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PE

Perceptron Mk1.5

PerceptronReleased Sep 25, 2026perceptron/perceptron-mk1.5
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Perceptron Mk1.5 is Perceptron's multimodal perception model for image, video, and audio understanding with visual grounding, reasoning, and function calling. It accepts text, images, video, and audio (WAV, MP3, FLAC) and returns text, spatial and temporal annotations (points, boxes, polygons, clips, and video tracks), structured JSON or regex-constrained responses, and function calls for the caller to execute. It supports configurable reasoning effort, multiple assets per conversation, optional video soundtrack analysis, and Multilook for independent prompts over a shared context. The context window is 36,864 tokens.

Context
37K
Max output
8K
Input /1M
$0.15
Output /1M
$1.50
Blended /1M
$0.49
AcceptsTextImageVideoAudioProducesTextInstruct type none

Providers

FastRouter routes your requests to this provider. You can also bring your own key.

PE
perceptron
perceptron
Input /1M$0.15
Output /1M$1.50
Context37K
Max output8K
Latency—
Throughput—
Supports tool callingSupports structured (JSON) output

Supported parameters

Request parameters you can send with this model.

Parameter
Type
perceptron
Core (Sampling & basic generation)
max_completion_tokensMaximum tokens in the completion.
number
0 to 8192
nNumber of completions to generate.
number
1 to 1default: 1
temperatureControls randomness of output.
number
0 to 2
top_kLimits sampling to top K tokens.
number
≥ 0
top_pNucleus sampling threshold.
number
0 to 1
Penalties (Token Penalties)
frequency_penaltyPenalizes frequent tokens.
number
-2 to 2
presence_penaltyPenalizes tokens already present.
number
-2 to 2
Reasoning (Reasoning Controls)
reasoning_effortReasoning effort before answering.
string
"none", "minimal", "low", "medium", "high"
Tool Use (Function Calls & Tools)
parallel_tool_callsAllow multiple tool calls per turn.
bool
true | falsedefault: true
tool_choiceHow the model should use tools.
string
"auto", "none"default: auto

Performance

Median throughput and time to first token per provider over the last week.

Throughput

Latency

Make your first API call

OpenAI-compatible. Point your SDK at FastRouter, or call the API directly.

Full API docs
Replace <FASTROUTER_API_KEY> with your key · Get your key →Install
OpenAI SDKHTTP
from openai import OpenAI
client = OpenAI(
base_url="https://api.fastrouter.ai/api/v1", # FastRouter base URL
api_key= "<FASTROUTER_API_KEY>", # Replace with your FastRouter API key
)
completion = client.chat.completions.create(
model="perceptron/perceptron-mk1.5", # Replace with your model ID
messages=[
{ "role": "user", "content": "What is the meaning of life?" }
]
)
print(completion.choices[0].message.content)

Frequently asked questions

More models from Perceptron

PE
Perceptron Mk1perceptron/perceptron-mk1

Perceptron Mk1 ("Mark One") is Perceptron's closed-source vision-language model for video understanding and embodied reasoning. It accepts text, images, and video (MP4, WebM, or pre-decoded frames) and returns text, with optional structured spatial and temporal annotations (points, boxes, polygons, and clips) and optional reasoning. It analyzes video at up to 2 frames per second within a 32K-token context window, and supports grounded Q&A, captioning, OCR, object detection and counting, video clipping, in-context learning from examples, JSON Schema or regex constrained output, and Multilook for up to 16 independent prompts over one shared context. It does not support function calling.

Model scores sourced from ArtificialAnalysis