Model Detail

Phi-4-mini-instruct is a code generation model with 1.9B parameters released by Microsoft. The model is registered under the text-generation pipeline tag on Hugging Face, and supports text->text inputs, distributed under the permissive mit license.

Open-LLM-Leaderboard scoring places it at MMLU-Pro 33, GPQA 8, IFEval 74, BBH 39, giving a sense of how it handles instruction following, reasoning, and graduate-level QA in absolute terms.

Phi-4-mini-instruct is priced at $0.075/M input tokens and $0.3/M output tokens. Operationally the model offers a 131K-token context window, which matters when sizing it for prompt-heavy or latency-sensitive workloads. At this input rate the model sits in the commodity tier and is suitable for high-volume workloads where per-call cost dominates the decision.

Phi-4-mini-instruct ships as a Phi3ForCausalLM / 💬 chat models (RLHF, DPO, IFT, ...) architecture with 1.9B parameters. Total weight footprint is approximately 3.8 GB, which is the relevant figure when planning local-inference VRAM. The mit license is permissive, allowing commercial deployment and derivative work without per-seat fees, though attribution requirements still apply.

Phi-4-mini-instruct is best fit for code completion, repository-scale Q&A, and pair-programming integrations, and high-volume batch jobs where per-call cost dominates the budget. It is a less obvious choice for one-shot generation of security-critical code without review. Treat this as a starting matrix rather than a benchmark verdict — the right deployment usually depends on the specific evaluation suite that mirrors your workload.