Mistral Large 4 is a multimodal Mixture-of-Experts model from Mistral AI that accepts text and images and generates text for instruction following, reasoning, coding, and agentic workflows. It has approximately 1000B total parameters and 49B active parameters per token, or 52B including embeddings and output layers.
This model is ready for commercial or non-commercial use.
This model is not owned or developed by NVIDIA. This model has been developed and built to a third-party's requirements for this application and use case; see link to Non-NVIDIA Mistral Large 4 Model Card
GOVERNING TERMS: The trial service is governed by the NVIDIA API Trial Terms of Service. To deploy and customize the model in your environment, please contact Mistral.
Global
Use Case: Developers can use the model for instruction following, reasoning, coding, image and document analysis, structured outputs, and agentic workflows with tool calls, including applications in cybersecurity, finance, and law.
Build.NVIDIA.com: 10/29/2026 via link
References:
Architecture Type: Transformer
Network Architecture: Mixture-of-Experts
Total Parameters: ~1000B
Active Parameters: 49B
Input Types: Text, Image
Input Formats: String, Red, Green, Blue (RGB)
Input Parameters: One-Dimensional (1D), Two-Dimensional (2D)
Other Input Properties: Text and images share a combined input/output context window of up to 1M tokens. Images use a Pixtral-style vision encoder that handles variable image sizes and aspect ratios. Maximum pixel dimensions, preprocessing steps, and images-per-request limits are not specified by the vendor.
Output Types: Text
Output Format: String
Output Parameters: One-Dimensional (1D)
Other Output Properties: Combined input and output context length is 1M tokens. The vendor's max_completion_tokens setting includes reasoning tokens and visible output tokens.
Our AI models are designed and/or optimized to run on NVIDIA GPU-accelerated systems. By leveraging NVIDIA's hardware (e.g. GPU cores) and software frameworks (e.g., CUDA libraries), the model achieves faster training and inference times compared to CPU-only solutions.
Runtime Engines:
Supported Hardware:
Preferred Operating Systems: Linux
The integration of foundation and fine-tuned models into AI systems requires additional testing using use-case-specific data to ensure safe and effective deployment. Following the V-model methodology, iterative testing and validation at both unit and system levels are essential to mitigate risks, meet technical and functional requirements, and ensure compliance with safety and ethical standards before deployment.
Mistral Large 4; vendor checkpoint: mistralai/Mistral-Large-4-1T-A52B; vendor API model ID: mistral-large-4. Vendor release: 10/29/2026.
Data Modality: Text, Image
Image Training Data Size: Undisclosed
Text Training Data Size: Undisclosed
Training Data Collection: Undisclosed
Training Labeling: Undisclosed
Training Properties: Undisclosed
Testing Data Collection: Undisclosed
Testing Labeling: Undisclosed
Testing Properties: Undisclosed
Evaluation Data Collection: Undisclosed
Evaluation Labeling: Undisclosed
Evaluation Properties: The vendor reports results on the Artificial Analysis Intelligence Index v5 and Artificial Analysis Coding Agent Index. Underlying task sets, dataset collection and labeling methods, evaluation scaffolding, and run counts are not specified.
Evaluation Benchmark Score: Artificial Analysis Intelligence Index v5: 26; Artificial Analysis Coding Agent Index: 48%.
Acceleration Engine: vLLM
Test Hardware: NVIDIA Blackwell (GB300)
Architecture and training: The model uses 128 routed experts, with four active per token, and one shared expert. Mistral AI states it trained the model from scratch on 3,800 NVIDIA Grace Blackwell GPUs in its European datacenters.
Capabilities: The vendor describes support for 160+ languages, including all official European Union languages, system prompts, native function calling, JSON output, strict JSON schema enforcement, and visual grounding of images, charts, technical drawings, and documents.
Vendor-recommended settings: Reasoning effort can be low, medium, or high, with high as the default; the vendor recommends temperature=1.0 and top_p=0.95. These are vendor model recommendations; hosted endpoint options depend on the serving configuration.
Speculative decoding: The checkpoint includes an embedded DSpark draft head with three dense MLA draft layers in BF16 that propose three tokens. The vendor reports throughput gains of up to 1.4x to 1.8x over serving the target model alone, with gains dependent on hardware and workload.
Vendor-reported evaluation: On the Artificial Analysis Coding Agent Index, the vendor describes the 48% result as effectively tied with GLM-5.3 while using 47% fewer tokens per task on average. The draft does not supply additional evaluation methodology for this comparison.
Vendor usage restriction: The vendor states that operating a Model-as-a-Service business requires a separate commercial license with Mistral.
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Please make sure you have proper rights and permissions for all input image content; if image includes people, personal health information, or intellectual property, the image generated will not blur or maintain proportions of image subjects included.
Users are responsible for model inputs and outputs. Users are responsible for ensuring safe integration of this model, including implementing guardrails as well as other safety mechanisms, prior to deployment.
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