# Apriel (ServiceNow) > Source: https://aiwiki.ai/wiki/apriel > Updated: 2026-10-28 > Categories: AI Models, Large Language Models, Open Source AI > License: CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/) - attribute to "AI Wiki (aiwiki.ai)" > Cite as: AI Wiki. "Apriel (ServiceNow)." aiwiki.ai, 28 Oct 2026. https://aiwiki.ai/wiki/apriel > From AI Wiki (https://aiwiki.ai), the free encyclopedia of artificial intelligence. Reuse freely with attribution. **Apriel** is a family of open-weight [small language models](https://aiwiki.ai/wiki/small_language_model) developed by [ServiceNow](https://aiwiki.ai/wiki/servicenow), the enterprise-software company, through its in-house AI research organization. The family pairs compact size (roughly 5 billion to 15 billion parameters) with an emphasis on strong [reasoning](https://aiwiki.ai/wiki/reasoning_models) and efficiency, so that frontier-adjacent reasoning quality can be delivered on a single GPU and embedded cost-effectively in ServiceNow's [enterprise AI](https://aiwiki.ai/wiki/enterprise_ai) agents and workflows. The best-known members are Apriel-5B (a general-purpose base and instruct model), Apriel-Nemotron-15B-Thinker (a reasoning model built with NVIDIA Nemotron data and infrastructure), and Apriel-1.5-15B-Thinker (a multimodal reasoning model). All are released under the permissive [MIT license](https://aiwiki.ai/wiki/mit_license) on [Hugging Face](https://aiwiki.ai/wiki/hugging_face).[1][2][3]
ServiceNow logo
ServiceNow logo. Source: Wikimedia Commons, Public domain.
The name "Apriel" derives from the Latin word *aperire*, meaning "to open," chosen to signal the family's open-weight release, its clear reasoning-based answers, and its accessibility for developers.[4] ## Overview Apriel models are positioned as an efficiency-first alternative to much larger frontier systems. Rather than scaling parameter count, ServiceNow's stated thesis is that careful data curation and a [staged training](https://aiwiki.ai/wiki/staged_training) recipe can extract competitive reasoning from a model small enough to run on a single accelerator. ServiceNow reports that the 15B "Thinker" variants occupy roughly half the memory of comparable 32B-parameter reasoning models, allowing them to fit within a single 80 GB GPU (such as an [NVIDIA](https://aiwiki.ai/wiki/nvidia) H100) or a pair of consumer GPUs.[2][5] The "Thinker" suffix denotes the reasoning variants, which produce explicit [chain-of-thought](https://aiwiki.ai/wiki/chain_of_thought) traces before answering, in the same vein as other small reasoning models such as [Microsoft](https://aiwiki.ai/wiki/microsoft)'s [Phi](https://aiwiki.ai/wiki/phi) series, Alibaba's [Qwen](https://aiwiki.ai/wiki/qwen) reasoning models, NVIDIA's [Nemotron](https://aiwiki.ai/wiki/nemotron) family, and the distilled variants of [DeepSeek](https://aiwiki.ai/wiki/deepseek)-R1. The family's intended use is to power ServiceNow's agentic products and enterprise workflows, where predictable cost and on-premise deployability matter as much as raw capability.[1][5] ## Developer (ServiceNow AI Research) Apriel is built by ServiceNow's AI research group, sometimes referred to as the ServiceNow [Language Model](https://aiwiki.ai/wiki/language_model)s (SLAM) lab, a collaboration between ServiceNow Research and ServiceNow AI. ServiceNow states that the models were "built and trained entirely in-house, from data to architecture to infrastructure."[1][6] Torsten Scholak is cited as a research lead at ServiceNow's [Foundation Models](https://aiwiki.ai/wiki/foundation_models) Lab overseeing Apriel, and Srinivas Sunkara, ServiceNow's VP of [Machine Learning](https://aiwiki.ai/wiki/machine_learning) Engineering, has spoken publicly about the naming and design goals.[4][6] The two 15B "Thinker" reasoning models were developed in collaboration with NVIDIA. For Apriel-Nemotron-15B-Thinker, ServiceNow used [NVIDIA DGX Cloud](https://aiwiki.ai/wiki/nvidia_dgx_cloud) infrastructure and curated datasets from the NVIDIA Nemotron collection; the company reports that roughly a quarter of the data used in the model's depth up-scaling stage came from Nemotron.[2][5] In October 2025 ServiceNow and NVIDIA announced a deepened partnership, including the next-generation "Apriel 2.0" Nemotron open-model family aimed at multimodal enterprise reasoning.