[whatmodel.sh](../../README.md) / GLM-4.7-Flash # GLM-4.7-Flash Z.ai · released 2026-01 · 31.2B params (3B active) · mit **score 58.3** ±1.2 · #220 of 732 · from 11 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-4.7-Flash among every model on that task or test, worst to best. 
task            vs the others            rank  counts  tests
agent tasks     ▁▁▃▆●▄▅▅▄▄▂▁     #197 of 313*     10%      3
coding          ▁▁▁▃▅▇●▇▅▄▃▁     #283 of 469*     22%      3
chat & writing  ▁▁▁▃▆▄▆█●▇▇▃     #148 of 402*     14%      1
math            ▁▁▃▅█▆●█▄▂▁▁     #165 of 431*     10%      2
web search                     not ranked yet       —      1
agent tasks · #197 of 313 · 10% of its score Beats [o1 (2024-12-17)](o1-2024-12-17.md), [o3-mini](o3-mini.md), [DeepSeek R1 0528](deepseek-r1-0528.md) here, though they rank higher overall. Trails [SmolLM3 3B](smollm3-3b.md), [Jamba 1.5 Mini](jamba-1-5-mini.md), [Mellum2 12B-A2.5B Instruct](mellum2-12b-a2-5b-instruct.md) here, though they rank lower overall. 
test                        result  vs the others in its run   difficulty           counts  why
SWE-bench Verified           39.7%  ▁▁▁▁▁▁▂▄●▆▁▁ beats 54/138  hard · avg 23%          10%  sharp · at its level · small run
BrowseComp (maker)           42.8%  ▂·▃▁▂▂●▅█▂▂▄ beats 16/46   very hard · avg 11%       —  not counted yet: no other model in the …
τ²-bench aggregate (maker)   79.5%  ▁▁▁·▁▁▃▃▃●█▁ beats 22/39   medium · avg 45%          —  not counted yet: no other model in the …
coding · #283 of 469 · 22% of its score Beats [Qwen3-VL 32B Instruct](qwen3-vl-32b-instruct.md), [Solar Open 100B (102B-A12B)](solar-open-100b.md) here, though they rank higher overall. Trails [DeepSeek LLM 67B Chat](deepseek-llm-67b-chat.md), [Mistral NeMo Instruct (2407)](mistral-nemo-instruct-2407.md), [QwQ-32B-Preview](qwq-32b-preview.md) here, though they rank lower overall. 
test                                result  vs the others in its run   difficulty        counts  why
Luau Leetcode fully solved (Torpe…   14.9%  ▁●▁▁·▁▂▃▅▃█▇ beats 1/49    hard · avg 17%       12%  sharp · at its level
SWE-bench Verified                   39.7%  ▁▁▁▁▁▁▂▄●▆▁▁ beats 54/138  hard · avg 23%       10%  sharp · at its level · small run
LiveCodeBench v6 (window unspecif…   64.0%  ▂▃▄▄▅●█▆▄▇▄▆ beats 28/67   medium · avg 38%       —  not counted yet: no other model in the …
chat & writing · #148 of 402 · 14% of its score Beats [INTELLECT-3](intellect-3.md), [MiniMax-M2](minimax-m2.md), [Nemotron 3 Super 120B-A12B](nemotron-3-super-120b-a12b.md) here, though they rank higher overall. Trails [LFM2.5 1.2B Thinking](lfm2-5-1-2b-thinking.md), [LFM2.5 8B-A1B](lfm2-5-8b-a1b.md), [LFM2.5 2.6B](lfm2-5-2-6b.md) here, though they rank lower overall. 
test            result  vs the others in its run    difficulty  counts  why
LMArena (text)  1364.7  ▁▁▁▂▃▃▃▆●▇█▃ beats 209/385  avg 1260       14%  blunt
math · #165 of 431 · 10% of its score Beats [MiMo-V2.5 (310B-A15B)](mimo-v2-5.md), [MiniMax-M2.7](minimax-m2-7.md), [MiniMax-M2.1](minimax-m2-1.md) here, though they rank higher overall. Trails [Olmo 3 7B Think](olmo-3-7b-think.md), [Qwen3-VL 4B Thinking](qwen3-vl-4b-thinking.md), [Nemotron 3 Nano 30B-A3B](nemotron-3-nano-30b-a3b.md) here, though they rank lower overall. 
