Table of Contents

1. Summary
2. Evaluation
3. Intended Use
4. How to Use
5. Training Details
6. Limitations
7. Security and Responsible Use
8. License
9. Citation

Summary

Apriel-1.6-15B-Thinker is an updated multimodal reasoning model in ServiceNow’s Apriel SLM series, building on Apriel-1.5-15B-Thinker. With significantly improved text and image reasoning capabilities, Apriel-1.6 achieves competitive performance against models up to 10x its size. Like its predecessor, it benefits from extensive continual pre-training across both text and image domains. We additionally perform post-training that focuses on Supervised Finetuning (SFT) and Reinforcement Learning (RL). Apriel-1.6 obtains frontier performance without sacrificing reasoning token efficiency. The model improves or maintains task performance when compared with Apriel-1.5-15B-Thinker, while reducing reasoning token usage by more than 30%.

Highlights

• Achieves a score of 57 on the Artificial Analysis index outperforming models like Gemini 2.5 Flash, Claude Haiku 4.5 and GPT OSS 20b. It obtains a score on par with Qwen3 235B A22B, while being significantly more efficient.
• Reduces reasoning token usage by more than 30%, delivering significantly better efficiency than Apriel-1.5-15B-Thinker.
• Scores 69 on Tau2 Bench Telecom and 69 on IFBench, which are key benchmarks for the enterprise domain.
• At 15B parameters, the model fits on a single GPU, making it highly memory-efficient.
• Based on community feedback on Apriel-1.5-15B-Thinker, we simplified the chat template by removing redundant tags and introduced four special tokens to the tokenizer (, , [BEGIN FINAL RESPONSE], ``) for easier output parsing.

Please see our blog post for more details

Evaluation

• Text benchmarks included in the Artificial Analysis Index v3.0 use scores reported by Artificial Analysis. All other benchmarks were evaluated internally.

* This score is with DCA enabled. Without this, the model scores 36.

** The average score is calculated using all benchmarks except BFCL v3 Only and DeepResearchBench, since some models do not have scores for these two benchmarks.

*** AA LCR score for o3-mini-high is projected score based on its AA Index score.

• For image benchmarks, we report evaluations obtained by https://github.com/open-compass/VLMEvalKit

Intended Use

The Apriel family of models are designed for a variety of general-purpose instruction tasks, including:

• Code assistance and generation
• Logical reasoning and multi-step tasks
• Question answering and information retrieval
• Function calling, complex instruction following and agent use cases

They are not intended for use in safety-critical applications without human oversight or in scenarios requiring guaranteed factual accuracy.

How to Use

pip install transformers

Running the Reasoning model

Here is a code snippet demonstrating the model's usage with the transformers library's generate function:

# Tested with transformers==4.48

import re
import requests
import torch
from PIL import Image
from transformers import AutoProcessor, AutoModelForImageTextToText

# Load model
model_id = "ServiceNow-AI/Apriel-1.6-15b-Thinker"
model = AutoModelForImageTextToText.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16,
    device_map="auto"
)
processor = AutoProcessor.from_pretrained(model_id)

# Example 1: Text-only prompt
chat = [
    {
        "role": "user",
        "content": [
            {"type": "text", "text": "What is the capital for France?"},
        ],
    }
]

inputs = processor.apply_chat_template(chat, add_generation_prompt=True, tokenize=True, return_dict=True, return_tensors="pt")
inputs = {k: v.to(model.device) if isinstance(v, torch.Tensor) else v for k, v in inputs.items()}
inputs.pop("token_type_ids", None)

with torch.no_grad():
    output_ids = model.generate(**inputs, max_new_tokens=1024, do_sample=True, temperature=0.6)

generated_ids = output_ids[:, inputs['input_ids'].shape[1]:]
output = processor.decode(generated_ids[0], skip_special_tokens=True)
response = re.findall(r"\[BEGIN FINAL RESPONSE\](.*?)(?:)", output, re.DOTALL)[0].strip()

print("Text-only Response:", response)

# Example 2: Image understanding
url = "https://picsum.photos/id/237/200/300"
image = Image.open(requests.get(url, stream=True).raw).convert("RGB")

chat = [
    {
        "role": "user",
        "content": [
            {"type": "text", "text": "Which animal is this?"},
            {"type": "image"},
        ],
    }
]

prompt = processor.apply_chat_template(chat, add_generation_prompt=True, tokenize=False)
inputs = processor(text=prompt, images=[image], return_tensors="pt").to(model.device)

with torch.no_grad():
    output_ids = model.generate(**inputs, max_new_tokens=1024, do_sample=True, temperature=0.6)

generated_ids = output_ids[:, inputs['input_ids'].shape[1]:]
output = processor.decode(generated_ids[0], skip_special_tokens=True)
response = re.findall(r"\[BEGIN FINAL RESPONSE\](.*?)(?:)", output, re.DOTALL)[0].strip()

print("Image Response:", response)

Usage Guidelines

1. Use the model’s default chat template, which already includes a system prompt.
2. We recommend setting temperature to 0.6.
3. We ensure the model starts with Here are my reasoning steps:\n during all our evaluations. This is implemented in the default chat template.
4. For multi-turn conversations, intermediate turns (historical model outputs) are expected to contain only the final response, without reasoning steps.

Chat Template

`