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perceptron · Efficient

Perceptron Mk1.5

Added Sep 25, 2026

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Use in the app

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.

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Perceptron Mk1.5perceptronThis modelMiniMax M3.1 FlashMiniMaxRemove
ProviderperceptronMiniMax
Discovery tierEfficientEfficient
AddedSep 25, 2026Oct 19, 2026
Limits
Context window36.86K1.05MBest
Max output8.19K524.29KBest
Average output81 tokens1.4K tokens
Pricing, USD per 1M tokens
Input$0.15$0.15
Output$1.50$0.60Best
Cache read—$0.015
Performance
Throughput—266.1 tok/s
Time to first token—3.7 s
Request success (24h)—99.7%
Benchmark indexes
Intelligence Index—42.7
Capabilities
ReasoningYesYes
ToolsYesYes
VisionYesYes
Structured outputYesNo
Audio inputYesNo
Video inputYesYes
Reasoning effortnone, minimal, low, medium, highlow, medium, high, xhigh, max

Public evaluations

Benchmarks

Independent evaluation results, with how each score ranks among the models in the Noumen catalog.

  • Perceptron Mk1.5
  • MiniMax M3.1 Flash

Intelligence Index

  • Perceptron Mk1.5—
  • MiniMax M3.1 Flash42.7
3.8Catalog models by score55.1

Agentic work

  • AutomationBench-AA

    Workflow automation, penalized for guardrail violations

    • Perceptron Mk1.5: —
    • MiniMax M3.1 Flash: 72.5%

  • AutomationBench-AA tasks completed

    Workflows finished end to end without a guardrail violation

    • Perceptron Mk1.5: —
    • MiniMax M3.1 Flash: 38.1%

  • AA-Briefcasev1.1

    Agentic knowledge work, rated head to head

    • Perceptron Mk1.5: —
    • MiniMax M3.1 Flash: 1,473 Elo

  • GDPval-AAv2.1

    Economically valuable occupational tasks, rated head to head

    • Perceptron Mk1.5: —
    • MiniMax M3.1 Flash: 1,458 Elo

Document reasoning

  • GDP.pdf

    Professional reasoning over PDF documents, all-pass rate

    • Perceptron Mk1.5: —
    • MiniMax M3.1 Flash: 20.8%

  • AA-LCRv1.1

    Reasoning across long documents

    • Perceptron Mk1.5: —
    • MiniMax M3.1 Flash: 79.7%

  • MLCR-AA

    Long-context reasoning over medical records

    • Perceptron Mk1.5: —
    • MiniMax M3.1 Flash: 16.7%

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Perceptron Mk1.5: —
    • MiniMax M3.1 Flash: 48.0%

Coding

  • Terminal-Benchv4.0

    Practical software engineering in a terminal

    • Perceptron Mk1.5: —
    • MiniMax M3.1 Flash: 25.8%

  • SciCode

    Scientific computing in Python

    • Perceptron Mk1.5: —
    • MiniMax M3.1 Flash: 53.4%

Legacy evaluations

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Perceptron Mk1.5: —
    • 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.

Read the quickstart
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)

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