[7] ## The Apriel models The family has grown from a general-purpose 5B base model into a line of compact reasoning systems. - **Apriel-5B** (released April 2025) is a decoder-only model with about 4.8 billion parameters, shipped as a Base and an Instruct variant. ServiceNow reports it was trained on more than 4.5 trillion tokens and emphasizes efficiency, citing roughly 2.3 times fewer GPU-hours and about 31 percent less compute than the comparable OLMo-2 7B model.[3][8] - **Apriel-Nemotron-15B-Thinker** (paper August 2025) is a 15-billion-parameter text reasoning model, up-scaled from Mistral-Nemo-Base-2407 and post-trained with Nemotron data using continual pretraining, [supervised fine-tuning](https://aiwiki.ai/wiki/supervised_fine-tuning), and [reinforcement learning](https://aiwiki.ai/wiki/reinforcement_learning). ServiceNow reports it matches medium-sized state-of-the-art reasoning models such as o1-mini, QwQ-32B, and EXAONE-Deep-32B at roughly half their memory footprint.[2][5] - **Apriel-1.5-15B-Thinker** (released October 2025) is a 15-billion-parameter multimodal (image-and-text) reasoning model built from Mistral's Pixtral-12B base. Its accompanying paper carries the subtitle "Mid-training is all you need," reflecting that it reaches its quality through continual pretraining and supervised fine-tuning alone, without a reinforcement-learning or preference-optimization stage.[1][9] ServiceNow has continued to iterate, with later updates such as Apriel-1.6-15B-Thinker extending the same single-GPU, multimodal reasoning approach.[10] ## Architecture and training Apriel-5B uses a transformer decoder with [grouped-query attention](https://aiwiki.ai/wiki/grouped_query_attention) and YaRN [rotary position embedding](https://aiwiki.ai/wiki/rotary_position_embedding)s, trained on ServiceNow's in-house "Fast-LLM" training stack using on the order of 91,000 H100 GPU-hours, with a stated knowledge cutoff around April 2024.[3] The 15B "Thinker" models are not trained from scratch. They are produced by depth up-scaling, in which transformer layers from a smaller open base model are duplicated to enlarge the network before further training, an approach ServiceNow reports uses less than 20 percent of the compute of training from scratch.[2] - For **Apriel-Nemotron-15B-Thinker**, ServiceNow describes a four-stage pipeline: (1) depth up-scaling from Mistral-Nemo-Base-2407 (a 12B model), (2) continual pretraining on roughly 68 billion tokens weighted toward reasoning and chain-of-thought data, (3) supervised fine-tuning (including specialized math-focused checkpoints later merged by weight averaging), and (4) reinforcement learning using Group Relative Policy Optimization (GRPO) with rule-based rewards.[2] - For **Apriel-1.5-15B-Thinker**, ServiceNow builds on Pixtral-12B-Base-2409, expanding the decoder from 40 to 48 layers and realigning the projection network so the enlarged decoder matches the existing [vision encoder](https://aiwiki.ai/wiki/vision_encoder). Training then proceeds through staged continual pretraining (developing text and vision understanding, then visual reasoning via [synthetic data](https://aiwiki.ai/wiki/synthetic_data)) followed by text-only supervised fine-tuning on curated reasoning traces. The paper reports the final model was trained on 640 NVIDIA H100 GPUs over roughly seven days, and that no image-specific SFT or RL stage was used.[1][9] ## Benchmarks ServiceNow's central claim is "frontier-adjacent" reasoning at around 15 billion parameters. The headline result is for Apriel-1.5-15B-Thinker, which the independent evaluation service [Artificial Analysis](https://aiwiki.ai/wiki/artificial_analysis) scores at 52 on its Artificial Analysis Intelligence Index, an aggregate of ten third-party evaluations. ServiceNow and Artificial Analysis note this is competitive with much larger systems such as DeepSeek-R1-0528 while the model is roughly 8 to 10 times smaller, framing it as comparable intelligence at a fraction of the cost.[1][11] These scores are vendor-reported or drawn from a third-party aggregator and should be read as such; users should consult the original sources for exact, current figures. Reported component scores for Apriel-1.5-15B-Thinker include approximately 88 on [AIME 2025](https://aiwiki.ai/wiki/aime_2025), about 71 on [GPQA Diamond](https://aiwiki.ai/wiki/gpqa_diamond), about 73 on [LiveCodeBench](https://aiwiki.ai/wiki/livecodebench), 62 on the IFBench instruction-following benchmark, and 68 on the Tau-squared Bench (Telecom) agentic benchmark.