Microsoft: Phi 4

Phi-4 performs multi-step logical reasoning and problem-solving tasks using a 14B parameter architecture optimized for inference speed and low memory footprint. The model uses a transformer-based architecture with optimized attention mechanisms and quantization-friendly design that enables deployment on resource-constrained hardware while maintaining reasoning capability across mathematical, coding, and analytical domains.

Phi-4 generates, analyzes, and debugs code across multiple programming languages by leveraging its reasoning capabilities to understand code structure, intent, and correctness. The model processes code as text input and produces syntactically valid code with explanations of logic, using transformer attention patterns trained on code-heavy datasets to maintain semantic correctness across function boundaries and multi-file contexts.

Phi-4 solves mathematical problems by decomposing them into logical steps and performing symbolic reasoning over equations, formulas, and numerical operations. The model uses chain-of-thought patterns to work through algebra, calculus, statistics, and discrete math problems, generating intermediate reasoning steps that can be validated and traced for correctness.

Phi-4 maintains conversational context across multiple turns, using transformer-based attention mechanisms to track conversation history and apply reasoning to follow-up questions that reference prior exchanges. The model processes the full conversation history as input and generates responses that are contextually aware of previous statements, questions, and reasoning chains.

Phi-4 is accessible via OpenRouter's API abstraction layer, which provides unified endpoint access with automatic provider routing, fallback handling, and usage tracking. The API accepts standard HTTP requests with JSON payloads containing messages, system prompts, and inference parameters, returning structured JSON responses with generated text, token counts, and metadata.

Phi-4 can be deployed locally using compatible inference frameworks (llama.cpp, vLLM, Ollama) with support for multiple quantization formats (GGUF, int4, int8) that reduce model size and memory requirements while maintaining reasoning capability. The model weights are distributed in quantized formats that enable inference on consumer hardware with 8-16GB VRAM, using optimized kernels for CPU and GPU acceleration.

Phi-4 can generate structured outputs conforming to JSON schemas by using constrained decoding techniques that guide token generation to produce valid JSON matching specified field types and constraints. The model accepts schema definitions as part of the prompt or system context and generates responses that are guaranteed to parse as valid JSON matching the provided structure, enabling reliable integration with downstream systems.