Mistral Small 4 Struggles with Image Recognition

🔑 Enhanced Key Takeaways

• •Mistral Small 4 employs a Mixture of Experts (MoE) architecture with 128 experts and 4 active per token, totaling 119B parameters but only 6B active per token for efficiency.[1]
• •The model features a 256k context window and native multimodality for text and image inputs, with a configurable 'reasoning_effort' parameter to balance speed and depth.[1]
• •Mistral Small 4 achieves 40% lower end-to-end latency and 3x higher throughput compared to Mistral Small 3 in optimized setups.[1]

🛠️ Technical Deep Dive

• •Mixture of Experts (MoE) architecture: 128 experts, 4 active per token; 119B total parameters, 6B active parameters per token (8B including embedding/output layers).[1]
• •Context window: 256k tokens, supporting long-form interactions and document analysis.[1]
• •Configurable reasoning: Users can set 'reasoning_effort' from 'none' (fast responses) to 'high' (deep analysis).[1]
• •Native multimodality: Processes both text and image inputs for tasks like visual analysis and document parsing.[1]
• •Performance optimizations: 40% reduction in completion time and 3x more requests per second vs. Mistral Small 3.[1]

🔮 Future ImplicationsAI analysis grounded in cited sources

⏳ Timeline