[whatmodel.sh](../../README.md) / Ministral 3 14B
# Ministral 3 14B
Mistral · released 2025-12 · 13.9B params · apache-2.0
**score 55.3** ±1.0 · #273 of 732 · from 18 benchmark results across 8 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks Ministral 3 14B among every model on that task or test, worst to best.
task vs the others rank counts tests
agent tasks ▁▁▃▆●▄▅▅▄▄▂▁ #217 of 313 10% 3
coding ▁▁▁▃▅●█▇▅▄▃▁ #300 of 469* 10% 2
chat & writing ▁▁▁▃▆▄●█▆▇▇▃ #285 of 402* 5.5% 2
math ▁▁▃▅█●▄█▄▂▁▁ #205 of 431* 9.0% 1
agent tasks · #217 of 313 · 10% of its score
Beats [o1 (2024-12-17)](o1-2024-12-17.md), [o3-mini](o3-mini.md), [DeepSeek R1 (Jan '25)](deepseek-r1.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
ContractBench success rate (33 ob… 28.3% ▆··▆▄●▄▂▆▆█▄ beats 8/23 very hard · avg 13% 6.3% sharp
BFCL V4 (December 2025 archive) 38.8% ▁▁▁▂▆▆●▆█▂▄▃ beats 51/114 medium · avg 36% 2.7% at its level
BFCL v3 52.7% ▁▁▁▁▁▃▅●▆▅▄▁ beats 56/171 medium · avg 55% 1.3% blunt · at its level
coding · #300 of 469 · 10% of its score
Beats [Qwen3-VL 32B Instruct](qwen3-vl-32b-instruct.md), [Solar Open 100B (102B-A12B)](solar-open-100b.md), [Qwen3-VL 30B-A3B Instruct](qwen3-vl-30b-a3b-instruct.md) here, though they rank higher overall. Trails [DeepSeek LLM 67B Chat](deepseek-llm-67b-chat.md), [WizardLM 70B V1.0](wizardlm-70b-v1-0.md), [Yi 1.5 9B Chat](yi-1-5-9b-chat.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
LiveCodeBench v6 (window unspecif… 42.4% ▂▃▄●▅██▆▄▇▄▆ beats 10/67 hard · avg 33% 8.3% at its level
MultiPL-E 71.5% ▃··▄·▅▅▇●█▃▄ beats 19/31 medium · avg 54% 1.8% at its level
chat & writing · #285 of 402 · 5.5% of its score
Beats [Ministral 3 14B Reasoning 2512](ministral-3-14b-reasoning.md) here, though they rank higher overall. Trails [EXAONE 3.5 2.4B Instruct](exaone-3-5-2-4b.md), [LFM2 700M](lfm2-700m.md), [Flan-T5 XXL](flan-t5-xxl.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
Arena-Hard (version/judge unspeci… 55.1% ▃▃▂▅▃●▅█▅▅▂▃ beats 10/30 medium · avg 46% 4.1% at its level · small run
IFEval (aggregation unspecified) 67.3% ▁▂▄▂●▄▆██▅▂▁ beats 30/145 easy · avg 75% 1.3% typical
math · #205 of 431 · 9.0% of its score
Beats [MiniMax-M2.1](minimax-m2-1.md), [DeepSeek V3 (Dec '24)](deepseek-v3.md), [Qwen3 235B-A22B (Apr 2025, hybrid)](qwen3-235b-a22b.md) here, though they rank higher overall. Trails [Mixtral 8x7B v0.1 (base)](mixtral-8x7b-v0-1.md), [Olmo 3 7B Instruct](olmo-3-7b-instruct.md), [Phi-4-mini-reasoning 3.8B](phi-4-mini-reasoning.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
MATH (subset unspecified) (maker) 90.4% ▂▄▄▃█▇▅▆●▃▄▁ beats 66/80 easy · avg 84% 9.0% small run · several runs
other tests (9)
test result vs the others in its run difficulty counts why
Medmarks-V mean win rate (19 veri… 0.4760 ▁▄▃▃▇●█▃▅▁▁▁ beats 13/30 avg 0.418 19% sharp
GPQA Diamond 58.6% ▁▁▁▁▂▄▅●███▄ beats 109/322 hard · avg 46% 16% sharp · at its level
