perceptron · Efficient
Perceptron Mk1
Perceptron Mk1 is Perceptron's vision-language model for image and video understanding, OCR, document parsing, object detection, counting, spatial localization, and embodied visual reasoning.
- Reasoning
- Vision
- Structured output
- Video input
- Input
- Text, Images, Video
- Output
- Text
- Discovery tier
- Efficient
Pricing
USD per 1M tokens- Input
- $0.15
- Output
- $1.50
Reference prices as published by the model provider. API example
Specifications
- Context window
- 32.8K
- Max output
- 8.2K
Side by side
Perceptron Mk1 compared
Specifications, prices, speed and benchmark scores next to each other. Benchmarks below show every compared model too.
| Perceptron Mk1perceptronThis model | Ultramarine 1 FlashpaperinstrumentsRemove | |
|---|---|---|
| Provider | perceptron | paperinstruments |
| Discovery tier | Efficient | Efficient |
| Added | May 12, 2026 | Oct 15, 2026 |
| Limits | ||
| Context window | 32.77K | — |
| Max output | 8.19K | 32.77KBest |
| Pricing, USD per 1M tokens | ||
| Input | $0.15 | $0.15 |
| Output | $1.50 | $0.50Best |
| Cache read | — | $0.03 |
| Performance | ||
| Request success (24h) | — | 98.4% |
| Capabilities | ||
| Reasoning | Yes | Yes |
| Tools | No | Yes |
| Vision | Yes | Yes |
| Structured output | Yes | Yes |
| Video input | Yes | No |
| Parallel tools | No | Yes |
| Reasoning effort | — | low, high, max |
Public evaluations
Benchmarks
There are no public benchmark results for these models yet.
Developers
Use it through the API
Noumen’s API is OpenAI-compatible: any Chat Completions client works with a base URL and a key.
curl https://www.noumen.xyz/api/v1/chat/completions \
-H "Authorization: Bearer $NOUMEN_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "perceptron/perceptron-mk1",
"messages": [{ "role": "user", "content": "Explain this stack trace." }]
}'import os
from openai import OpenAI
client = OpenAI(base_url="https://www.noumen.xyz/api/v1", api_key=os.environ["NOUMEN_API_KEY"])
reply = client.chat.completions.create(
model="perceptron/perceptron-mk1",
messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)perceptron
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- EfficientCompare