perceptron · Efficient
Perceptron Mk1.5
Perceptron Mk1.5 is a multimodal reasoning model for visual analysis and physical agents. It accepts text, images, video, and audio, supports tool calling and structured output, and can return spatial annotations such as points, boxes, polygons, and tracks.
- Reasoning
- Tools
- Vision
- Structured output
- Audio input
- Video input
- Input
- Text, Images, Video, Audio
- Output
- Text
- Reasoning effort
- none · minimal · low · medium · high
- 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
- 36.9K
- Max output
- 8.2K
- Average output (7 days)
- 81 tokens
- Longer than 7% of 468 models
Side by side
Perceptron Mk1.5 compared
Specifications, prices, speed and benchmark scores next to each other. Benchmarks below show every compared model too.
| Perceptron Mk1.5perceptronThis model | Qwen3.8 Omni FlashQwenRemove | |
|---|---|---|
| Provider | perceptron | Qwen |
| Discovery tier | Efficient | Efficient |
| Added | Sep 25, 2026 | Sep 17, 2026 |
| Limits | ||
| Context window | 36.86K | 991.81KBest |
| Max output | 8.19K | 131.07KBest |
| Average output | 81 tokens | — |
| Pricing, USD per 1M tokens | ||
| Input | $0.15 | $0.15 |
| Output | $1.50 | $0.47Best |
| Cache read | — | $0.016 |
| Performance | ||
| Request success (24h) | — | 100.0% |
| Capabilities | ||
| Reasoning | Yes | Yes |
| Tools | Yes | Yes |
| Vision | Yes | Yes |
| Structured output | Yes | Yes |
| Audio input | Yes | Yes |
| Video input | Yes | Yes |
| Reasoning effort | none, minimal, low, medium, high | none, high |
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-mk1.5",
"reasoning_effort": "medium",
"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-mk1.5",
messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)perceptron
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- EfficientCompare