (4bit, needs ~198.6 GB, Mac only, from [huggingface.co/mlx-community](https://huggingface.co/mlx-community/GLM-4.7-4bit/tree/0e9f6c4babaef5d5fd04c9efc8770ef234b6d576)) ```sh mlx_lm.chat --model mlx-community/GLM-4.7-4bit ``` 3. Or start an OpenAI-compatible server at http://localhost:8080/v1 ```sh mlx_lm.server --model mlx-community/GLM-4.7-4bit ``` Other versions: 8bit (~374.9 GB, Mac only) `mlx_lm.chat --model mlx-community/GLM-4.7-8bit` Model repository and documentation: [huggingface.co/zai-org](https://huggingface.co/zai-org/GLM-4.7) [interactive version](https://whatmodel.sh/model/glm-4-7) · [JSON](https://whatmodel.sh/api?format=json&all=1)