What is the context window of Le Chat (Mistral)?
- Graziano Stefanelli
- Aug 30
- 3 min read
Le Chat’s capabilities now depend on a redesigned model lineup.
Le Chat, Mistral’s conversational platform, has evolved into a unified interface that dynamically selects the most suitable model depending on the task. Recent updates have standardized context handling across nearly all core models, allowing for deeper, longer, and more complex interactions without repeated resets or fragmented prompts.
Earlier releases were limited to 32,000 tokens, which often forced users to break large conversations or documents into smaller parts. With the rollout of the Mistral Large 2.1 model and the integration of Pixtral, Codestral, and Magistral into Le Chat, the maximum window has expanded significantly. Today, the majority of available models support 128,000 tokens, with certain specialized options capable of reaching 256,000 tokens when required.
This shift marks a change in how the platform handles memory, large documents, multimodal content, and code-heavy workloads, making Le Chat more competitive against other top-tier AI assistants.
Mistral Large now delivers 128K tokens by default.
The Mistral Large 2.1 model, now serving as the primary engine behind Le Chat, has extended its context capacity from the previous 32K limit to a new 128K window. This allows for seamless analysis of longer documents, transcripts, reports, and conversation threads without truncation or partial summaries.
For users who rely on persistent deep conversations—such as legal analysis, academic research, or financial document review—this increase reduces the need for manual segmentation. The model also handles interlinked references better, preserving relationships between earlier and later sections of the same discussion.
Pixtral adds multimodal support with the same 128K window.
Le Chat integrates Pixtral Large, a multimodal variant designed for scenarios where text and images coexist. This model supports the same 128,000-token limit, making it possible to process image-rich PDFs, visual datasets, presentations, and technical diagrams without reducing context length.
When dealing with mixed content, Pixtral reconstructs the relationship between embedded images and textual components, ensuring that captions, charts, and related references are interpreted accurately within the broader context of the file or conversation.
Codestral doubles the capacity for code-intensive workflows.
The Codestral series, accessible within Le Chat through the dedicated “Code” tool, provides the platform’s largest context window to date: 256,000 tokens. It is optimized for managing extensive codebases, technical specifications, and integrated engineering tasks.
This increased capacity allows developers to load entire repositories, search across multiple modules, refactor large functions, and generate structured documentation—all within a single continuous thread. Codestral combines reasoning, retrieval, and synthesis steps in a single pass, minimizing fragmentation when working on complex software projects.
Magistral handles reasoning with a smaller, specialized window.
While most Le Chat models now handle 128K tokens, Magistral Medium remains a specialized alternative designed for reasoning-heavy and “deep research” scenarios. Its 40K-token limit reflects a trade-off: reduced raw capacity in exchange for slower, more deliberate step-by-step planning when tackling multi-layered logic tasks.
Magistral is selected automatically when activating advanced features like structured hypothesis testing or multi-document comparative analysis, where model precision is prioritized over token span.
A detailed view of current context limits.
Most scenarios now operate at 128K, but planning matters.
For the majority of users, 128,000 tokens is the effective working window across free, pro, and team tiers. That covers entire books, large legal contracts, academic research papers, or multi-departmental reports without forcing you to split materials into separate sessions.
However, choosing the correct model for the task remains critical: Pixtral for images, Codestral for software, Magistral for reasoning depth, and Mistral Large for balanced conversational performance. By understanding which model is handling your session, you can better predict limits, avoid truncation, and structure your prompts for maximum accuracy.
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