Apriel-1.6-Thinker

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

`