test                      result  vs the others in its run    difficulty            counts  why
OTIS mock AIME 2024–2025   58.3%  ▃▃▄▃▃▃●▇█▃▂▅ beats 88/197   very hard · avg 7.2%     10%  sharp · at its level · noisy
AIME 2025 (maker)          91.6%  ▁·▁▁▁▂▅▆█▇●▁ beats 118/133  medium · avg 42%           —  not counted yet: no other model in the …
web search · 0.0% of its score 
test                result  vs the others in its run  difficulty           counts  why
BrowseComp (maker)   42.8%  ▂·▃▁▂▂●▅█▂▂▄ beats 16/46  very hard · avg 11%       —  not counted yet: no other model in the …
other tests (9) 
test                                result  vs the others in its run   difficulty            counts  why
GPQA Diamond                         60.5%  ▁▁▁▁▂▄▆●▅▇█▅ beats 88/211  hard · avg 39%           23%  sharp · at its level
EQ-Bench 3 (emotional intelligenc…  1151.3  ▁·▂▁▄●▅▃█▄▁▁ beats 20/62   avg 1034                8.5%  typical
RobloxQA v2.0 multiple choice (To…   83.1%  ▁▁●▁█▄▃▃▆▂▂▂ beats 3/49    easy · avg 77%          7.9%  typical
EuroEval Life in the UK (English …   94.1%  ▂·▂▂·▂·▃▃·█● beats 13/14   easy · avg 80%          6.8%  typical
EuroEval HellaSwag (English), val…   74.8%  ▂··▂▂·▂▆●█·▄ beats 7/15    medium · avg 63%        5.0%  at its level
Chess Puzzles (Epoch)                 0.0%  ●▁▁▁▁▂▃▃▃▂▁▁ beats 31/137  very hard · avg 5.8%    1.9%  sharp · too hard for it · noisy
Vectara hallucination leaderboard…   90.7%  ▁▂▁▂▅●▂▃▄▃▁▁ beats 51/98   very easy · avg 90%     0.0%  blunt
Humanity's Last Exam (maker)         14.4%  ▁▁▁▄▆●▇██▆▁▃ beats 36/111  very hard · avg 3.8%       —  not counted yet: no other model in the …
GPQA (subset unspecified) (maker)    75.2%  ▂▁▂▂▄▃▆▃▅●▃▃ beats 89/106  very hard · avg 32%        —  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
Epoch AI                                        3   36% ▇▇▇▇
Roblox LLM Leaderboard                          2   20% ▇▇
LMArena leaderboard dataset                     1   14% ▇▇
EuroEval leaderboards                           2   12% ▇
SERA: Soft-Verified Efficient Repositor…        1   10% ▇
EQ-Bench 3 canonical leaderboard results        1    9% ▇
Vectara hallucination leaderboard               1    0% ▇
prior: pull toward the middle                        1% ▇
weighted average                                   = 58.3
Licences: Epoch AI ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · Roblox LLM Leaderboard ([MIT](../../licenses/roblox-llm-leaderboard-MIT.txt)) · LMArena leaderboard dataset ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · EuroEval leaderboards ([MIT](https://github.com/EuroEval/leaderboards/blob/1a77f1d2f8ba1ba05ab73287fb090d76bf41b2e0/LICENSE)) · SERA: Soft-Verified Efficient Repository Agents ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · EQ-Bench 3 canonical leaderboard results ([MIT](../../licenses/eqbench3-MIT.txt)) · Vectara hallucination leaderboard ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0))
Not counted (7) 
source                  test                            score  why
zai-org/GLM-4.7-Flash…  AIME 2025                       91.6%  too few models have it yet
zai-org/GLM-4.7-Flash…  BrowseComp                      42.8%  too few models have it yet
zai-org/GLM-4.7-Flash…  GPQA (subset unspecified)       75.2%  too few models have it yet
zai-org/GLM-4.7-Flash…  Humanity's Last Exam            14.4%  too few models have it yet
zai-org/GLM-4.7-Flash…  LiveCodeBench v6 (window unsp…  64.0%  too few models have it yet
zai-org/GLM-4.7-Flash…  SWE-bench Verified              59.2%  only model in this source's run
zai-org/GLM-4.7-Flash…  τ²-bench aggregate              79.5%  too few models have it yet
