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 | MiniMax M3.1 FlashMiniMaxRemove | |
|---|---|---|
| Provider | perceptron | MiniMax |
| Discovery tier | Efficient | Efficient |
| Added | May 12, 2026 | Oct 19, 2026 |
| Limits | ||
| Context window | 32.77K | 1.05MBest |
| Max output | 8.19K | 524.29KBest |
| Average output | — | 1.4K tokens |
| Pricing, USD per 1M tokens | ||
| Input | $0.15 | $0.15 |
| Output | $1.50 | $0.60Best |
| Cache read | — | $0.015 |
| Performance | ||
| Throughput | — | 220.7 tok/s |
| Time to first token | — | 14.4 s |
| Request success (24h) | — | 99.7% |
| Benchmark indexes | ||
| Intelligence Index | — | 42.7 |
| Capabilities | ||
| Reasoning | Yes | Yes |
| Tools | No | Yes |
| Vision | Yes | Yes |
| Structured output | Yes | No |
| Video input | Yes | Yes |
| Reasoning effort | — | low, medium, high, xhigh, max |
Public evaluations
Benchmarks
Independent evaluation results, with how each score ranks among the models in the Noumen catalog.
- Perceptron Mk1
- MiniMax M3.1 Flash
Intelligence Index
- Perceptron Mk1—
- MiniMax M3.1 Flash42.7
Agentic work
AutomationBench-AA
Workflow automation, penalized for guardrail violations
- Perceptron Mk1: —
- MiniMax M3.1 Flash: 72.5%
AutomationBench-AA tasks completed
Workflows finished end to end without a guardrail violation
- Perceptron Mk1: —
- MiniMax M3.1 Flash: 38.1%
AA-Briefcasev1.1
Agentic knowledge work, rated head to head
- Perceptron Mk1: —
- MiniMax M3.1 Flash: 1,473 Elo
GDPval-AAv2.1
Economically valuable occupational tasks, rated head to head
- Perceptron Mk1: —
- MiniMax M3.1 Flash: 1,458 Elo
Document reasoning
GDP.pdf
Professional reasoning over PDF documents, all-pass rate
- Perceptron Mk1: —
- MiniMax M3.1 Flash: 20.8%
AA-LCRv1.1
Reasoning across long documents
- Perceptron Mk1: —
- MiniMax M3.1 Flash: 79.7%
MLCR-AA
Long-context reasoning over medical records
- Perceptron Mk1: —
- MiniMax M3.1 Flash: 16.7%
Reasoning and knowledge
Humanity’s Last Exam
Expert-level questions across academic fields
- Perceptron Mk1: —
- MiniMax M3.1 Flash: 48.0%
Coding
Terminal-Benchv4.0
Practical software engineering in a terminal
- Perceptron Mk1: —
- MiniMax M3.1 Flash: 25.8%
SciCode
Scientific computing in Python
- Perceptron Mk1: —
- MiniMax M3.1 Flash: 53.4%
Legacy evaluations
AA-LCR (unversioned)
Long-context reasoning, earlier grading
- Perceptron Mk1: —
- MiniMax M3.1 Flash: 79.7%
Scores from Artificial Analysis, updated Oct 26, 2026. Artificial Analysis
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
Related models
Other perceptron models, starting with the same family.
- EfficientCompare