Key Features
Ministral 3 3B consists of two main architectural components:
- 3.4B Language Model
- 0.4B Vision Encoder
The Ministral 3 3B Instruct model offers the following capabilities:
- Vision: Enables the model to analyze images and provide insights based on visual content, in addition to text.
- Multilingual: Supports dozens of languages, including English, French, Spanish, German, Italian, Portuguese, Dutch, Chinese, Japanese, Korean, Arabic.
- System Prompt: Maintains strong adherence and support for system prompts.
- Agentic: Offers best-in-class agentic capabilities with native function calling and JSON outputting.
- Edge-Optimized: Delivers best-in-class performance at a small scale, deployable anywhere.
- Apache 2.0 License: Open-source license allowing usage and modification for both commercial and non-commercial purposes.
- Large Context Window: Supports a 256k context window.
Use Cases
Ideal for lightweight, real-time applications on edge or low-resource devices, such as:
- Image captioning
- Text classification
- Real-time efficient translation
- Data extraction
- Short content generation
- Fine-tuning and specialization
- And more...
Bringing advanced AI capabilities to edge and distributed environments for embedded systems.
Ministral 3 Family
Table with columns: Model Name, Type, Precision, Link| Model Name | Type | Precision | Link |
|---|
| Ministral 3 3B Base 2512 | Base pre-trained | BF16 | Hugging Face |
| Ministral 3 3B Instruct 2512 | Instruct post-trained | FP8 | Hugging Face |
| Ministral 3 3B Reasoning 2512 | Reasoning capable | BF16 | |
Other formats available here.
Benchmark Results
We compare Ministral 3 to similar sized models.
Reasoning
Table with columns: Model, AIME25, AIME24, GPQA Diamond, LiveCodeBench| Model | AIME25 | AIME24 | GPQA Diamond | LiveCodeBench |
|---|
| Ministral 3 14B | 0.850 | 0.898 | 0.712 | 0.646 |
| Qwen3-14B (Thinking) | 0.737 | 0.837 | 0.663 | 0.593 |
| | | | |
|
Instruct
Table with columns: Model, Arena Hard, WildBench, MATH Maj@1, MM MTBench| Model | Arena Hard | WildBench | MATH Maj@1 | MM MTBench |
|---|
| Ministral 3 14B | 0.551 | 68.5 | 0.904 | 8.49 |
| Qwen3 14B (Non-Thinking) | 0.427 | 65.1 | 0.870 | NOT MULTIMODAL |
| Gemma3-12B-Instruct | 0.436 | 63.2 | 0.854 | 6.70 |
Base
Table with columns: Model, Multilingual MMLU, MATH CoT 2-Shot, AGIEval 5-shot, MMLU Redux 5-shot, MMLU 5-shot, TriviaQA 5-shot| Model | Multilingual MMLU | MATH CoT 2-Shot | AGIEval 5-shot | MMLU Redux 5-shot | MMLU 5-shot | TriviaQA 5-shot |
|---|
| Ministral 3 14B | 0.742 | 0.676 | 0.648 | 0.820 | 0.794 | 0.749 |
| Qwen3 14B Base | 0.754 | 0.620 | 0.661 | 0.837 | 0.804 |
Usage
The model can be used with the following frameworks;
vLLM
We recommend using this model with vLLM.
Installation
Make sure to install vLLM >= 0.12.0:
pip install vllm --upgrade
Doing so should automatically install mistral_common >= 1.8.6.
To check:
python -c "import mistral_common; print(mistral_common.__version__)"
You can also make use of a ready-to-go docker image or on the docker hub.
Serve
Due to their size and the FP8 format of their weights Ministral-3-3B-Instruct-2512, Ministral-3-8B-Instruct-2512 and Ministral-3-14B-Instruct-2512 can run on a single 1xH200 GPU.
A simple launch command is:
vllm serve mistralai/Ministral-3-3B-Instruct-2512 \
--enable-auto-tool-choice --tool-call-parser mistral
Key parameter notes:
- enable-auto-tool-choice: Required when enabling tool usage.
- tool-call-parser mistral: Required when enabling tool usage.
Additional flags:
- You can set
--max-model-len to preserve memory. By default it is set to 262144 which is quite large but not necessary for most scenarios.