## How to run it ### Through an API api (Z.AI) · $0.0000/M blended · $0 in / $0 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-flash.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-flash.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-Flash (Q4_K_M, needs ~18.1 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/GLM-4.7-Flash-GGUF/tree/b58019be521f54a64160968f3c37a55174b1f186)) ```sh llama cli -hf lmstudio-community/GLM-4.7-Flash-GGUF:Q4_K_M ``` 3. Or start a local server and open the address it prints (http://localhost:9931) ```sh llama serve -hf lmstudio-community/GLM-4.7-Flash-GGUF:Q4_K_M ``` Other versions: Q8_0 (~31.8 GB) `llama cli -hf lmstudio-community/GLM-4.7-Flash-GGUF:Q8_0` ### 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-Flash (Q4_K_M, needs ~18.1 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/GLM-4.7-Flash-GGUF/tree/b58019be521f54a64160968f3c37a55174b1f186)) ```sh lms get https://huggingface.co/lmstudio-community/GLM-4.7-Flash-GGUF@Q4_K_M -y ``` 3. Then run lms ls to see its name, and lms chat followed by that name Other versions: Q8_0 (~31.8 GB) `lms get https://huggingface.co/lmstudio-community/GLM-4.7-Flash-GGUF@Q8_0 -y` · 4bit (~16.9 GB, Mac only) `lms get https://huggingface.co/lmstudio-community/GLM-4.7-Flash-MLX-4bit -y` · 8bit (~31.8 GB, Mac only) `lms get https://huggingface.co/lmstudio-community/GLM-4.7-Flash-MLX-8bit -y` ### Ollama 1. Install Ollama. [Download it (macOS / Windows)](https://ollama.com/download) ```sh # Linux curl -fsSL https://ollama.com/install.sh | sh ``` 2. Run GLM-4.7-Flash (Q4_K_M, needs ~19.22 GB, from [ollama.com](https://ollama.com/library/glm-4.7-flash/tags)) ```sh ollama run glm-4.7-flash:q4_K_M ``` ### 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-Flash (4bit, needs ~16.9 GB, Mac only, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/GLM-4.7-Flash-MLX-4bit/tree/a122231a36d90f7325a0f692e26197dadcd5ab9f)) ```sh mlx_lm.chat --model lmstudio-community/GLM-4.7-Flash-MLX-4bit ``` 3. Or start an OpenAI-compatible server at http://localhost:8080/v1 ```sh mlx_lm.server --model lmstudio-community/GLM-4.7-Flash-MLX-4bit ``` Other versions: 8bit (~31.8 GB, Mac only) `mlx_lm.chat --model lmstudio-community/GLM-4.7-Flash-MLX-8bit` Model repository and documentation: [huggingface.co/zai-org](https://huggingface.co/zai-org/GLM-4.7-Flash) ## Published speed measurements Reference results for the hardware, engine and settings below. These are not verified speeds of the downloads above or predictions for your machine. 
hardware                      precision               speed  engine and settings
NVIDIA DGX Spark (GB10)       Q8_0              48.68 tok/s  llama.cpp 11fb327bf (7941), CUDA …
NVIDIA DGX Spark (GB10)       Q8_0              44.62 tok/s  llama.cpp 11fb327bf (7941), CUDA …
NVIDIA DGX Spark (GB10)       Q8_0              32.65 tok/s  llama.cpp 11fb327bf (7941), CUDA …
Apple M2 Ultra (GPU core co…  Q8_0              59.58 tok/s  llama.cpp b828e18c7 (7948), Metal…
Apple M2 Ultra (GPU core co…  Q8_0              47.44 tok/s  llama.cpp b828e18c7 (7948), Metal…
Apple M2 Ultra (GPU core co…  Q8_0              21.81 tok/s  llama.cpp b828e18c7 (7948), Metal…
NVIDIA DGX Spark (GB10)       Q8_0              42.59 tok/s  llama.cpp 11fb327bf (7941), CUDA …
NVIDIA DGX Spark (GB10)       Q8_0              38.72 tok/s  llama.cpp 11fb327bf (7941), CUDA …
Apple M2 Ultra (GPU core co…  Q8_0               40.2 tok/s  llama.cpp b828e18c7 (7948), Metal…
Apple M2 Ultra (GPU core co…  Q8_0               31.5 tok/s  llama.cpp b828e18c7 (7948), Metal…
NVIDIA GeForce RTX 5090       GGUF Q6_K_XL (U…  151.3 tok/s  llama.cpp (GGUF; version not stat…
Apple M4 Pro (14-core CPU /…  MLX 4-bit          53.6 tok/s  MLX v0.30.6 (macOS 26 Tahoe) · Of…
Apple M3 Ultra (28-core CPU…  MLX 4-bit            55 tok/s  MLX v0.30.6 (macOS 26 Tahoe) · Of…
Apple M2 Max (12-core CPU /…  MLX 4-bit          39.7 tok/s  MLX v0.30.6 (macOS 26 Tahoe) · Of…
Each speed links to its source, which has the full settings. [interactive version](https://whatmodel.sh/model/glm-4-7-flash) · [JSON](https://whatmodel.sh/api?format=json&all=1)