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What is Mistral Medium 3.5?

Mistral Medium 3.5 emerges as a leading 128B dense model that seamlessly integrates instruction-following, logical reasoning, and coding functionalities into a cohesive system. It boasts an impressive 256K context window, which allows for customizable reasoning effort and accommodates multimodal input, demonstrating strong agentic capabilities particularly advantageous for extended tasks that require reliable multi-tool usage and organized outcomes. This groundbreaking model powers remote agents within Mistral Vibe, facilitating the execution of coding projects in the cloud, effectively tackling prolonged assignments while managing multiple sessions concurrently. Users can conveniently activate agents via the Vibe CLI or Le Chat, with the assurance that local command-line interface sessions can seamlessly transition to the cloud without any loss of history, task status, or approvals. As work progresses, users have the ability to track file modifications, tool interactions, progress updates, and inquiries. Each coding project is conducted within its own sandbox, skillfully handling module refactors, generating tests, updating dependencies, performing CI investigations, and debugging issues, which ultimately leads to the creation of a GitHub pull request for review. The introduction of this model signifies a major leap forward in the management and execution of intricate coding tasks within collaborative settings, enhancing the efficiency and effectiveness of software development processes. Furthermore, its design emphasizes user autonomy and adaptability, empowering developers with tools that cater to their specific project needs.

What is GLM-5.1?

GLM-5.1 marks the newest evolution in Z.ai’s GLM lineup, designed as a state-of-the-art AI model focused on agents, specifically for tasks involving coding, logical reasoning, and overseeing long-term processes. This version builds on the foundation set by GLM-5, which utilizes a Mixture-of-Experts (MoE) framework to maximize performance while keeping inference costs low, supporting a broader vision of making weight models available to developers. A key feature of GLM-5.1 is its ability to promote agentic behavior, enabling it to plan, execute, and enhance multi-step tasks rather than just responding to single prompts. The model is meticulously crafted to handle complex workflows, such as troubleshooting code, navigating repositories, and conducting sequential tasks, all while preserving context over extended periods. Compared to earlier models, GLM-5.1 provides improved reliability during prolonged interactions, ensuring consistency throughout longer sessions and reducing errors in multi-step reasoning tasks. Furthermore, this advancement represents a significant step forward in the realm of AI, especially in its proficiency for managing intricate task workflows with ease. With its innovative features, GLM-5.1 sets a new standard for what agent-focused AI can achieve in practical applications.

Media

Media

Integrations Supported

Linear
Sentry
Slack

Integrations Supported

APIFree
C
C#
C++
Canopy Wave
Claude Code
Claw Code
Cline
GLM Coding Plan
HTML
Hermes Agent
OpenClaw
OpenRouter
PyTorch
Wafer
Z.ai
scikit-learn

API Availability

API Availability

Has API

Pricing Information

$14.99 per month
Free Version

Pricing Information

Free
Open source
Free Version

Supported Platforms

SaaS
Android
iPhone
iPad

Supported Platforms

SaaS
Windows
Mac
On-Prem
Linux

Customer Service / Support

Web-Based Support

Customer Service / Support

Web-Based Support

Training Options

Documentation Hub

Training Options

Documentation Hub

Company Facts

Organization Name

Mistral AI

Date Founded

2023

Company Location

France

Company Website

mistral.ai/news/vibe-remote-agents-mistral-medium-3-5/

Company Facts

Organization Name

Z.ai

Date Founded

2023

Company Location

China

Company Website

z.ai/

Categories and Features

AI Models

Not specified

Categories and Features

AI Coding Models

Not specified

AI Models

Not specified

AI Reasoning Models

Not specified

Foundation Models

Not specified

Large Language Models

Not specified

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