Apriel (ServiceNow)
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Last reviewed
Jun 8, 2026
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Apriel is a family of open-weight small language models developed by 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 and efficiency, so that frontier-adjacent reasoning quality can be delivered on a single GPU and embedded cost-effectively in ServiceNow's 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 on Hugging Face.[1][2][3]
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]
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 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 H100) or a pair of consumer GPUs.[2][5]
The "Thinker" suffix denotes the reasoning variants, which produce explicit chain-of-thought traces before answering, in the same vein as other small reasoning models such as Microsoft's Phi series, Alibaba's Qwen reasoning models, NVIDIA's Nemotron family, and the distilled variants of 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]
Apriel is built by ServiceNow's AI research group, sometimes referred to as the ServiceNow Language Models (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 Lab overseeing Apriel, and Srinivas Sunkara, ServiceNow's VP of 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 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 family has grown from a general-purpose 5B base model into a line of compact reasoning systems.
ServiceNow has continued to iterate, with later updates such as Apriel-1.6-15B-Thinker extending the same single-GPU, multimodal reasoning approach.[10]
Apriel-5B uses a transformer decoder with grouped-query attention and YaRN rotary position embeddings, 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]
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 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, about 71 on GPQA Diamond, about 73 on 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 around 73, MATH-500 around 92, 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.[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 ~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 ~88 |
Figures are approximate and as reported by ServiceNow or by Artificial Analysis; benchmark methodologies differ and results change with evaluation settings.
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]