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Cataloged from mistralai/Mistral-Small-4-119B-2603
All models by mistralai →Mistral Small 4 is a powerful hybrid model capable of acting as both a general instruction model and a reasoning model. It unifies the capabilities of three different model families—Instruct, Reasoning (previously called Magistral), and Devstral—into a single, unified model.
With its multimodal capabilities, efficient architecture, and flexible mode switching, it is a powerful general-purpose model for any task. In a latency-optimized setup, Mistral Small 4 achieves a 40% reduction in end-to-end completion time, and in a throughput-optimized setup, it handles 3x more requests per second compared to Mistral Small 3.
To further improve efficiency you can either take advantages of:
mistralai/Mistral-Small-4-119B-2603-eagle.mistralai/Mistral-Small-4-119B-2603-NVFP4.Mistral Small 4 includes the following architectural choices:
Mistral Small 4 offers the following capabilities:
'none' → Do not use reasoning'high' → Use reasoning (recommended for complex prompts)
Use reasoning_effort="high" for complex tasksreasoning_effort="high". Temp between 0.0 and 0.7 for reasoning_effort="none" depending on task.Mistral Small 4 is designed for general chat assistants, coding, agentic tasks, and reasoning tasks (with reasoning mode toggled). Its multimodal capabilities also enable document and image understanding for data extraction and analysis.
Its capabilities are ideal for:
Mistral Small 4 is also well-suited for customization and fine-tuning for more specialized tasks.
Depending on your tasks you can trigger reasoning thanks to the support of the per-request parameter reasoning_effort. Set it to:
reasoning_effort="none": Fast, lightweight responses for everyday tasks, equivalent to the same chat style of mistralai/Mistral-Small-3.2-24B-Instruct-2506.reasoning_effort="high": Deep, step-by-step reasoning for complex problems, with equivalent verbosity to previous Magistral models such as mistralai/Magistral-Small-2509.
Mistral Small 4 with reasoning achieves competitive scores, matching or surpassing GPT-OSS 120B across all three benchmarks while generating significantly shorter outputs. On AA LCR, Mistral Small 4 scores 0.72 with just 1.6K characters, whereas Qwen models require 3.5-4x more output (5.8-6.1K) for comparable performance. On LiveCodeBench, Mistral Small 4 outperforms GPT-OSS 120B while producing 20% less output. This efficiency reduces latency, inference costs, and improves user experience.
You can find Mistral Small 4 support on multiple libraries for inference and fine-tuning. We here thank every contributors and maintainers that helped us making it happen.
The model can be deployed with:
vllm (recommended): See herellama.cpp: See here for Unsloth's GGUFsLM studio: See hereSGLang: See heretransformers: See hereFor optimal performance, we recommend using the Mistral AI API if local serving is subpar.
Fine-tune the model via:
We recommend using Mistral Small 4 with the vLLM library for production-ready inference.
Install latest vllm:
Doing so should automatically install mistral_common >= 1.11.0.
To check:
You can also make use of a ready-to-go docker image or on the docker hub.
We recommend a server/client setup:
Mistral Small 4 can follow your instructions to the letter.
Let's solve some equations thanks to our simple Python calculator tool.
Let's see if the Mistral Small 4 knows when to pick a fight !
You need to install the main branch of Transformers to use Mistral Small 4:
This model is licensed under the Apache 2.0 License.
You must not use this model in a manner that infringes, misappropriates, or violates any third party’s rights, including intellectual property rights.
uv pip install -U vllmpython -c "import mistral_common; print(mistral_common.__version__)"vllm serve mistralai/Mistral-Small-4-119B-2603 --max-model-len 262144 --tensor-parallel-size 2 --attention-backend FLASH_ATTN_MLA \ --tool-call-parser mistral --enable-auto-tool-choice --reasoning-parser mistral --max_num_batched_tokens 16384 --max_num_seqs 128 \ --gpu_memory_utilization 0.8from datetime import datetime, timedelta
from openai import OpenAIfrom huggingface_hub import hf_hub_download
# Modify OpenAI's API key and API base to use vLLM's API server.openai_api_key = "EMPTY"openai_api_base = "http://localhost:8000/v1"
TEMP = 0.1# use TEMP = 0.7 for reasoning="high"
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")
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, reasoning_effort="none",)
assistant_message = response.choices[0].message.contentprint(assistant_message)import jsonfrom datetime import datetime, timedelta
from openai import OpenAIfrom huggingface_hub import hf_hub_download
# Modify OpenAI's API key and API base to use vLLM's API server.openai_api_key = "EMPTY"openai_api_base = "http://localhost:8000/v1"
TEMP = 0.1
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://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, tools=tools, tool_choice="auto", reasoning_effort="none",)
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, reasoning_effort="none",)
print(response.choices[0].message.content)from datetime import datetime, timedelta
from openai import OpenAIfrom huggingface_hub import hf_hub_download
# Modify OpenAI's API key and API base to use vLLM's API server.openai_api_key = "EMPTY"openai_api_base = "http://localhost:8000/v1"
TEMP = 0.7
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}}, ], },]
response = client.chat.completions.create( model=model, messages=messages, temperature=TEMP, reasoning_effort="high",)
print(response.choices[0].message.content)uv pip install git+https://github.com/huggingface/transformers.gitimport torchfrom transformers import AutoProcessor, Mistral3ForConditionalGeneration
model_id = "mistralai/Mistral-Small-4-119B-2603"
processor = AutoProcessor.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}}, ], },]
inputs = processor.apply_chat_template(messages, return_tensors="pt", tokenize=True, return_dict=True, reasoning_effort="high")inputs = inputs.to(model.device)
output = model.generate( **inputs, max_new_tokens=1024, do_sample=True, temperature=0.7,)[0]
# Setting `skip_special_tokens=False` to visualize reasoning trace between [THINK] [/THINK] tags.decoded_output = processor.decode(output[len(inputs["input_ids"][0]):], skip_special_tokens=False) print(decoded_output)