Models / ServiceNow AIApriel /  / Apriel-1.5-15b-Thinker API

Apriel-1.5-15b-Thinker API

Frontier-level multimodal reasoning in a compact, efficient model

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Apriel-1.5-15b-Thinker is a breakthrough multimodal reasoning model from ServiceNow's Apriel SLM series that achieves frontier-level performance despite being just 15 billion parameters. Built through innovative mid-training techniques, this model demonstrates that thoughtful data curation and staged continual pretraining can rival systems 10x its size.

52
AA Intelligence Index
Competitive with DeepSeek-R1 & Gemini-2.5-Flash
87%
AIME'25 Accuracy
Elite mathematical reasoning
1 GPU
Deployment Footprint
Single-GPU efficiency at 15B params
Key Capabilities
Mathematical Reasoning: 87% on AIME'25 for competition-level mathematics
Enterprise Benchmarks: 68% Tau2 Bench Telecom, 62% IFBench
Multimodal Understanding: Text and image reasoning across diverse domains
Accessible Deployment: Frontier AI for organizations with limited infrastructure

Apriel-1.5-15b-Thinker API Usage

Endpoint

curl -X POST "https://api.together.xyz/v1/chat/completions" \
  -H "Authorization: Bearer $TOGETHER_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "ServiceNow-AI/Apriel-1.5-15b-Thinker",
    "messages": [
      {
        "role": "user",
        "content": "What are some fun things to do in New York?"
      }
    ]
}'
curl -X POST "https://api.together.xyz/v1/images/generations" \
  -H "Authorization: Bearer $TOGETHER_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "ServiceNow-AI/Apriel-1.5-15b-Thinker",
    "prompt": "Draw an anime style version of this image.",
    "width": 1024,
    "height": 768,
    "steps": 28,
    "n": 1,
    "response_format": "url",
    "image_url": "https://huggingface.co/datasets/patrickvonplaten/random_img/resolve/main/yosemite.png"
  }'
curl -X POST https://api.together.xyz/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $TOGETHER_API_KEY" \
  -d '{
    "model": "ServiceNow-AI/Apriel-1.5-15b-Thinker",
    "messages": [{
      "role": "user",
      "content": [
        {"type": "text", "text": "Describe what you see in this image."},
        {"type": "image_url", "image_url": {"url": "https://huggingface.co/datasets/patrickvonplaten/random_img/resolve/main/yosemite.png"}}
      ]
    }],
    "max_tokens": 512
  }'
curl -X POST https://api.together.xyz/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $TOGETHER_API_KEY" \
  -d '{
    "model": "ServiceNow-AI/Apriel-1.5-15b-Thinker",
    "messages": [{
      "role": "user",
      "content": "Given two binary strings `a` and `b`, return their sum as a binary string"
    }]
  }'
curl -X POST https://api.together.xyz/v1/rerank \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $TOGETHER_API_KEY" \
  -d '{
    "model": "ServiceNow-AI/Apriel-1.5-15b-Thinker",
    "query": "What animals can I find near Peru?",
    "documents": [
      "The giant panda (Ailuropoda melanoleuca), also known as the panda bear or simply panda, is a bear species endemic to China.",
      "The llama is a domesticated South American camelid, widely used as a meat and pack animal by Andean cultures since the pre-Columbian era.",
      "The wild Bactrian camel (Camelus ferus) is an endangered species of camel endemic to Northwest China and southwestern Mongolia.",
      "The guanaco is a camelid native to South America, closely related to the llama. Guanacos are one of two wild South American camelids; the other species is the vicuña, which lives at higher elevations."
    ],
    "top_n": 2
  }'
curl -X POST https://api.together.xyz/v1/embeddings \
  -H "Authorization: Bearer $TOGETHER_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "input": "Our solar system orbits the Milky Way galaxy at about 515,000 mph.",
    "model": "ServiceNow-AI/Apriel-1.5-15b-Thinker"
  }'
curl -X POST https://api.together.xyz/v1/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $TOGETHER_API_KEY" \
  -d '{
    "model": "meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8",
    "prompt": "A horse is a horse",
    "max_tokens": 32,
    "temperature": 0.1,
    "safety_model": "ServiceNow-AI/Apriel-1.5-15b-Thinker"
  }'
curl --location 'https://api.together.ai/v1/audio/generations' \
  --header 'Content-Type: application/json' \
  --header 'Authorization: Bearer $TOGETHER_API_KEY' \
  --output speech.mp3 \
  --data '{
    "input": "Today is a wonderful day to build something people love!",
    "voice": "helpful woman",
    "response_format": "mp3",
    "sample_rate": 44100,
    "stream": false,
    "model": "ServiceNow-AI/Apriel-1.5-15b-Thinker"
  }'
curl -X POST "https://api.together.xyz/v1/audio/transcriptions" \
  -H "Authorization: Bearer $TOGETHER_API_KEY" \
  -F "model=ServiceNow-AI/Apriel-1.5-15b-Thinker" \
  -F "language=en" \
  -F "response_format=json" \
  -F "timestamp_granularities=segment"
from together import Together