Medmarks-V weighted mean win rate… 47.6% ▂▃▅▂▆●█▅█▅▃▆ beats 12/34 medium · avg 41% 8.6% blunt · at its level
MMLU-Redux 85.9% ▁▁▃▁▂▃▄●▇▅█▄ beats 21/63 easy · avg 78% 6.3% typical
EuroEval Life in the UK (English … 92.7% ▂·▂▂·▂·▃▃·●▃ beats 8/14 easy · avg 80% 6.1% typical
EuroEval HellaSwag (English), val… 79.3% ▂··▂▂·▂▆▆●·▄ beats 10/15 medium · avg 63% 4.0% at its level
Next Steps data preparation (bala… 52.6% ●·▄···█·▄▄·▄ beats 0/6 medium · avg 52% 3.3% at its level
GSM-Plus 86.6% ▁··▁▁▁·▃▁●█▁ beats 9/16 easy · avg 82% 1.3% blunt
Vectara hallucination leaderboard… 80.6% ▁●▁▂▅█▂▃▄▃▁▁ beats 5/98 very easy · avg 90% 0.0% blunt
source results share of the score
JetBrains/Mellum2-12B-A2.5B-Instruct mo… 8 39% ▇▇▇▇▇
Medmarks: A Comprehensive Open-Source L… 2 28% ▇▇▇
EuroEval leaderboards 2 10% ▇
mistralai/Ministral-3-14B-Instruct-2512… 2 9% ▇
ContractBench: Can LLM Agents Preserve … 1 6% ▇
mistralai/Ministral-3-8B-Instruct-2512 … 1 5% ▇
Agentic coding without the cloud: evalu… 1 3% ▇
Vectara hallucination leaderboard 1 0% ▇
prior: pull toward the middle 0% ▇
weighted average = 55.3
Licences: JetBrains/Mellum2-12B-A2.5B-Instruct model card ([Apache-2.0](https://huggingface.co/JetBrains/Mellum2-12B-A2.5B-Instruct/blob/273d9c1c6882ac8e1562e5b7e043857d35a787b6/README.md)) · Medmarks: A Comprehensive Open-Source LLM Benchmark Suite for Medical Tasks ([CC BY 4.0](../../licenses/paper-2605.01417v1-CC-BY-4.0.txt)) · EuroEval leaderboards ([MIT](https://github.com/EuroEval/leaderboards/blob/1a77f1d2f8ba1ba05ab73287fb090d76bf41b2e0/LICENSE)) · mistralai/Ministral-3-14B-Instruct-2512 model card ([Apache-2.0](../../licenses/cards-gap-NOTICE.txt)) · ContractBench: Can LLM Agents Preserve Observation Contracts? ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · mistralai/Ministral-3-8B-Instruct-2512 model card ([Apache-2.0](../../licenses/cards-gap-NOTICE.txt)) · Agentic coding without the cloud: evaluating open-weight LLMs on longitudinal data preparation tasks ([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))
## How to run it
### Through an API
api (Mistral) · $0.20/M blended · $0.2 in / $0.2 out per 1M tokens · [docs.mistral.ai](https://docs.mistral.ai/models/ministral-3-14b-25-12)
[price source](https://github.com/BerriAI/litellm/blob/c19ce71bcdad234ca87a7af10cee45375ce811a8/model_prices_and_context_window.json): USD per 1M text tokens. Mistral standard synchronous API; model ministral-14b-2512. Uncached input; output includes generated reasoning where billed. Batch, cache storage, tools and other modalities excluded. · [context source](https://huggingface.co/mistralai/Ministral-3-14B-Instruct-2512/blob/29439f81c2be264d8d393273f99e7db9c0961120/config.json): Official Instruct-2512 model configuration; card corroborates 256K. For 3B this replaces a missing value using the first-party record, not LiteLLM’s conflicting 131,072 entry.
