Mistral Le Chat context window: token limits, memory policy, and 2025 rules

Mistral models support large windows, but actual limits depend on routing.

Le Chat, the AI assistant built by Mistral, is powered by various internal models depending on the task—reasoning, code, image analysis, or general Q&A. Each model has its own token window, determining how much content (input + output) it can process in a single run. While models like Mistral Medium or Pixtral support up to 128,000 tokens, other models such as Codestral and Magistral use different limits for programming or reasoning tasks. The assistant routes users dynamically based on intent, meaning the context window size may vary depending on the feature used.

Most Mistral models support up to 128K tokens in a single turn.

The main general-purpose models—Mistral Medium 3, Mistral Large 2411, and the multimodal Pixtral Large—all support 128,000 tokens as their context window. This total includes both the input prompt and the model’s output. Older versions of Mistral Small were limited to 32K tokens, but newer iterations have been aligned to the full 128K ceiling.For code-specific tasks, Codestral expands the limit to 256,000 tokens, ideal for large codebases or structured documents. Conversely, Magistral, used in the “Think” reasoning mode, operates with a more compact 40,000-token window, optimized for step-by-step logic.

Input and output are both counted toward the total context window.

In Mistral’s architecture, every token is counted—whether it's part of the user’s prompt, system instructions, past history (if included), or the assistant’s reply. The guiding rule is simple:prompt_tokens + max_tokens ≤ context_length.For example, with a 128K model, if your prompt is 100,000 tokens long, you can allocate at most 28,000 tokens for the reply. Exceeding this hard limit causes the model to reject the prompt or truncate the output.

Token size varies by language and structure.

Mistral uses a custom tokenizer (tekken v3) with high compression efficiency. The actual number of tokens generated depends on the language, symbols, and formatting used. As a general guideline:

• 1,000 tokens ≈ 700–800 English words

• Compressed code or structured data (e.g., JSON) may produce more tokens per character

  
  

  The platform offers usage metrics for every API call, breaking down prompt_tokens, completion_tokens, and total_tokens. Users should inspect these regularly when working near the window limits.

Le Chat is stateless, but opt-in memory is available.

By default, Le Chat does not retain information across sessions. Each chat is session-specific, and there is no long-term memory unless users enable the Memories feature. When active, Memories allow the assistant to remember names, preferences, or recurring topics.However, this memory is not structural—it does not expand the context window or modify the token budget. Instead, selected data is injected into the prompt at runtime, consuming part of the allowed 128K tokens.

Reasoning and Deep Research consume more tokens internally.

Le Chat includes Think Mode (backed by Magistral) and Deep Research, which use internal steps, chaining, and web retrievals. These features generate additional token traffic internally, which may impact the available space for your instructions and outputs.

In Deep Research, for example, a user’s single question may trigger a cascade of searches and summarizations, each contributing to the token total for that interaction. For better performance, use short prompts and clear objectives in these modes.

Uploading documents or using Libraries triggers RAG workflows.

Users can upload files directly or create Libraries that support up to 100 files, with each file maxing out at 100MB. Supported formats include PDF, DOCX, TXT, CSV, and many types of code files. During ingestion, files are tokenized, and their embeddings are stored for retrieval.

While the RAG system works outside of the strict 128K prompt window, any retrieved content is inserted into the model's prompt dynamically, and therefore does consume tokens. This affects how much space remains for user input and final output.

Vision features use Pixtral and have standard limits.

When Le Chat receives an image as input, it uses the Pixtral Large multimodal model (128K context). Images are transformed into structured embeddings and count toward the prompt token limit. Complex images or multi-image threads can consume thousands of tokens, especially when combined with long user prompts.

To avoid overflows, users are encouraged to reduce resolution, limit the number of images per message, and avoid embedding long textual captions alongside.

Usage caps vary by plan, but context windows do not.

The context window size is not influenced by pricing plan—128K applies to free and paid users alike. However, usage caps such as number of messages, daily file uploads, or access to advanced features like web search and Deep Research do vary:

• Free: capped messages and limited tools

• Pro/Student: unlimited messages and extended limits

• Team/Enterprise: full access, custom integrations, and private deployment

  
  

  These tiers affect how often you can interact with Le Chat, but not how many tokens are allowed per single prompt.

Privacy settings impact retention, not prompt size.

For Free, Pro, and Student plans, input and output data may be used for training by default. Users can opt out from the settings menu. For Team and Enterprise, data is excluded from training by design.

Regardless of training status, uploaded documents, Gmail/Drive/SharePoint connectors, and chat history are stored unless deleted manually. No zero data retention mode exists for Le Chat at the moment.

Token management tips for working with Mistral models.

• Choose the right model: use Codestral for large documents, Pixtral for images, and Magistral for deep logic.

• Leave buffer space: reserve at least 10–20% of your token window for the reply.

• RAG is not free: retrieved document content counts toward your token budget.

• Use Libraries for multi-doc retrieval, but remember embedded images are not yet supported.

• Think mode adds tokens: avoid verbose prompts when using step-by-step reasoning features.

High context capacity makes Mistral models flexible, but planning is key.

Mistral's 128K and 256K token models offer some of the largest windows available in mainstream chat assistants. Still, without proper token budgeting—especially in stateless sessions with document uploads, tool calls, and deep reasoning—you may hit the ceiling and lose coherence.

Efficient input formatting, strategic use of Memories, and awareness of internal model routing help ensure Le Chat operates within its limits, delivering answers that are complete, relevant, and contextually accurate.

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