client = Together()

response = client.chat.completions.create(
  model="ServiceNow-AI/Apriel-1.5-15b-Thinker",
  messages=[
    {
      "role": "user",
      "content": "What are some fun things to do in New York?"
    }
  ]
)
print(response.choices[0].message.content)
from together import Together

client = Together()

imageCompletion = client.images.generate(
    model="ServiceNow-AI/Apriel-1.5-15b-Thinker",
    width=1024,
    height=768,
    steps=28,
    prompt="Draw an anime style version of this image.",
    image_url="https://huggingface.co/datasets/patrickvonplaten/random_img/resolve/main/yosemite.png",
)

print(imageCompletion.data[0].url)


from together import Together

client = Together()

response = client.chat.completions.create(
    model="ServiceNow-AI/Apriel-1.5-15b-Thinker",
    messages=[{
    	"role": "user",
      "content": [
        {"type": "text", "text": "Describe what you see in this image."},
        {"type": "image_url", "image_url": {"url": "https://huggingface.co/datasets/patrickvonplaten/random_img/resolve/main/yosemite.png"}}
      ]
    }]
)
print(response.choices[0].message.content)

from together import Together

client = Together()
response = client.chat.completions.create(
  model="ServiceNow-AI/Apriel-1.5-15b-Thinker",
  messages=[
  	{
	    "role": "user", 
      "content": "Given two binary strings `a` and `b`, return their sum as a binary string"
    }
 ],
)

print(response.choices[0].message.content)

from together import Together

client = Together()

query = "What animals can I find near Peru?"

documents = [
  "The giant panda (Ailuropoda melanoleuca), also known as the panda bear or simply panda, is a bear species endemic to China.",
  "The llama is a domesticated South American camelid, widely used as a meat and pack animal by Andean cultures since the pre-Columbian era.",
  "The wild Bactrian camel (Camelus ferus) is an endangered species of camel endemic to Northwest China and southwestern Mongolia.",
  "The guanaco is a camelid native to South America, closely related to the llama. Guanacos are one of two wild South American camelids; the other species is the vicuña, which lives at higher elevations.",
]

response = client.rerank.create(
  model="ServiceNow-AI/Apriel-1.5-15b-Thinker",
  query=query,
  documents=documents,
  top_n=2
)

for result in response.results:
    print(f"Relevance Score: {result.relevance_score}")

from together import Together

client = Together()

response = client.embeddings.create(
  model = "ServiceNow-AI/Apriel-1.5-15b-Thinker",
  input = "Our solar system orbits the Milky Way galaxy at about 515,000 mph"
)

from together import Together

client = Together()

response = client.completions.create(
  model="meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8",
  prompt="A horse is a horse",
  max_tokens=32,
  temperature=0.1,
  safety_model="ServiceNow-AI/Apriel-1.5-15b-Thinker",
)

print(response.choices[0].text)

from together import Together

client = Together()

speech_file_path = "speech.mp3"

response = client.audio.speech.create(
  model="ServiceNow-AI/Apriel-1.5-15b-Thinker",
  input="Today is a wonderful day to build something people love!",
  voice="helpful woman",
)
    
