DeepSeek R1 Distill Qwen 14B

DeepSeek R1 Distill Qwen 14B is part of DeepSeek's R1 distillation series, a set of smaller dense models trained on reasoning data generated by the larger DeepSeek-R1 model. Rather than being a scaled-down architecture of R1 itself, it uses a Qwen 14B dense backbone that has been fine-tuned so that it reproduces the step-by-step, chain-of-thought answering style of R1. Within the R1 distill lineup it sits in the mid-size range, above the 1.5B, 7B, and 8B variants and below the 32B and 70B distills, which makes it a common choice when a full R1 deployment is impractical but a very small distill is not accurate enough. The model responds in R1's characteristic format: it emits an extended internal reasoning segment before producing a final answer, which means output token counts per request are typically much higher than for a conventional instruction-tuned chat model of similar size. It is a text-in, text-out chat model aimed at math, logic puzzles, code generation and debugging, and multi-step question answering. Because it is a dense 14B model with published weights, it can be self-hosted on a single mid-range or high-end GPU, and it is also offered through several inference API providers — see the pricing table on this page for who serves it and at what rates. In practice this model is used where reasoning quality matters more than latency and where the cost or hardware footprint of a large mixture-of-experts reasoning model is prohibitive. Compared with the 32B and 70B distills it trades some accuracy on hard reasoning benchmarks for lower memory requirements and faster generation; compared with the 7B and 8B distills it generally holds up better on multi-step math and code tasks. Our database does not currently record throughput or time-to-first-token measurements for this model, so compare provider-reported performance before committing to a deployment.