huggingface--transformers/docs/source/en/model_doc/mistral4.md

This model was contributed to Hugging Face Transformers on 2026-03-16.

Mistral4

Overview

Mistral 4 is a powerful hybrid model with the capability of acting as both a general instruction model and a reasoning model. It unifies the capabilities of three different model families - Instruct, Reasoning ( previous called Magistral ), and Devstral - into a single, unified model.

Mistral-Small-4 consists of the following architectural choices:

• MoE: 128 experts and 4 active.
• 119B with 6.5B activated parameters per token.
• 256k Context Length.
• Multimodal Input: Accepts both text and image input, with text output.
• Instruct and Reasoning functionalities with Function Calls
  • Reasoning Effort configurable by request.

Mistral 4 offers the following capabilities:

• Reasoning Mode: Switch between a fast instant reply mode, and a reasoning thinking mode, boosting performance with test time compute when requested.
• 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.
• Speed-Optimized: Delivers best-in-class performance and speed.
• 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.

Usage examples

from 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").to(model.device)
inputs = inputs.to(model.device)

output = model.generate(
    **inputs,
    max_new_tokens=512,
)[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)

Mistral4Config

autodoc Mistral4Config

Mistral4PreTrainedModel

autodoc Mistral4PreTrainedModel - forward

Mistral4Model

autodoc Mistral4Model - forward

Mistral4ForCausalLM

autodoc Mistral4ForCausalLM

Mistral4ForSequenceClassification

autodoc Mistral4ForSequenceClassification

Mistral4ForTokenClassification

autodoc Mistral4ForTokenClassification