Mk1.5 API

Perceptron Mk1.5 is Perceptron’s embodied reasoning model for physical agents, built for multimodal input, tool use, and

Modalities: Image, Text, VideoText
In / Out price: $0.1587/$1.59per 1M
Context window: 36K tokens
Released: Sep 25, 2026

Mk1.5 overview

Mk1.5 by Perceptron

Send text, image, video, or audio, and get back text with optional structured annotations, function calls, and graded reasoning.

Mk1.5 specs

Model ID: perceptron/perceptron-mk1.5
Developer: Perceptron
Context window: 36,864 tokens
Max output: 8,192 tokens
Input: Image, Text, Video
Output: Text
API: OpenAI-compatible, base URL https://api.aimlapi.com/v1

What Is Mk1.5 API?

Perceptron Mk1.5 is Perceptron’s embodied reasoning model for physical agents. It accepts text, image, video, and audio input, and returns text with optional structured annotations such as points, boxes, polygons, tracks, and clips.

The model is described as Perceptron’s most performant public model and adds native audio support, video tracking, web search, sub-agent calls, and more complex visual reasoning.

Technical Reference

Developer	Perceptron
Model ID	perceptron/perceptron-mk1.5
Release Date	September 25, 2026
Context Window	36,864 tokens (~37K)
Max Output Tokens	8,192 tokens
Modalities	Image, Text, Video → Text
Capabilities	reasoning, streaming, structured (JSON schema) output, tool/function calling, vision

What Mk1.5 Brings to the Table

Multimodal input and structured output
Mk1.5 accepts text, images, video, and audio, and can answer with text plus structured annotations including points, boxes, polygons, tracks, and clips.

Tool calling and workflows
The model supports arbitrary tools defined in the OpenAI format with JSON Schema, and it can decide when to call them.

Grounded reasoning across space and time
Perceptron describes Mk1.5 as understanding images, video, and audio while grounding answers in space and time, including object tracking and locating events over time.

Streaming and structured outputs
Perceptron’s documentation and model listings state that Mk1.5 supports use in chat-completions workflows for questions, transcription, structured extraction, object tracking, and tool-assisted workflows, with structured outputs via JSON Schema.

Where Mk1.5 Makes the Most Sense

Image, video, and audio understanding
Mk1.5 is presented as suitable for questions, transcription, structured extraction, and multimodal understanding across images, video, and audio.

Object tracking and spatial localization
The model can emit points, boxes, polygons, clips, and object tracks, making it useful for localization and tracking tasks in images and video.

Tool-assisted workflows
Mk1.5 can call application-provided functions and support workflows that combine model reasoning with external tools such as custom functions or web search.

Embodied-agent applications
Perceptron positions Mk1.5 for embodied agents and physical-agent tasks that require grounded perception and action-oriented reasoning.

Mk1.5 vs the Alternatives

Perceptron Mk1
OpenRouter’s comparison page identifies Mk1.5 as the newer model relative to Perceptron Mk1.

Sources: https://www.perceptron.inc/blog/introducing-perceptron-mk1-5 · https://docs.perceptron.inc/perceptron-mk1.5/index.md · https://x.com/perceptroninc/status/2103508527813669193

Use cases

Autonomous drone missions are a documented use case for Mk1.5, including short missions for home inspection, construction site mapping, and search and rescue. It is described as reasoning about what it sees in real time during these flights.

Smart glasses assistance is another documented use case. Mk1.5 is said to power smart glasses and help with navigation by using OCR and web search to supplement the wearer’s surroundings.

Robotic quadruped control is also documented. The model is described as controlling quadrupeds and as being deployed on robotic dogs.

Multimodal question answering and analysis is documented as a general use case. The model is presented as supporting image, video, and audio understanding, including questions, transcription, object tracking, and locating events in time.

Capabilities

Reasoning
Streaming
Structured outputs
Function calling
Vision

Mk1.5 API pricing

Mk1.5 pricing in US dollars
Usage	Price	Unit
Input	$0.1587	1M tokens
Output	$1.59	1M tokens

Prices from the AI/ML API price list, checked on Nov 4, 2026.

Mk1.5 vs other models: token prices

AI/ML API input and output rates for 5 models, per million tokens.

AI/ML API model prices in USD per million tokens
Model	Input	Output
Mk1.5	$0.1587	$1.59
Mk1	$0.1587	$1.59
Gemini 3.1 Flash Lite Preview	$0.2645	$1.59
Muse Glimmer 30B	$0.3703	$1.59
Nemotron 3.5 Super VL	$0.2645	$1.59

How to use the Mk1.5 API

View docs

Build with your agent

Give your agent the model details, code and documentation.

