[whatmodel.sh](../../README.md) / Kimi K2 Instruct (0905) # Kimi K2 Instruct (0905) Moonshot AI · released 2025-09 · 1000B params (32B active) · modified-mit **score 63.4** ±2.1 · #165 of 732 · from 5 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 Kimi K2 Instruct (0905) among every model on that task or test, worst to best. 
task            vs the others          rank  counts  tests
coding          ▁▁▁▃▅▇█●▅▄▃▁   #167 of 469*     27%      1
chat & writing  ▁▁▁▃▆▄▆█▆●▇▃    #93 of 402*     47%      1
coding · #167 of 469 · 27% of its score Beats [Qwen3-Coder-Next (80B-A3B)](qwen3-coder-next.md), [Gemini 2.5 Pro](gemini-2-5-pro.md), [Grok 4.1 Fast (Reasoning)](grok-4-1-fast-reasoning.md) here, though they rank higher overall. Trails [QwQ-32B-Preview](qwq-32b-preview.md), [Ministral 3 8B Reasoning 2512](ministral-3-8b-reasoning.md), [Phi-4-reasoning 14B](phi-4-reasoning.md) here, though they rank lower overall. 
test                                result  vs the others in its run  difficulty      counts  why
WebApp1K-Duo-React pass@1 (onekq-…   57.9%  ▁··▁·▁▁▁●█▇█ beats 11/45  hard · avg 29%     27%  at its level
chat & writing · #93 of 402 · 47% 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) here, though they rank lower overall. 
test            result  vs the others in its run    difficulty  counts  why
LMArena (text)  1418.7  ▁▁▁▂▃▃▃▆▆●█▃ beats 272/385  avg 1260       47%  blunt
other tests (3) 
test                                result  vs the others in its run   difficulty           counts  why
CPTUB average (SpeakLeash, 0-5 ju…   79.7%  ▁▂▂▂▃█▃▆▅●▂▃ beats 80/95   medium · avg 65%        22%  typical
Vectara hallucination leaderboard…   93.8%  ▁▁▂▃▄●█▆▃▂▂▁ beats 50/140  very easy · avg 94%    1.6%  blunt · maxed out · noisy
Vectara hallucination leaderboard…   82.1%  ▁●▁▂▅█▂▃▄▃▁▁ beats 8/98    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
LMArena leaderboard dataset              1   47% ▇▇▇▇▇▇
WebApp1K models leaderboard              1   27% ▇▇▇
SpeakLeash CPTUB                         1   22% ▇▇▇
Vectara hallucination leaderboard        2    2% ▇
prior: pull toward the middle                 2% ▇
weighted average                            = 63.4
Licences: LMArena leaderboard dataset ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · WebApp1K models leaderboard ([MIT](../../licenses/webapp1k-leaderboard-MIT.txt)) · SpeakLeash CPTUB ([MIT](../../licenses/hf-leaderboard-spaces-MIT.txt)) · Vectara hallucination leaderboard ([Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0)) ## How to run it ### 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 Kimi K2 Instruct (0905) (ud-q4_k_xl, needs ~588.12 GB, from [huggingface.co/unsloth](https://huggingface.co/unsloth/Kimi-K2-Instruct-0905-GGUF/tree/ca516d05c7621c0615db3fc7efa63c9617547363)) ```sh llama cli -hf unsloth/Kimi-K2-Instruct-0905-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/Kimi-K2-Instruct-0905-GGUF:UD-Q4_K_XL ``` Other versions: ud-q2_k_xl (~383.57 GB) `llama cli -hf unsloth/Kimi-K2-Instruct-0905-GGUF:UD-Q2_K_XL` · ud-iq1_s (~281.9 GB) `llama cli -hf unsloth/Kimi-K2-Instruct-0905-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 Kimi K2 Instruct (0905) (ud-q4_k_xl, needs ~588.12 GB, from [huggingface.co/unsloth](https://huggingface.co/unsloth/Kimi-K2-Instruct-0905-GGUF/tree/ca516d05c7621c0615db3fc7efa63c9617547363)) ```sh lms get https://huggingface.co/unsloth/Kimi-K2-Instruct-0905-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 (~383.57 GB) `lms get https://huggingface.co/unsloth/Kimi-K2-Instruct-0905-GGUF@UD-Q2_K_XL -y` · ud-iq1_s (~281.9 GB) `lms get https://huggingface.co/unsloth/Kimi-K2-Instruct-0905-GGUF@UD-IQ1_S -y` · 3bit (~449.3 GB, Mac only) `lms get https://huggingface.co/mlx-community/Kimi-K2-Instruct-0905-mlx-3bit -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 Kimi K2 Instruct (0905) (3bit, needs ~449.3 GB, Mac only, from [huggingface.co/mlx-community](https://huggingface.co/mlx-community/Kimi-K2-Instruct-0905-mlx-3bit/tree/426c860cd40026e83dbaa6d9f12db5b082f6b1af)) ```sh mlx_lm.chat --model mlx-community/Kimi-K2-Instruct-0905-mlx-3bit ``` 3. Or start an OpenAI-compatible server at http://localhost:8080/v1 ```sh mlx_lm.server --model mlx-community/Kimi-K2-Instruct-0905-mlx-3bit ``` Model repository and documentation: [huggingface.co/moonshotai](https://huggingface.co/moonshotai/Kimi-K2-Instruct-0905) [interactive version](https://whatmodel.sh/model/kimi-k2-0905) · [JSON](https://whatmodel.sh/api?format=json&all=1)