Mk1.5

Perceptron · released Sep 25, 2026

ProprietaryReasoningTool useStructured outputVisionAudio inVideo in

Perceptron Mk1.5 is Perceptron's embodied reasoning model for physical agents. It accepts text, image, video, and audio input, and answers with text plus optional structured annotations: points, boxes, polygons, tracks,...

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Specification

Context window: 37K
Max output: 8K
Knowledge cutoff: Not stated
Parameters: Undisclosed
Licence: Proprietary
Serving providers: 1
Moderated: No
Uptime: 100.0%

Intelligence

—

Not benchmarked

Coding

—

Coding Index

Agentic

—

Agentic Index

Output speed

195 t/s

Median across providers

Latency

614ms

Time to first token

Cost per task

$0.13

Estimated

Benchmarks

Where the score comes from

The Intelligence Index is a composite. These are the underlying evaluations this model was actually measured on.

Evaluation scores

This model has not been put through the benchmark suite. Its speed, latency and pricing figures below are still measured.

Against its peers

Intelligence Index · this model highlighted, nearest peers in grey

Qwen3.8 MaxQwen
58.1
Claude Opus 5.5Anthropic
57.6
Claude Sonnet 5.5Anthropic
56.0
Claude Fable 5.1Anthropic
53.4
GPT-6 AstraOpenAI
52.7
Gemini 4 ArgonGoogle
52.6
Muse WonderMeta
52.4

Peers are the models sitting closest on the Intelligence Index — the set you would realistically choose between.

View as table

Model	Intelligence
Qwen3.8 Max	58.1
Claude Opus 5.5	57.6
Claude Sonnet 5.5	56.0
Claude Fable 5.1	53.4
GPT-6 Astra	52.7
Gemini 4 Argon	52.6
Muse Wonder	52.4

Pricing

What it costs to run

List prices per million tokens, plus what one representative task works out to.

List price

Input / 1M tokens: $0.15
Output / 1M tokens: $1.5
Cached input / 1M: Not offered
Blended 3:1: $0.487

One task, estimated

$0.13

Input tokens: 50,000
Output tokens: 80,000
Profile: Reasoning

Estimated from list pricing: 50K input tokens plus 80K output tokens for reasoning models (25K for non-reasoning).

Serving providers

Perceptron

Speed and latency figures are medians across these providers, so a widely-served model reports a blend rather than any single endpoint.