DeepSeek R1 vs Phi-3 Mini 4k

On shared benchmark coverage, Google-Proof Q&A has DeepSeek R1 at 71.5 and Phi-3 Mini 4k at 40.9, with DeepSeek R1 ahead by 30.6 points; HumanEval has DeepSeek R1 at 89.9 and Phi-3 Mini 4k at 59.8, with DeepSeek R1 ahead by 30.1 points. The largest visible gap is 30.6 points on Google-Proof Q&A, which matters most when that benchmark mirrors your workload. Treat isolated benchmark wins as directional, because provider routing, prompt style, and tool access can move real application results.

The capability footprint differs most on reasoning mode: DeepSeek R1, structured outputs: DeepSeek R1, and code execution: DeepSeek R1. Both models share the core language-model surface, so the practical split is not just feature count. Use those differences to decide whether the page is about raw model quality, agentic coding support, multimodal ingestion, or predictable structured API behavior.

For cost, DeepSeek R1 lists $0.1/1M input and $0.3/1M output tokens, while Phi-3 Mini 4k lists $0.05/1M input and $0.25/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Phi-3 Mini 4k lower by about $0.05 per million blended tokens. Availability is 13 providers versus 4, so concentration risk also matters.

Choose DeepSeek R1 when coding workflow support, larger context windows, and broader provider choice are central to the workload. Choose Phi-3 Mini 4k when provider fit and lower input-token cost are more important. For production, rerun your own prompts through the exact provider, region, and tool stack you plan to ship.