Mistral Medium 3.5

Mistral Medium 3.5は、LLM models関連タスクを最適化するために設計された最先端のAIツールです。

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Mistral Medium 3.5とは？

Mistral Medium 3.5はLLM models分野に特化した高度なAIソリューションです。プロフェッショナルやクリエイターの作業プロセスを自動化し、生産性を劇的に向上させます。

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主な特徴とメリット

✓

Dense 128B Parameters

The model is built with 128 billion parameters, contributing to its robust performance across various complex tasks.

✓

256k Context Length

Supports an exceptionally large context window of 256,000 tokens, enabling the model to process and understand very long inputs and maintain coherence over extended conversations or documents.

✓

Multimodal Input (Text & Image)

The model accepts both text and image inputs, allowing it to analyze visual content and provide insights in addition to processing textual information, with text as the output format.

✓

Configurable Reasoning Effort

Users can toggle between a fast instant reply mode and a dedicated reasoning mode, boosting performance with test-time compute when requested for complex prompts and agentic usage.

✓

Agentic Capabilities with Function Calling

Offers best-in-class agentic capabilities, including native function calling and the ability to produce JSON output, facilitating complex automated workflows.

✓

Multilingual Support

The model supports dozens of languages, including major global languages like English, French, Spanish, German, Italian, Portuguese, Dutch, Chinese, Japanese, Korean, and Arabic.

✓

Strong System Prompt Adherence

Demonstrates strong adherence and support for system prompts, ensuring that instructions and constraints provided at the system level are followed reliably.

Mistral Medium 3.5の料金

料金モデルNot Disclosed

開始価格—

無料プラン—

無料トライアル—

請求—

詳細な料金情報

Pricing information for Mistral Medium 3.5 is not available in the provided official context. The Hugging Face pricing page provided is generic and does not list specific pricing for this model.

長所と短所: Mistral Medium 3.5

長所

Achieves strong performance across instruction following, reasoning, and coding tasks, unifying these capabilities in one model.
Features an exceptionally large 256k context window, allowing for processing and understanding of very long inputs.
Supports multimodal input, capable of analyzing images and providing insights based on visual content in addition to text.
Offers configurable reasoning effort, allowing users to optimize between fast responses and deeper reasoning for complex tasks.
Provides best-in-class agentic capabilities with native function calling and JSON output, enhancing automation and complex workflows.
Supports dozens of languages, including major global languages, making it suitable for diverse multilingual applications.

短所

Local inference performance might be subpar, with optimal performance often achieved via the Mistral AI API.
Requires updated Transformers configurations and GGUF files to avoid performance degradation, especially in long-context sessions.
The Modified MIT License includes exceptions for companies with large revenue, which may restrict commercial use for certain organizations.

よくある質問

What is Mistral Medium 3.5?

Mistral Medium 3.5 is Mistral AI's first flagship merged model. It is a dense 128B large language model featuring a 256k context window, designed to handle instruction-following, reasoning, and coding tasks, and supports multimodal input (text and image).

What are the key capabilities of Mistral Medium 3.5?

Its key capabilities include configurable reasoning effort, vision analysis for images, multilingual support across dozens of languages, strong adherence to system prompts, best-in-class agentic capabilities with native function calling and JSON output, and a large 256k context window.

How does Mistral Medium 3.5 compare to previous Mistral models?

Mistral Medium 3.5 replaces its predecessors, Mistral Medium 3.1 and Magistral in Le Chat, and Devstral 2 in the Vibe coding agent. It offers better performance for instruct, reasoning, and coding tasks in a new unified model compared to previously released models.

Can Mistral Medium 3.5 process images?

Yes, Mistral Medium 3.5 accepts both text and image input, producing text output. Its vision encoder was trained from scratch to handle variable image sizes and aspect ratios, allowing it to analyze images and provide insights based on visual content.

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