response.stream_to_file(speech_file_path)

from together import Together

client = Together()
response = client.audio.transcribe(
    model="ServiceNow-AI/Apriel-1.5-15b-Thinker",
    language="en",
    response_format="json",
    timestamp_granularities="segment"
)
print(response.text)
import Together from 'together-ai';
const together = new Together();

const completion = await together.chat.completions.create({
  model: 'ServiceNow-AI/Apriel-1.5-15b-Thinker',
  messages: [
    {
      role: 'user',
      content: 'What are some fun things to do in New York?'
     }
  ],
});

console.log(completion.choices[0].message.content);
import Together from "together-ai";

const together = new Together();

async function main() {
  const response = await together.images.create({
    model: "ServiceNow-AI/Apriel-1.5-15b-Thinker",
    width: 1024,
    height: 1024,
    steps: 28,
    prompt: "Draw an anime style version of this image.",
    image_url: "https://huggingface.co/datasets/patrickvonplaten/random_img/resolve/main/yosemite.png",
  });

  console.log(response.data[0].url);
}

main();

import Together from "together-ai";

const together = new Together();
const imageUrl = "https://huggingface.co/datasets/patrickvonplaten/random_img/resolve/main/yosemite.png";

async function main() {
  const response = await together.chat.completions.create({
    model: "ServiceNow-AI/Apriel-1.5-15b-Thinker",
    messages: [{
      role: "user",
      content: [
        { type: "text", text: "Describe what you see in this image." },
        { type: "image_url", image_url: { url: imageUrl } }
      ]
    }]
  });
  
  console.log(response.choices[0]?.message?.content);
}

main();

import Together from "together-ai";

const together = new Together();

async function main() {
  const response = await together.chat.completions.create({
    model: "ServiceNow-AI/Apriel-1.5-15b-Thinker",
    messages: [{
      role: "user",
      content: "Given two binary strings `a` and `b`, return their sum as a binary string"
    }]
  });
  
  console.log(response.choices[0]?.message?.content);
}

main();

import Together from "together-ai";

const together = new Together();

const query = "What animals can I find near Peru?";
const documents = [
  "The giant panda (Ailuropoda melanoleuca), also known as the panda bear or simply panda, is a bear species endemic to China.",
  "The llama is a domesticated South American camelid, widely used as a meat and pack animal by Andean cultures since the pre-Columbian era.",
  "The wild Bactrian camel (Camelus ferus) is an endangered species of camel endemic to Northwest China and southwestern Mongolia.",
  "The guanaco is a camelid native to South America, closely related to the llama. Guanacos are one of two wild South American camelids; the other species is the vicuña, which lives at higher elevations."
];

async function main() {
  const response = await together.rerank.create({
    model: "ServiceNow-AI/Apriel-1.5-15b-Thinker",
    query: query,
    documents: documents,
    top_n: 2
  });
  
  for (const result of response.results) {
    console.log(`Relevance Score: ${result.relevance_score}`);
  }
}

main();


import Together from "together-ai";

const together = new Together();

const response = await client.embeddings.create({
  model: 'ServiceNow-AI/Apriel-1.5-15b-Thinker',
  input: 'Our solar system orbits the Milky Way galaxy at about 515,000 mph',
});

import Together from "together-ai";

const together = new Together();

async function main() {
  const response = await together.completions.create({
    model: "meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8",
    prompt: "A horse is a horse",
    max_tokens: 32,
    temperature: 0.1,
    safety_model: "ServiceNow-AI/Apriel-1.5-15b-Thinker"
  });
  
  console.log(response.choices[0]?.text);
}

main();

import Together from 'together-ai';

const together = new Together();

async function generateAudio() {
   const res = await together.audio.create({
    input: 'Today is a wonderful day to build something people love!',
    voice: 'helpful woman',
    response_format: 'mp3',
    sample_rate: 44100,
    stream: false,
    model: 'ServiceNow-AI/Apriel-1.5-15b-Thinker',
  });

  if (res.body) {
    console.log(res.body);
    const nodeStream = Readable.from(res.body as ReadableStream);
    const fileStream = createWriteStream('./speech.mp3');

    nodeStream.pipe(fileStream);
  }
}

generateAudio();

import Together from "together-ai";

const together = new Together();

const response = await together.audio.transcriptions.create(
  model: "ServiceNow-AI/Apriel-1.5-15b-Thinker",
  language: "en",
  response_format: "json",
  timestamp_granularities: "segment"
});
console.log(response)

