# Apriel-H1: The Surprising Key to Distilling Efficient Reasoning Models

DevFeed: [Apriel-H1: The Surprising Key to Distilling Efficient Reasoning Models](<https://devfeed.tech/articles/apriel-h1-the-surprising-key-to-distilling-efficient-reasoning-models-7043.md>)

Original publisher: [Read original article](<https://huggingface.co/blog/ServiceNow-AI/apriel-h1>)

Author: Torsten Scholak; Oleksiy Ostapenko; Raymond Li; Luke Kumar; Joel Lamy-Poirier

Published: 2025-11-19T05:19:07Z

Content type: article

Language: en

Sources: [Hugging Face - Blog](<https://devfeed.tech/sources/hugging-face-blog.md>)

Topics: [Mamba](<https://devfeed.tech/topics/mamba.md>), [Language models](<https://devfeed.tech/topics/language-models.md>), [Chain-of-thought](<https://devfeed.tech/topics/chain-of-thought.md>), [dataset](<https://devfeed.tech/topics/dataset.md>), [model-deployment](<https://devfeed.tech/topics/model-deployment.md>)

Tags: [benchmarks](<https://devfeed.tech/tags/benchmarks.md>), [efficiency](<https://devfeed.tech/tags/efficiency.md>), [mamba](<https://devfeed.tech/tags/mamba.md>), [models](<https://devfeed.tech/tags/models.md>), [reasoning](<https://devfeed.tech/tags/reasoning.md>), [retrieval](<https://devfeed.tech/tags/retrieval.md>), [tokens](<https://devfeed.tech/tags/tokens.md>), [training](<https://devfeed.tech/tags/training.md>)

## AI overview

The article explains how the Apriel-H1 team distilled a strong 15B reasoning model into more efficient hybrids using Mamba layers. Distillation on pretraining data and generic SFT data reduced reasoning quality, while high-quality reasoning traces from the teacher's SFT dataset preserved performance. The flagship model achieved roughly 2.1x throughput with minimal quality loss across several benchmarks.

## Source excerpt

When MiniMax published their M2 post-mortem in October explaining why they abandoned efficient attention at 230B scale, the narrative briefly became "efficient attention is dead." Within days, Kimi Linear proved otherwise. The real lesson: it depends on your constraints. Our constraint was simple: we had a strong 15B reasoning model and needed to make it efficient without starting over. No infinite compute for 20T-token pretraining. No luxury of architectural co-design from day one.