[whatmodel.sh](../../README.md) / Nemotron-Cascade-14B-Thinking # Nemotron-Cascade-14B-Thinking NVIDIA · released 2025-12 · 14.77B params · nvidia-open-model-license **score 59.8** ±1.0 · #206 of 732 · from 12 benchmark results across 1 source · † no independently run results verified Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only. ## What it's good at ● marks Nemotron-Cascade-14B-Thinking among every model on that task or test, worst to best. 
task            vs the others          rank  counts  tests
agent tasks     ▁▁▃▆█●▅▅▄▄▂▁   #178 of 313*    7.6%      2
coding          ▁▁▁▃▅▇█●▅▄▃▁    #170 of 469     54%      4
chat & writing  ▁▁▁▃▆▄▆●▆▇▇▃   #194 of 402*    1.8%      1
math            ▁▁▃▅█▆▄●▄▂▁▁   #124 of 431*     17%      2
agent tasks · #178 of 313 · 7.6% 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), [Jamba 1.5 Large](jamba-1-5-large.md) here, though they rank lower overall. 
test                        result  vs the others in its run    difficulty        counts  why
SWE-bench Verified (maker)   43.1%  ▁▁▁▁▁▁▂●█▆▁▁ beats 32/138   hard · avg 19%      6.1%  sharp · at its level · small run
BFCL v3 (maker)              67.5%  ▁▁▁▁▁▃▅█▆●▄▁ beats 136/171  medium · avg 58%    1.4%  blunt · at its level · small run
coding · #170 of 469 · 54% 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
LiveCodeBench Pro 2025Q2 (Medium)…   10.5%  ▃▃····▃·█·▃● beats 7/7     very hard · avg 2.6%     31%  sharp
LiveCodeBench Pro 2025Q2 (Easy) (…   68.9%  ▄·▄····▄▄▄█● beats 7/7     medium · avg 46%         12%  at its level
SWE-bench Verified (maker)           43.1%  ▁▁▁▁▁▁▂●█▆▁▁ beats 32/138  hard · avg 19%          6.1%  sharp · at its level · small run
LiveCodeBench v6 (Aug 2024–May 20…   74.6%  ▄▄···▄·▄▄█·● beats 7/7     medium · avg 58%        5.1%  at its level
chat & writing · #194 of 402 · 1.8% of its score Beats [Seed-OSS 36B Instruct](seed-oss-36b.md), [Gemma 2 9B IT SimPO](gemma-2-9b-simpo.md) here, though they rank higher overall. Trails [LFM2.5 1.2B Instruct](lfm2-5-1-2b-instruct.md), [LFM2.5 1.2B Thinking](lfm2-5-1-2b-thinking.md), [LFM2.5 8B-A1B](lfm2-5-8b-a1b.md) here, though they rank lower overall. 
test                                result  vs the others in its run  difficulty      counts  why
IFEval prompt-level strict (maker)   81.9%  ▁▁··▂··▂●█▃▃ beats 12/26  easy · avg 77%    1.8%  small run
math · #124 of 431 · 17% of its score Beats [Claude Sonnet 4.6](claude-sonnet-4-6.md), [MiniMax-M3](minimax-m3.md), [MiMo-V2.5 (310B-A15B)](mimo-v2-5.md) here, though they rank higher overall. Trails [Olmo 3.1 32B Think](olmo-3-1-32b-think.md), [Granite 4.2 8B](granite-4-2-8b.md) here, though they rank lower overall. 
