[whatmodel.sh](../../README.md) / Phi-4-reasoning-plus 14B # Phi-4-reasoning-plus 14B Microsoft · released 2025-04 · 14.7B params · mit **score 58.5** ±0.8 · #219 of 732 · from 32 benchmark results across 5 sources Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only. ## What it's good at ● marks Phi-4-reasoning-plus 14B among every model on that task or test, worst to best. 
task            vs the others         rank  counts  tests
coding          ▁▁▁▃▅▇█●▅▄▃▁   #182 of 469     13%      5
chat & writing  ▁▁▁▃▆▄▆█●▇▇▃   #157 of 402    7.4%      3
math            ▁▁▃▅█▆●█▄▂▁▁   #161 of 431     35%      5
coding · #182 of 469 · 13% of its score Beats [Qwen3.5 9B](qwen3-5-9b.md), [Gemini 2.5 Flash](gemini-2-5-flash.md), [Qwen3-VL 32B Thinking](qwen3-vl-32b-thinking.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
HumanEval+ (maker)                   92.3%  ▁·▁▁▂▃▄▄█▅▄● beats 139/145  easy · avg 77%          8.0%  sharp · several runs
LiveCodeBench v6 (window unspecif…   53.1%  ●·▄·▄····▄·▄ beats 1/5      medium · avg 41%        4.1%  at its level
SciCode main problems                 7.2%  ▄··▄····█●·▄ beats 4/5      very hard · avg 3.8%    0.9%  too hard for it
LiveCodeBench (Aug 1, 2024–Feb 1,…   53.1%  ●··········█ beats 0/1                                 —  not counted yet: no other model in the …
LiveCodeBench 2024-08 to 2025-01 …   53.1%  ●··········█ beats 0/1                                 —  not counted yet: no other model in the …
chat & writing · #157 of 402 · 7.4% of its score Beats [INTELLECT-3](intellect-3.md), [MiniMax-M2](minimax-m2.md), [MiniCPM5 2B](minicpm5-2b.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
Arena-Hard (version/judge unspeci…   79.0%  ▃▃▂▅▃▆▅█▅●▂▃ beats 27/30   medium · avg 59%    5.3%  at its level · several runs
IFEval strict (prompt/instruction…   84.9%  █·········█● beats 2/2     easy · avg 67%      1.1%  small run
IFEval (aggregation unspecified) …   84.9%  ▁▂▄▂▂▄▆●█▅▂▁ beats 73/145  easy · avg 74%      0.9%  typical
math · #161 of 431 · 35% 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
AIME 2025 (maker)                 78.0%  █··▄··█··●·▄ beats 5/6    medium · avg 52%        12%  sharp · several runs
AIME 2024 (maker)                 81.3%  ▄···▄█·▄●··▄ beats 5/6    medium · avg 62%        12%  sharp · several runs
HMMT 2025 (edition unspecified)   47.7%  ▄··●▄·▄····█ beats 1/5    hard · avg 29%         6.7%  sharp · at its level
MATH-500                          95.4%  ▄·●·····█··█ beats 1/5    very easy · avg 92%    2.5%  maxed out
HMMT February 2025 (maker)        53.6%  ▁▁▁▃●▅▅▃██▁▄ beats 5/31   hard · avg 32%         2.2%  at its level · small run
other tests (9) 
test                                result  vs the others in its run    difficulty            counts  why
GPQA Diamond (maker)                 69.3%  █··●··▄···█▄ beats 2/6      hard · avg 43%           23%  sharp · at its level · several runs
MMLU-Pro                             79.2%  █··█··███··● beats 5/5      medium · avg 57%         10%  at its level · several runs
Humanity's Last Exam                  5.9%  █··█●···█·██ beats 2/5      very hard · avg 3.7%    3.9%  typical
FlenQA (3K-token subset) (maker)     97.9%  ▄···▄····▄·● beats 4/4      easy · avg 86%          2.7%  sharp · noisy · several runs
Open PT-LLM Leaderboard average (…   66.0%  ▃▁▁▁▁▁▁▁▂●▅▁ beats 121/276  medium · avg 72%        1.1%  at its level · noisy
IFBench (aggregation unspecified)    51.7%  ███···●█···█ beats 3/5      hard · avg 34%          1.1%  at its level
EuroEval HellaSwag (English), tes…   25.0%  ●·▁▁▂▁▂▅█▇▄▁ beats 0/100    medium · avg 67%        0.8%  sharp · noisy
