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Favicon for perceptron

Perceptron: Perceptron Mk1.5

perceptron/perceptron-mk1.5

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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, and clips.

It supports graded reasoning through the standard reasoning controls, function tool calling, and structured outputs via JSON Schema. Structured annotations are emitted inline with text only when requested via the annotation_format parameter ("point", "box", or "polygon" for spatial localization on images, "clip" for temporal segments in video). Video soundtracks are analyzed only when explicitly enabled per request.

Modalities
In / Out Price
$0.15 / $1.50per 1M
Context
37K
Released
Sep 25, 2026
Compare
ProvidersPricingPerformanceUptimeAppsActivityFAQExplore

Providers

This model is hosted by one provider. OpenRouter forwards every request to it directly — no routing decisions to make.

Pricing

The average price customers actually pay for this model, next to the prices providers post. Caching and discounts mean the price actually paid is often well below the listed one.

Performance

Throughput is how fast the model writes (tokens per second — higher is better). Latency is total round-trip time (lower is better). TTFT is time-to-first-token — how long before you see anything appear (lower is better).

Uptime

Uptime is the percentage of the past 3 days that at least one provider was responding to requests. Availability is the percentage of time that inference was successfully served. OpenRouter continuously monitors and uses the next-best provider when one returns an error.

Apps

Public apps that send the most traffic to this model. Good signal for what real production workloads look like — and a hint at which use cases this model is best suited for.

Activity

Token volume and request traffic to this model over time.

API

Drop-in code to call this model. OpenRouter's API is OpenAI-compatible — most SDKs work by just swapping the base URL. The only thing that changes between models is the model slug below.

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P50, best across providers

Latency
10.03s

P50, best provider

Uptime (3d)The model was reachable. Request routed to a provider.
100.00%
Availability (3d)The model returned inference from any provider. Errors and empty responses count against it.
99.94%

Availability over the last 3 days

Last 72 hours
Availability 99.94%
3 Days Ago2 Days AgoYesterdayNow

Availability over the last 24 hours

OpenRouter Availability
93.45%

When an error occurs in an upstream provider, we can recover by routing to another healthy provider, if your request filters allow it. You can access per-provider uptime data programmatically through the Endpoints API. Learn more about our load balancing and customization options.

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Frequently asked questions

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, and clips. It supports graded reasoning through the standard reasoning controls, function tool calling, and structured outputs via JSON Schema.

Perceptron Mk1.5 costs $0.15/M input tokens and $1.50/M output tokens.

Perceptron Mk1.5 has a 36,864 token context window. It supports up to 8,192 completion tokens.

Yes. Perceptron Mk1.5 accepts tools and tool_choice for function calling. It also supports structured outputs via a JSON schema in response_format.

Perceptron Mk1.5 accepts text, images, video, and audio as input and returns text.

Perceptron Mk1 is another text model from Perceptron.

Perceptron Mk1.5 was released on September 25, 2026.

More models from perceptron

Perceptron Mk1

Perceptron Mk1 (Mark One) is Perceptron's highest-quality vision-language model for video and embodied reasoning.** It accepts image and video inputs paired with natural language queries, and produces detailed visual understanding responses, either structured or natural language. It excels at video understanding tasks like video QA, summarization, and event detection. On image inputs, it advances point-by-example grounding from multimodal prompts, OCR and document parsing on messy real-world inputs, open vocabulary object detection and counting, and hand pose estimation.

Reasoning can be enabled per request to trade latency for deeper analysis on harder tasks. Structured annotations are emitted inline with text only when explicitly requested via the annotation_format parameter (pass "point", "box", or "polygon" for spatial localization on images, or "clip" (start/end timestamps) for temporal segments in video). Without annotation_format, the model returns natural-language text only.

Text33K context$0.15 / $1.50