1Get your API key

Create API key

export AIMLAPI_KEY="8f2b4c7d9a1e6b3f…"

2Make your first request

curl 'https://api.aimlapi.com/v1/chat/completions' \
  -H "Authorization: Bearer $AIMLAPI_KEY" \
  -H 'Content-Type: application/json' \
  -d '{
  "model": "perceptron/perceptron-mk1.5",
  "messages": [
    {
      "role": "user",
      "content": "Write a haiku about the ocean."
    }
  ]
}'

import os
import requests

headers = {
    "Authorization": f"Bearer {os.environ['AIMLAPI_KEY']}",
    "Content-Type": "application/json",
}
payload = {
    "model": "perceptron/perceptron-mk1.5",
    "messages": [
        {
            "role": "user",
            "content": "Write a haiku about the ocean."
        }
    ]
}

response = requests.post("https://api.aimlapi.com/v1/chat/completions", headers=headers, json=payload, timeout=120)
response.raise_for_status()
print(response.json())

const response = await fetch("https://api.aimlapi.com/v1/chat/completions", {
  method: "POST",
  headers: {
    Authorization: `Bearer ${process.env.AIMLAPI_KEY}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
    "model": "perceptron/perceptron-mk1.5",
    "messages": [
      {
        "role": "user",
        "content": "Write a haiku about the ocean."
      }
    ]
  }),
});

if (!response.ok) throw new Error(`HTTP ${response.status}`);
console.log(await response.json());

Mk1.5 FAQ

How much does Mk1.5 cost?

Mk1.5 costs $0.1587 input and $1.59 output per 1M tokens on AI/ML API, billed as you go with no subscription.

What inputs does Perceptron Mk1.5 accept on AI/ML API?

Perceptron Mk1.5 accepts text, images, video, and audio input.

What outputs can Perceptron Mk1.5 return?

It returns text plus optional structured annotations such as points, boxes, polygons, tracks, and clips.

Does Perceptron Mk1.5 support vision tasks?

Yes. It understands images and video, and it is described as supporting more complex visual reasoning.

Can Perceptron Mk1.5 handle audio?

Yes. Native audio support is listed as a new capability, and the model can understand audio input.

Does Perceptron Mk1.5 support video tracking?

Yes. It can track objects in video and emit object tracks as timestamped geometries.

Can Perceptron Mk1.5 call tools?

Yes. It supports arbitrary tools and can call functions declared in the OpenAI format with a JSON Schema.

Does Perceptron Mk1.5 support structured output?

Yes. It supports structured outputs via JSON Schema.

Does Perceptron Mk1.5 support streaming?

Yes. Streaming is listed among its supported capabilities.

Can Perceptron Mk1.5 search the web?

Yes. Web search is listed among its new capabilities.

Start building with Mk1.5

Get API key

Mk1.5 API

perceptron/perceptron-mk1.5

Build with your agent

Give your agent the model details, code and documentation.

1Get your API key

Create API key

export AIMLAPI_KEY="8f2b4c7d9a1e6b3f…"

2Make your first request

curl 'https://api.aimlapi.com/v1/chat/completions' \
  -H "Authorization: Bearer $AIMLAPI_KEY" \
  -H 'Content-Type: application/json' \
  -d '{
  "model": "perceptron/perceptron-mk1.5",
  "messages": [
    {
      "role": "user",
      "content": "Write a haiku about the ocean."
    }
  ]
}'

import os
import requests

headers = {
    "Authorization": f"Bearer {os.environ['AIMLAPI_KEY']}",
    "Content-Type": "application/json",
}
payload = {
    "model": "perceptron/perceptron-mk1.5",
    "messages": [
        {
            "role": "user",
            "content": "Write a haiku about the ocean."
        }
    ]
}

response = requests.post("https://api.aimlapi.com/v1/chat/completions", headers=headers, json=payload, timeout=120)
response.raise_for_status()
print(response.json())

const response = await fetch("https://api.aimlapi.com/v1/chat/completions", {
  method: "POST",
  headers: {
    Authorization: `Bearer ${process.env.AIMLAPI_KEY}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
    "model": "perceptron/perceptron-mk1.5",
    "messages": [
      {
        "role": "user",
        "content": "Write a haiku about the ocean."
      }
    ]
  }),
});

if (!response.ok) throw new Error(`HTTP ${response.status}`);
console.log(await response.json());

Docs for AI agents Plain-text index · llms.txt API documentation API reference and examples

Talk to our sales team

Thanks for reaching out

Mk1.5 API

Mk1.5 overview

Mk1.5 by Perceptron

Mk1.5 specs

What Is Mk1.5 API?

Technical Reference

What Mk1.5 Brings to the Table

Where Mk1.5 Makes the Most Sense

Mk1.5 vs the Alternatives

Use cases

Capabilities

Mk1.5 API pricing

Mk1.5 vs other models: token prices

How to use the Mk1.5 API

Build with your agent

1Get your API key

2Make your first request

Mk1.5 FAQ

Start building with Mk1.5

Mk1.5 API

Build with your agent

1Get your API key

2Make your first request

Mk1.5 API

Mk1.5 overview

Mk1.5 by Perceptron

Mk1.5 specs

What Is Mk1.5 API?

Technical Reference

What Mk1.5 Brings to the Table

Where Mk1.5 Makes the Most Sense

Mk1.5 vs the Alternatives

Use cases

Capabilities

Mk1.5 API pricing

Mk1.5 vs other models: token prices

How to use the Mk1.5 API

Build with your agent

1Get your API key

2Make your first request

Models similar to Mk1.5

Mk1

Gemma 3 (27B)

Mistral Nemo

Ministral 3 3B 2512

Qwen3-Next-80B-A3B Instruct

Command R (08-2024)

Mk1.5 FAQ

Start building with Mk1.5