Model ID
microsoft/Phi-4-mini-reasoning
Capabilities
Phi 4 Mini Reasoning
Phi 4 Mini Reasoning is a lightweight dense transformer model optimized for multi-step mathematical and logical reasoning with agentic tool-calling capabilities in constrained environments. It features 3.8B parameters with 32 layers, 3,072 hidden size, and 24 attention heads using grouped-query attention with 8 key-value heads. The model supports a 128K token context window with long RoPE scaling and a sliding window of 262K tokens, along with a 200K vocabulary. Trained on ~150B tokens, it delivers efficient, low-latency structured problem solving.
About this model
Phi 4 Mini Reasoning is a lightweight dense transformer model optimized for multi-step mathematical and logical reasoning with agentic tool-calling capabilities in constrained environments. It features 3.8B parameters with 32 layers, 3,072 hidden size, and 24 attention heads using grouped-query attention with 8 key-value heads. The model supports a 128K token context window with long RoPE scaling and a sliding window of 262K tokens, along with a 200K vocabulary. Trained on ~150B tokens, it delivers efficient, low-latency structured problem solving.
Model specifications and capabilities are published by the model author and reproduced here from HF Model Card (microsoft/Phi-4-mini-reasoning). Released under MIT. Further reading: Paper · Blog
Deploy Private Inference Endpoint
Choose your hardware and inference engine to get deployment commands and performance benchmarks tailored to your infrastructure
Hardware
B200
1,440 GB
Engine
vLLM
SGLang
Deployment code
Access Restricted
Enter your email to get access to this content
Performance under load
Tokens/s (total)
Benchmarks measured by Vultr on B200 · vLLM. Throughput and latency vary with concurrency, input length and engine configuration, so treat these as a comparison baseline, not a service guarantee.
NVIDIA HGX B200
Unlock Next-Generation AI Power on Vultr Cloud GPU