Omni-MATH (maker)                    81.9%  █·█·█·██··●█ beats 5/6      easy · avg 68%          0.8%  blunt · small run · several runs
EuroEval Life in the UK (English …   52.8%  ▁▁▁▁▁▁●▂▄█▄▃ beats 22/146   easy · avg 82%          0.4%  noisy
## 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
Falcon-H1R: Pushing the Reasoning Front…       10   45% ▇▇▇▇▇
Microsoft (submitted / maker-reported)         10   30% ▇▇▇▇
microsoft/Phi-4-reasoning model card (s…        9   22% ▇▇▇
EuroEval leaderboards                           2    1% ▇
Open Portuguese LLM Leaderboard                 1    1% ▇
prior: pull toward the middle                        0% ▇
weighted average                                   = 58.5
Licences: Falcon-H1R: Pushing the Reasoning Frontiers with a Hybrid Model for Efficient Test-Time Scaling ([CC BY 4.0](../../licenses/report-2601.02346v1.txt)) · Microsoft ([CC BY 4.0](../../licenses/report-2504.21318v1.txt)) · microsoft/Phi-4-reasoning model card ([mit](../../licenses/microsoft--Phi-4-reasoning.txt)) · EuroEval leaderboards ([MIT](https://github.com/EuroEval/leaderboards/blob/1a77f1d2f8ba1ba05ab73287fb090d76bf41b2e0/LICENSE)) · Open Portuguese LLM Leaderboard ([Apache-2.0](../../licenses/hf-leaderboard-spaces-Apache-2.0.txt))
Not counted (2) 
source                  test                            score  why
microsoft/Phi-4-reaso…  LiveCodeBench (Aug 1, 2024–Fe…  53.1%  too few models have it yet
Microsoft               LiveCodeBench 2024-08 to 2025…  53.1%  too few models have it yet
## 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 Phi-4-reasoning-plus 14B (Q4_K_M, needs ~9.1 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/Phi-4-reasoning-plus-GGUF/tree/32a07ec573ed9a856f7f75c26a558bc8f23cca55)) ```sh llama cli -hf lmstudio-community/Phi-4-reasoning-plus-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/Phi-4-reasoning-plus-GGUF:Q4_K_M ``` Other versions: Q8_0 (~15.6 GB) `llama cli -hf lmstudio-community/Phi-4-reasoning-plus-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 Phi-4-reasoning-plus 14B (Q4_K_M, needs ~9.1 GB, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/Phi-4-reasoning-plus-GGUF/tree/32a07ec573ed9a856f7f75c26a558bc8f23cca55)) ```sh lms get https://huggingface.co/lmstudio-community/Phi-4-reasoning-plus-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 (~15.6 GB) `lms get https://huggingface.co/lmstudio-community/Phi-4-reasoning-plus-GGUF@Q8_0 -y` · 4bit (~8.2 GB, Mac only) `lms get https://huggingface.co/lmstudio-community/Phi-4-reasoning-plus-MLX-4bit -y` · 8bit (~15.6 GB, Mac only) `lms get https://huggingface.co/mlx-community/Phi-4-reasoning-plus-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 Phi-4-reasoning-plus 14B (Q4_K_M, needs ~11 GB, from [ollama.com](https://ollama.com/library/phi4-reasoning/tags)) ```sh ollama run phi4-reasoning:plus ``` ### 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 Phi-4-reasoning-plus 14B (4bit, needs ~8.2 GB, Mac only, from [huggingface.co/lmstudio-community](https://huggingface.co/lmstudio-community/Phi-4-reasoning-plus-MLX-4bit/tree/035f537492b8cedf94f354037b147a867b2c10c2)) ```sh mlx_lm.chat --model lmstudio-community/Phi-4-reasoning-plus-MLX-4bit ``` 3. Or start an OpenAI-compatible server at http://localhost:8080/v1 ```sh mlx_lm.server --model lmstudio-community/Phi-4-reasoning-plus-MLX-4bit ``` Other versions: 8bit (~15.6 GB, Mac only) `mlx_lm.chat --model mlx-community/Phi-4-reasoning-plus-8bit` Model repository and documentation: [huggingface.co/microsoft](https://huggingface.co/microsoft/Phi-4-reasoning-plus) [interactive version](https://whatmodel.sh/model/phi-4-reasoning-plus) · [JSON](https://whatmodel.sh/api?format=json&all=1)