Key facts
| Model class | Small multimodal model in the Plugsky catalogue |
| Best for | Image captioning, classification and structured extraction |
| Context class | 128K-class window; live limits are published per model |
| Capabilities | Multimodal input, streaming and JSON mode — check /models for live flags |
| Pricing tier | Paid-plan model; free plan covers plugsky-micro and plugsky-lite |
| API | OpenAI-compatible /v1/chat/completions with image content parts |
| Resilience | Automatic backup upstream plus same-profile multimodal peers |
| Product status | Live |
TL;DR
- A small multimodal step-up: more capacity than the tiniest model, still fast.
- Built for repetitive image tasks with JSON output into your app.
- Use it before reaching for a full vision specialist on hard images.
- Multimodal peer fallback keeps image pipelines running through incidents.
- Validate with a labelled image set before you switch production traffic.
How it works, step by step
- Check the live vision flags for plugsky-gemma3-nano-4b at /models.
- Send a real image plus instruction as OpenAI-style content parts.
- Test a labelled set covering your common and edge image types.
- Use JSON mode and validate every structured field you depend on.
- Compare accuracy and latency with the smallest text-only model and a vision specialist.
- Route image traffic to this model and escalate hard images to a larger one.
- Monitor fallback events and image-task quality over time.
Try it yourself
Open the LLM cost calculator →
What plugsky-gemma3-nano-4b is
plugsky-gemma3-nano-4b is a small multimodal model: it accepts image and text input in one conversation and returns text or structured JSON. It sits between the ultra-compact text model and the full vision specialists in the catalogue. The practical benefit is that it can carry slightly richer visual tasks — more text in an image, more fields in a form — while staying fast enough for inline product use.
Capability flags differ across the compact family and change as routing evolves, so verify the current modalities and limits at /models before building around them.
When to choose it
Choose plugsky-gemma3-nano-4b for production image flows that need more headroom than the smallest model: reading labels and forms, captioning catalogue photos, sorting uploaded documents, or extracting a handful of fields from a scan. JSON mode makes it straightforward to validate the output and store it directly.
Escalate to a dedicated vision model such as plugsky-qwen-vl or to a frontier-tier model when the image is dense, ambiguous or part of a multi-image comparison. For text-only classification, the smaller plugsky-gemma3-nano-2b is usually enough.
Latency, quality and cost trade-offs
Images change the cost profile of any model, because resolution and count translate into prompt size. The model choice is only half of the equation.
- Downscale images for triage and keep high resolution for extraction tasks.
- Prefer one image per request; batch visual comparison is a job for larger models.
- Return JSON and validate; never parse free-form text for critical fields.
- Route low-confidence extractions to a human or a larger model.
- Self-serve plans are flat monthly with fair-use usage — see the live pricing page for current plans.
How to switch to plugsky-gemma3-nano-4b
Switching means changing the model name and sending image parts, for example {"model": "plugsky-gemma3-nano-4b", "messages": [{"role": "user", "content": [{"type": "text", "text": "Return the form fields as JSON."}, {"type": "image_url", "image_url": {"url": ".
Shadow-test on a labelled image set, verify schema validity, then cut traffic over in stages. Keep a vision specialist configured as the escalation target.
Honest comparison
| Dimension | plugsky-gemma3-nano-4b | plugsky-gemma3-nano-2b | Dedicated vision models |
|---|---|---|---|
| Best fit | Small multimodal extraction and captioning | Text-only classification and routing | Text-heavy images and visual reasoning |
| Inputs | Image plus text | Text | Image plus text |
| Latency profile | Fast | Fastest | Moderate |
| Context class | 128K-class | 128K-class | 128K-class |
| Routing role | Middle step for image traffic | First hop for text tasks | Escalation for hard images |
| Failover | Automatic multimodal peer fallback | Automatic compact peer fallback | Automatic peer fallback |
Frequently asked questions
Is plugsky-gemma3-nano-4b free?
No — it is a paid-plan model. The free plan includes two free models, plugsky-micro and plugsky-lite, and a 14-day full-access trial covers paid models.
What kinds of images can it process?
Typical photos, screenshots, forms and scans for captioning, classification and field extraction. Verify the current live modality flags at /models before production use.
Can it generate images?
No. It understands images and returns text or JSON. Media generation endpoints are separate roadmap items — check the docs for their current status.
How does it differ from plugsky-gemma3-nano-2b?
The 4B profile adds multimodal input and more headroom for repetitive visual tasks; the 2B profile is the lighter text-first option for classification and routing.
What context window does it have?
It is 128K-class today. Exact live limits are published per model at /models.
Does it support JSON mode?
Yes, on the shared OpenAI-compatible chat surface. Validate every structured response against your schema before trusting it.
When do I need a bigger model?
When images are dense with text, ambiguous, or need comparison across several images. Route those to a dedicated vision model or a frontier tier.
What happens during an upstream incident?
Requests retry through a backup upstream and same-profile multimodal peers. Live component health is on the status page.