### 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 Ministral 3 14B (Q4_K_M, needs ~8.2 GB, from [huggingface.co/mistralai](https://huggingface.co/mistralai/Ministral-3-14B-Instruct-2512-GGUF/tree/74fac473c43357d7fb2671713608183cc72496d0))
```sh
llama cli -hf mistralai/Ministral-3-14B-Instruct-2512-GGUF:Q4_K_M
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf mistralai/Ministral-3-14B-Instruct-2512-GGUF:Q4_K_M
```
Other versions: Q8_0 (~14.4 GB) `llama cli -hf mistralai/Ministral-3-14B-Instruct-2512-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 Ministral 3 14B (Q4_K_M, needs ~8.2 GB, from [huggingface.co/mistralai](https://huggingface.co/mistralai/Ministral-3-14B-Instruct-2512-GGUF/tree/74fac473c43357d7fb2671713608183cc72496d0))
```sh
lms get https://huggingface.co/mistralai/Ministral-3-14B-Instruct-2512-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 (~14.4 GB) `lms get https://huggingface.co/mistralai/Ministral-3-14B-Instruct-2512-GGUF@Q8_0 -y` · mxfp4 (~7.2 GB, Mac only) `lms get https://huggingface.co/mlx-community/mistralai_Ministral-3-14B-Instruct-2512-MLX-MXFP4 -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 Ministral 3 14B (Q4_K_M, needs ~8.84 GB, from [ollama.com](https://ollama.com/library/ministral-3/tags))
```sh
ollama run ministral-3:14b
```
Other versions: Q8_0 (~17.18 GB) `ollama run ministral-3:14b-instruct-2512-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 Ministral 3 14B (mxfp4, needs ~7.2 GB, Mac only, from [huggingface.co/mlx-community](https://huggingface.co/mlx-community/mistralai_Ministral-3-14B-Instruct-2512-MLX-MXFP4/tree/df992b14e4490b0b9eb496015f402b9449e44e08))
```sh
mlx_lm.chat --model mlx-community/mistralai_Ministral-3-14B-Instruct-2512-MLX-MXFP4
```
3. Or start an OpenAI-compatible server at http://localhost:8080/v1
```sh
mlx_lm.server --model mlx-community/mistralai_Ministral-3-14B-Instruct-2512-MLX-MXFP4
```
Model repository and documentation: [huggingface.co/mistralai](https://huggingface.co/mistralai/Ministral-3-14B-Instruct-2512)
## 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
Apple M5 Max (40-core GPU),… q4_k_m 46.23 tok/s ollama (version not recorded) · l…
Each speed links to its source, which has the full settings.
## Quantization evidence
How much of the full-precision result a quantized version kept, measured within one study. Rows from the same study share a table and aren't independent. They don't describe every download of that precision.
precision test full → quantized kept
FP8 W8A8 dynamic (weights per… GPQA Diamond (lighteval, vLLM, te… 0.586 → 0.662 112.9%
FP8 W8A8 dynamic (weights per… Reasoning average of 3 tasks (AIM… 0.613 → 0.619 100.9%
FP8 W8A8 dynamic (weights per… IFEval 0-shot (lm-evaluation-harn… 0.773 → 0.769 99.4%
FP8 W8A8 dynamic (weights per… AIME 2025 (lighteval, vLLM, tempe… 0.367 → 0.3 81.8%
NVFP4 (FP4 weights and activa… GPQA Diamond (lighteval, vLLM, te… 0.586 → 0.609 104.0%
NVFP4 (FP4 weights and activa… Reasoning average of 3 tasks (AIM… 0.613 → 0.598 97.5%
NVFP4 (FP4 weights and activa… AIME 2025 (lighteval, vLLM, tempe… 0.367 → 0.325 88.6%
NVFP4 (FP4 weights and activa… IFEval 0-shot (lm-evaluation-harn… 0.773 → 0.635 82.2%
Each kept value links to its source.
[interactive version](https://whatmodel.sh/model/ministral-3-14b) · [JSON](https://whatmodel.sh/api?format=json&all=1)