- You can set
--max-num-batched-tokens to balance throughput and latency, higher means higher throughput but higher latency.
Usage of the model
Here we asumme that the model mistralai/Ministral-3-3B-Instruct-2512 is served and you can ping it to the domain localhost with the port 8000 which is the default for vLLM.
Let's see if the Ministral 3 knows when to pick a fight !
from datetime import datetime, timedelta
from openai import OpenAI
from huggingface_hub import hf_hub_download
openai_api_key = "EMPTY"
openai_api_base = "http://localhost:8000/v1"
TEMP = 0.15
MAX_TOK = 262144
client = OpenAI(
api_key=openai_api_key,
base_url=openai_api_base,
)
models = client.models.list()
model = models.data[0].id
def load_system_prompt(repo_id: str, filename: str) -> str:
file_path = hf_hub_download(repo_id=repo_id, filename=filename)
with open(file_path, "r") as file:
system_prompt = file.read()
today = datetime.today().strftime("%Y-%m-%d")
yesterday = (datetime.today() - timedelta(days=1)).strftime("%Y-%m-%d")
model_name = repo_id.split("/")[-1]
return system_prompt.format(name=model_name, today=today, yesterday=yesterday)
SYSTEM_PROMPT = load_system_prompt(model, "SYSTEM_PROMPT.txt")
image_url = "https://static.wikia.nocookie.net/essentialsdocs/images/7/70/Battle.png/revision/latest?cb=20220523172438"
messages = [
{"role": "system", "content": SYSTEM_PROMPT},
{
"role": "user",
"content": [
{
"type": "text",
"text": "What action do you think I should take in this situation? List all the possible actions and explain why you think they are good or bad.",
},
{"type": "image_url", "image_url": {"url": image_url}},
],
},
]
print(messages)
response = client.chat.completions.create(
model=model,
messages=messages,
temperature=TEMP,
max_tokens=MAX_TOK,
)
print(response.choices[0].message.content)
Let's solve some equations thanks to our simple Python calculator tool.
import json
from openai import OpenAI
from huggingface_hub import hf_hub_download
openai_api_key = "EMPTY"
openai_api_base = "http://localhost:8000/v1"
TEMP = 0.15
MAX_TOK = 262144
client = OpenAI(
api_key=openai_api_key,
base_url=openai_api_base,
)
models = client.models.list()
model = models.data[0].id
def load_system_prompt(repo_id: str, filename: str) -> str:
file_path = hf_hub_download(repo_id=repo_id, filename=filename)
with open(file_path, "r") as file:
system_prompt = file.read()
return system_prompt
SYSTEM_PROMPT = load_system_prompt(model, "SYSTEM_PROMPT.txt")
image_url = "https://math-coaching.com/img/fiche/46/expressions-mathematiques.jpg"
def my_calculator(expression: str) -> str:
return str(eval(expression))
tools = [
{
"type": "function",
"function": {
"name": "my_calculator",
"description": "A calculator that can evaluate a mathematical expression.",
"parameters": {
"type": "object",
"properties": {
"expression": {
"type": "string",
"description": "The mathematical expression to evaluate.",
},
},
"required": ["expression"],
},
},
},
{
"type": "function",
"function": {
"name": "rewrite",
"description": "Rewrite a given text for improved clarity",
"parameters": {
"type": "object",
"properties": {
"text": {
"type": "string",
"description": "The input text to rewrite",
}
},
},
},
},
]
messages = [
{"role": "system", "content": SYSTEM_PROMPT},
{
"role": "user",
"content": [
{
"type": "text",
"text": "Thanks to your calculator, compute the results for the equations that involve numbers displayed in the image.",
},
{
"type": "image_url",
"image_url": {
"url": image_url,
},
},
],
},
]
response = client.chat.completions.create(
model=model,
messages=messages,
temperature=TEMP,
max_tokens=MAX_TOK,
tools=tools,
tool_choice="auto",
)
tool_calls = response.choices[0].message.tool_calls
results = []
for tool_call in tool_calls:
function_name = tool_call.function.name
function_args = tool_call.function.arguments
if function_name == "my_calculator":
result = my_calculator(**json.loads(function_args))
results.append(result)
messages.append({"role": "assistant", "tool_calls": tool_calls})
for tool_call, result in zip(tool_calls, results):
messages.append(
{
"role": "tool",
"tool_call_id": tool_call.id,
"name": tool_call.function.name,
"content": result,
}
)
response = client.chat.completions.create(
model=model,
messages=messages,
temperature=TEMP,
max_tokens=MAX_TOK,
)
print(response.choices[0].message.content)
Ministral 3 can follow your instructions to the letter.