[1][11] For the earlier Apriel-Nemotron-15B-Thinker, ServiceNow's paper reports figures such as [AIME 2024](https://aiwiki.ai/wiki/aime_2024) around 73, MATH-500 around 92, [MMLU-Pro](https://aiwiki.ai/wiki/mmlu-pro) around 73, and GPQA Diamond around 57, evaluated alongside enterprise-focused tasks including MBPP, BFCL, Enterprise RAG, MT-Bench, MixEval, IF-Eval, and [MultiChallenge](https://aiwiki.ai/wiki/multichallenge).[2] The table below summarizes the principal members of the family. | Model | Released | Parameters | Modality | Base model | Key training stages | License | Notable reported result | |-------|----------|-----------|----------|-----------|---------------------|---------|-------------------------| | Apriel-5B (Base / Instruct) | April 2025 | ~4.8B | Text | Trained from scratch | Pretraining (4.5T+ tokens) on Fast-LLM | MIT | GSM8K ~64 (Base); IFEval ~81 (Instruct) | | Apriel-Nemotron-15B-Thinker | August 2025 | ~15B | Text | Mistral-Nemo-Base-2407 (12B) | Depth up-scaling, CPT (~68B tokens), SFT, GRPO RL | MIT | [AIME 2024](https://aiwiki.ai/wiki/aime_2024) ~73; MATH-500 ~92 | | Apriel-1.5-15B-Thinker | October 2025 | ~15B | Text + image | Pixtral-12B-Base-2409 | Depth up-scaling (40 to 48 layers), staged CPT, SFT | MIT | Artificial Analysis Index 52; [AIME 2025](https://aiwiki.ai/wiki/aime_2025) ~88 | Figures are approximate and as reported by ServiceNow or by Artificial Analysis; benchmark methodologies differ and results change with evaluation settings. ## Availability and significance All Apriel models are released as open-weight checkpoints under the MIT license and are distributed through Hugging Face, with the reasoning variants also offered via inference partners.[1][3][10] The MIT license permits broad commercial use, which fits ServiceNow's strategy of embedding the models in its own platform while also encouraging external adoption. Apriel illustrates a broader 2025 to 2026 trend toward small, open reasoning models that aim to close much of the gap to frontier systems through data-centric training rather than sheer scale, alongside families such as Phi, Qwen, Nemotron, and DeepSeek distillations. For ServiceNow specifically, the family provides a controllable, deployable foundation for enterprise AI agents, where the ability to run capable reasoning on a single GPU translates directly into lower serving cost and easier on-premise or regulated deployment.[1][5][7] ## References 1. ServiceNow-AI. "Apriel-1.5-15b-Thinker." Hugging Face. https://huggingface.co/ServiceNow-AI/Apriel-1.5-15b-Thinker 2. ServiceNow Language Models Lab. "Apriel-Nemotron-15B-Thinker." arXiv:2508.10948. https://arxiv.org/abs/2508.10948 3. ServiceNow-AI. "Apriel-5B-Base." Hugging Face. https://huggingface.co/ServiceNow-AI/Apriel-5B-Base 4. The Letter Two. "The Meaning Behind ServiceNow's Apriel AI Model Name." May 18, 2025. https://thelettertwo.com/2025/05/18/why-servicenow-named-its-lightweight-ai-models-apriel/ 5. NVIDIA Blog. "Your Service Teams Just Got a New Coworker, Built by ServiceNow and NVIDIA." https://blogs.nvidia.com/blog/servicenow-apriel-nemotron/ 6. ServiceNow. "Apriel Model Family: Frontier Reasoning." 2025. https://www.servicenow.com/blogs/2025/apriel-model-family-frontier-reasoning 7. Business Wire. "ServiceNow Unites Intelligent Workflows and Open Models with NVIDIA Technologies to Scale Trusted AI Across Industries." October 28, 2025. https://www.businesswire.com/news/home/20251028739624/en/ 8. ServiceNow. "Apriel 5B: Small but mighty enterprise language model." 2025. https://www.servicenow.com/blogs/2025/apriel-5b-small-enterprise-language-model 9. ServiceNow AI Research. "Apriel-1.5-15b-Thinker: Mid-training is all you need." arXiv:2510.01141. https://arxiv.org/abs/2510.01141 10. ServiceNow-AI. "Apriel-1.6-15b-Thinker: Cost-efficient Frontier Multimodal Performance." Hugging Face Blog. https://huggingface.co/blog/ServiceNow-AI/apriel-1p6-15b-thinker 11. Artificial Analysis. "Apriel-v1.5-15B-Thinker: Intelligence, Performance & Price Analysis." https://artificialanalysis.ai/models/apriel-v1-5-15b-thinker