test               result  vs the others in its run   difficulty        counts  why
AIME 2025 (maker)   83.3%  ▁·▁▁▁▂▅▆█●▅▁ beats 93/133  medium · avg 56%    9.1%  sharp · small run
AIME 2024 (maker)   89.7%  ▁▁▁▂▁▃▁▅██●▃ beats 71/85   easy · avg 70%      7.6%  sharp · small run
other tests (4) 
test                                result  vs the others in its run    difficulty        counts  why
GPQA Diamond (maker)                 69.6%  ▁▁▁▁▂▄▅▅●██▄ beats 155/322  hard · avg 46%       11%  sharp · at its level · small run
MMLU (maker)                         85.1%  ▁▁▁▁▁▂▂▅██●▄ beats 187/214  medium · avg 71%    6.2%  small run
MMLU-Pro (maker)                     77.0%  ▁▁·▁▁▁▃▂▄●█▁ beats 108/188  medium · avg 60%    4.9%  small run
IFBench (aggregation unspecified)…   41.7%  ▂▆▂▄●▄▂▄▄▆█▇ beats 29/86    hard · avg 30%      3.4%  at its level · small run
## 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
NVIDIA (submitted / maker-reported)       12  100% ▇▇▇▇▇▇▇▇▇▇▇▇
prior: pull toward the middle                   0% ▇
weighted average                              = 59.8
Licences: NVIDIA ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) ## 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 Nemotron-Cascade-14B-Thinking (Q4_K_M, needs ~9 GB, from [huggingface.co/bartowski](https://huggingface.co/bartowski/nvidia_Nemotron-Cascade-14B-Thinking-GGUF/tree/bd0a557fd819faa543d240b70a5312c4bbff317b)) ```sh llama cli -hf bartowski/nvidia_Nemotron-Cascade-14B-Thinking-GGUF:Q4_K_M ``` 3. Or start a local server and open the address it prints (http://localhost:9931) ```sh llama serve -hf bartowski/nvidia_Nemotron-Cascade-14B-Thinking-GGUF:Q4_K_M ``` Other versions: Q8_0 (~15.7 GB) `llama cli -hf bartowski/nvidia_Nemotron-Cascade-14B-Thinking-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 Nemotron-Cascade-14B-Thinking (Q4_K_M, needs ~9 GB, from [huggingface.co/bartowski](https://huggingface.co/bartowski/nvidia_Nemotron-Cascade-14B-Thinking-GGUF/tree/bd0a557fd819faa543d240b70a5312c4bbff317b)) ```sh lms get https://huggingface.co/bartowski/nvidia_Nemotron-Cascade-14B-Thinking-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 (~15.7 GB) `lms get https://huggingface.co/bartowski/nvidia_Nemotron-Cascade-14B-Thinking-GGUF@Q8_0 -y` · 4bit (~8.3 GB, Mac only) `lms get https://huggingface.co/introvoyz041/Nemotron-Cascade-14B-Thinking-mlx-4Bit -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 Nemotron-Cascade-14B-Thinking (Q4_K_M, needs ~9 GB, from [huggingface.co/bartowski](https://huggingface.co/bartowski/nvidia_Nemotron-Cascade-14B-Thinking-GGUF/tree/bd0a557fd819faa543d240b70a5312c4bbff317b)) ```sh ollama run hf.co/bartowski/nvidia_Nemotron-Cascade-14B-Thinking-GGUF:Q4_K_M ``` Other versions: Q8_0 (~15.7 GB) `ollama run hf.co/bartowski/nvidia_Nemotron-Cascade-14B-Thinking-GGUF:Q8_0` ### 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 Nemotron-Cascade-14B-Thinking (4bit, needs ~8.3 GB, Mac only, from [huggingface.co/introvoyz041](https://huggingface.co/introvoyz041/Nemotron-Cascade-14B-Thinking-mlx-4Bit/tree/0cad98b3049e43cf5414d528201c5044e5443c64)) ```sh mlx_lm.chat --model introvoyz041/Nemotron-Cascade-14B-Thinking-mlx-4Bit ``` 3. Or start an OpenAI-compatible server at http://localhost:8080/v1 ```sh mlx_lm.server --model introvoyz041/Nemotron-Cascade-14B-Thinking-mlx-4Bit ``` Model repository and documentation: [huggingface.co/nvidia](https://huggingface.co/nvidia/Nemotron-Cascade-14B-Thinking) [interactive version](https://whatmodel.sh/model/nemotron-cascade-14b-thinking) · [JSON](https://whatmodel.sh/api?format=json&all=1)