Long Context Window
Processes up to 128,000 tokens in a single request, enabling analysis of long documents, codebases, or extended conversations without truncation.
Mistral
Mistral Large 24.02
Mistral Large 24.02 is a text generation model developed by Mistral, built around 123 billion parameters and designed to run on a single node for large-throughput inference. It features a 128,000-token context window, making it suited for long-document processing and extended conversational tasks. The model supports dozens of natural languages, including French, German, Spanish, Italian, Portuguese, Arabic, Hindi, Russian, Chinese, Japanese, and Korean. Beyond natural language, Mistral Large 24.02 supports over 80 programming languages, including Python, Java, C, C++, JavaScript, and Bash, making it applicable to code generation and analysis tasks. Its single-node inference design means it can deliver high throughput without requiring distributed infrastructure. This combination of broad language coverage, large context capacity, and coding support makes it well-suited for multilingual applications, long-context document workflows, and software development assistance.
Unknown
128,000 context
16,000 tokens output
Long Context Window
Multilingual Text Generation
Code Generation
Single-Node Inference
Instruction Following
Function Calling
Model Overview
High-signal model metadata in a structured two-column overview table.
Provider
The entity that provides this model.
Mistral
Input Context Window
The number of tokens supported by the input context window.
128,000
tokens
Maximum Output Tokens
The number of tokens that can be generated by the model in a single request.
16,000 tokens
tokens
Open Source
Whether the model's code is available for public use.
No
Release Date
When the model was first released.
Unknown
Knowledge Cut-off Date
When the model's knowledge was last updated.
Unknown
API Providers
The providers that offer this model. This is not an exhaustive list.
Mistral API, Amazon Bedrock
Modalities
Types of data this model can process.
Text
Code
What is Mistral Large 24.02
A fuller summary of positioning, capabilities, and source-specific details for Mistral Large 24.02.
Mistral Large 24.02 is a text generation model developed by Mistral, built around 123 billion parameters and designed to run on a single node for large-throughput inference. It features a 128,000-token context window, making it suited for long-document processing and extended conversational tasks. The model supports dozens of natural languages, including French, German, Spanish, Italian, Portuguese, Arabic, Hindi, Russian, Chinese, Japanese, and Korean.
Beyond natural language, Mistral Large 24.02 supports over 80 programming languages, including Python, Java, C, C++, JavaScript, and Bash, making it applicable to code generation and analysis tasks. Its single-node inference design means it can deliver high throughput without requiring distributed infrastructure. This combination of broad language coverage, large context capacity, and coding support makes it well-suited for multilingual applications, long-context document workflows, and software development assistance.
Capabilities
What Mistral Large 24.02 supports
Multilingual Text Generation
Generates and understands text in dozens of languages including French, German, Spanish, Arabic, Hindi, Chinese, Japanese, and Korean.
Code Generation
Supports code generation and comprehension across 80+ programming languages, including Python, Java, C, C++, JavaScript, and Bash.
Single-Node Inference
Runs at high throughput on a single node despite its 123 billion parameter size, reducing infrastructure complexity for deployment.
Instruction Following
Responds to detailed instructions and multi-step prompts, supporting structured task completion such as summarization, classification, and Q&A.
Function Calling
Supports function calling capabilities, allowing the model to invoke external tools or APIs based on structured prompts.
Pricing for Mistral Large 24.02
Primary API pricing shown in the same “quick compare” spirit as the reference page.
Input tokens
$4.00
Per million tokens
Output tokens
N/A
Per million tokens
Price Comparison
Additional usage-cost dimensions synced into the project for this model.
maxTemperature
1
maxResponseSize
16,000 tokens
API Access & Providers
Places where this model is available, based on the synced detail-page metadata.
Mistral API
Amazon Bedrock
Model Performance
Benchmark scores synced from the current model source and normalized into the local catalog.
| Benchmark | Score |
|---|---|
|
AIME 2024
American math olympiad problems
|
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GPQA Diamond
PhD-level science questions (biology, physics, chemistry)
|
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HLE
Questions that challenge frontier models across many domains
|
|
|
LiveCodeBench
Real-world coding tasks from recent competitions
|
|
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MATH-500
Undergraduate and competition-level math problems
|
|
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MMLU-Pro
Expert knowledge across 14 academic disciplines
|
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SciCode
Scientific research coding and numerical methods
|
Resources & Documentation
Official model cards, release notes, docs, and other references synced from the source page.
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Recent daily stories tied to Mistral Large 24.02 through direct model mentions or provider-level coverage.
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FAQ
Common questions about Mistral Large 24.02
What is the context window size for Mistral Large 24.02?
Mistral Large 24.02 has a context window of 128,000 tokens, allowing it to process long documents or extended conversations in a single request.
How many parameters does Mistral Large 24.02 have?
The model has 123 billion parameters and is designed to run on a single node for efficient large-throughput inference.
Which programming languages does Mistral Large 24.02 support?
The model supports over 80 coding languages, including Python, Java, C, C++, JavaScript, and Bash.
Which natural languages does Mistral Large 24.02 support?
It supports dozens of natural languages, including French, German, Spanish, Italian, Portuguese, Arabic, Hindi, Russian, Chinese, Japanese, and Korean, among others.
What is the knowledge cutoff date for Mistral Large 24.02?
The training date is listed as not available in the current metadata. For the most accurate information on the knowledge cutoff, refer to Mistral's official documentation.
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