Introducing
Mistral Small 4
A fast instruct model, a powerful reasoning engineer, a multimodal assistant. Mistral Small 4 unifies the capabilities of our flagship models, Magistral, Pixtral, and Devstral, into a single, versatile model for unparalleled efficiency and adaptability.
Executive Impact: Key Metrics
Mistral Small 4 redefines enterprise AI performance, delivering significant advancements in speed, throughput, and context handling.
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Latency Reduction
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Throughput Increase
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Context Window (tokens)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Key Architectural Details
Mixture of Experts (MoE)
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119B Total Parameters
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256k Context Window
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Configurable Reasoning Effort
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Native Multimodality
Extended Context Window
256k Tokens Supported| Feature | Mistral Small 4 (Reasoning) | GPT-OSS 120B |
|---|---|---|
| AA LCR Score | 0.72 | Comparable |
| AA LCR Output Length | 1.6K chars | 5.8-6.1K chars (3.5-4x more) |
| LiveCodeBench Performance | Outperforms | Outperformed |
| LiveCodeBench Output Reduction | 20% less output | N/A |
| Efficiency Impact | Lower latency, reduced cost | Higher latency, higher cost |
End-to-End Latency Reduction
40% Completion Time ReductionRequests Per Second (RPS)
3x Increased ThroughputEnterprise Value: Efficiency & Cost Savings
Efficiency per token directly impacts cost and scalability. Models that maintain or improve performance as responses grow longer reduce the need for manual intervention, lower operational costs, and ensure consistent quality, even for complex, high-stakes tasks like report generation, customer support, or decision-making workflows. Hybrid reasoning models deliver better value by maximizing accuracy without proportional increases in resource use, making them ideal for large-scale deployments where both performance and cost-efficiency are critical.
Technical Advantage: Scalability & Innovation
Performance per token is a key metric for model selection and optimization. Models that scale efficiently allow teams to deploy solutions for longer, more nuanced tasks (e.g., detailed analytics, multi-step reasoning) without sacrificing accuracy or inflating computational costs. This means fewer trade-offs between quality and resource allocation, enabling more innovative and reliable AI-driven applications. It also simplifies fine-tuning and integration, as the model’s robustness reduces the need for constant adjustments or fallback systems.
Intended Use Cases
Developers: Coding automation, codebase exploration
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Enterprises: General chat assistants, document understanding
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Researchers: Math, research, complex reasoning tasks
Calculate Your Potential ROI
Estimate the time and cost savings your organization could achieve by integrating Mistral Small 4.
Estimated Annual Savings
$0
Hours Reclaimed Annually
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Accelerated Implementation Roadmap
Our proven methodology ensures a seamless integration and rapid value realization for Mistral Small 4.
Phase: Evaluation & PoC
Assess Mistral Small 4's capabilities against your specific use cases. Develop a proof-of-concept for core applications.
Phase: Fine-tuning & Integration
Utilize NVIDIA NeMo for domain-specific fine-tuning. Integrate with existing enterprise systems and workflows.
Phase: Pilot Deployment & Optimization
Roll out to a limited user group, gather feedback, and optimize performance and efficiency.
Phase: Full-scale Production
Deploy Mistral Small 4 across the enterprise, leveraging NVIDIA NIM for optimized inference and scalability.
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Partner with us to unlock the full potential of Mistral Small 4 and drive innovation across your organization.
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