from openai import OpenAI
from huggingface_hub import hf_hub_download
openai_api_key = "EMPTY"
openai_api_base = "http://localhost:8000/v1"
TEMP = 0.15
MAX_TOK = 262144
client = OpenAI(
api_key=openai_api_key,
base_url=openai_api_base,
)
models = client.models.list()
model = models.data[0].id
def load_system_prompt(repo_id: str, filename: str) -> str:
file_path = hf_hub_download(repo_id=repo_id, filename=filename)
with open(file_path, "r") as file:
system_prompt = file.read()
return system_prompt
SYSTEM_PROMPT = load_system_prompt(model, "SYSTEM_PROMPT.txt")
messages = [
{"role": "system", "content": SYSTEM_PROMPT},
{
"role": "user",
"content": "Write me a sentence where every word starts with the next letter in the alphabet - start with 'a' and end with 'z'.",
},
]
response = client.chat.completions.create(
model=model,
messages=messages,
temperature=TEMP,
max_tokens=MAX_TOK,
)
assistant_message = response.choices[0].message.content
print(assistant_message)
You can also use Ministral 3 3B Instruct 2512 with Transformers !
Transformers very recently added prelimenary support for FP8, so please make sure to install from main:
uv pip install git+https://github.com/huggingface/transformers
To make the best use of our model with Transformers make sure to have installed mistral-common >= 1.8.6 to use our tokenizer.
pip install mistral-common --upgrade
Try it out by running the following snippet.
[!Tip]
By default Transformers will load the checkpoint in FP8 and dequantize it to BF16 on the fly,
which means the model currently does not make use of accelerated FP8-kernels.
Compatibility with accelerated FP8-kernels is currently worked on and will be available in a couple of weeks.
Stay tuned!
import torch
from transformers import Mistral3ForConditionalGeneration, MistralCommonBackend
model_id = "mistralai/Ministral-3-3B-Instruct-2512"
tokenizer = MistralCommonBackend.from_pretrained(model_id)
model = Mistral3ForConditionalGeneration.from_pretrained(model_id, device_map="auto")
image_url = "https://static.wikia.nocookie.net/essentialsdocs/images/7/70/Battle.png/revision/latest?cb=20220523172438"
messages = [
{
"role": "user",
"content": [
{
"type": "text",
"text": "What action do you think I should take in this situation? List all the possible actions and explain why you think they are good or bad.",
},
{"type": "image_url", "image_url": {"url": image_url}},
],
},
]
tokenized = tokenizer.apply_chat_template(messages, return_tensors="pt", return_dict=True)
tokenized["input_ids"] = tokenized["input_ids"].to(device="cuda")
tokenized["pixel_values"] = tokenized["pixel_values"].to(dtype=torch.bfloat16, device="cuda")
image_sizes = [tokenized["pixel_values"].shape[-2:]]
output = model.generate(
**tokenized,
image_sizes=image_sizes,
max_new_tokens=512,
)[0]
decoded_output = tokenizer.decode(output[len(tokenized["input_ids"][0]):])
print(decoded_output)
Note:
Transformers allows you to automatically convert the checkpoint to Bfloat16. To so simple load the model as follows:
from transformers import Mistral3ForConditionalGeneration, FineGrainedFP8Config
model_id = "mistralai/Ministral-3-3B-Instruct-2512"
model = Mistral3ForConditionalGeneration.from_pretrained(
model_id,
device_map="auto",
quantization_config=FineGrainedFP8Config(dequantize=True)
)
License
This model is licensed under the Apache 2.0 License.
You must not use this model in a manner that infringes, misappropriates, or otherwise violates any third party’s rights, including intellectual property rights.