How to use Apriel-1.5-15b-Thinker

Model details

Architecture & Foundation:
• Built from Pixtral-12B base using depth upscaling (40 to 48 layers) for enhanced reasoning capacity
• Multimodal architecture with vision encoder, projection network, and decoder supporting both text and image inputs
• 14.9B parameters optimized for single-GPU deployment with BF16 precision
• 131K token context window with sequence packing for efficient processing

Training Methodology:
• Three-stage progressive training: depth upscaling, staged continual pretraining (CPT), and supervised fine-tuning (SFT)
• CPT Stage 1: 50% text reasoning, 20% replay data, 30% multimodal tokens covering documents, charts, OCR, and visual reasoning
• CPT Stage 2: Targeted visual reasoning via synthetic data generation for spatial structure, compositional understanding, and fine-grained perception
• Text-SFT only approach with 2M+ high-quality instruction-response pairs featuring explicit reasoning traces—no reinforcement learning or preference optimization
• Trained on 640 H100 GPUs for 7 days using Fast-LLM training stack

Performance Characteristics:
• Achieves 52 on Artificial Analysis Intelligence Index, matching DeepSeek-R1-0528 and Gemini-2.5-Flash
• Strong mathematical reasoning: 87% AIME'25, 77.3% MMLU-Pro, 71.3% GPQA Diamond
• Enterprise-focused benchmarks: 68% Tau2 Bench Telecom, 62% IFBench
• Multimodal capabilities: 70.2% MMMU, 75.5% MathVista, 88.2% CharXiv descriptive, 82.87% AI2D
• Extensive reasoning by default with explicit thinking steps before final responses
• Performs within 5 points of Gemini-2.5-Flash and Claude Sonnet-3.7 across ten vision benchmarks
• At least 1/10 the size of any model scoring >50 on AA Intelligence Index

Prompting Apriel-1.5-15b-Thinker

Applications & Use Cases

Mathematical & Scientific Reasoning:
• Competition-level mathematics: 87% on AIME'25, 80.66% on AIME'24
• Graduate-level problem solving: 71.3% on GPQA Diamond
• Scientific computing and reasoning tasks with strong performance on SciCode
• Mathematical reasoning within visual contexts (MathVision, MathVista, MathVerse)

Code Assistance & Development:
• Functional correctness in code generation via LiveCodeBench evaluation
• Coding tasks spanning multiple programming languages
• API/function invocation and complex instruction following
• Real-world Linux shell execution and system tool use (TerminalBench)

Enterprise & Domain-Specific Applications:
• Specialized telecom domain tasks: 68% on Tau2 Bench Telecom
• Instruction following and compliance: 62% on IFBench
• Document understanding, chart interpretation, and OCR-related tasks
• Long-context reasoning (AA-LCR benchmark) for extended document analysis

Multimodal Understanding:
• Image understanding and reasoning: 70.2% MMMU, 66.3% MMStar
• Document and diagram comprehension: 88.2% CharXiv descriptive, 82.87% AI2D
• Visual mathematical problem-solving: 75.5% MathVista
• Chart understanding with descriptive and reasoning capabilities

General-Purpose Capabilities:
• Multi-domain knowledge and advanced reasoning (77.3% MMLU-Pro)
• Conversational AI and question answering across diverse topics
• Logical reasoning and multi-step task execution
• Content moderation, security, and robustness applications
• On-premises deployment for privacy-sensitive and air-gapped environments

Looking for production scale? Deploy on a dedicated endpoint

Deploy Apriel-1.5-15b-Thinker on a dedicated endpoint with custom hardware configuration, as many instances as you need, and auto-scaling.

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