Key facts
| Model class | Ultra-compact model in the Plugsky catalogue |
| Best for | Classification, routing, tagging, simple chat and batch jobs |
| Context class | 128K-class window; live limits are published per model |
| Capabilities | 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 — change the model name only |
| Resilience | Automatic backup upstream plus same-profile compact peers |
| Product status | Live |
TL;DR
- The smallest tier in the family: built for volume, not depth.
- Ideal as a first hop for classification, routing and tagging.
- JSON mode keeps structured outputs compact and parseable.
- Escalate hard or ambiguous inputs to a larger sibling model.
- Compact peers provide automatic failover during incidents.
How it works, step by step
- Confirm your plan can call plugsky-gemma3-nano-2b.
- Read the live card at /models for context window and feature flags.
- Build a labelled sample of the classification or routing task you care about.
- Run it through the model with JSON mode and validate the output schema.
- Compare accuracy and latency against a larger model on the same sample.
- Use it as the first hop and route low-confidence cases to a larger model.
- Monitor drift and retune the confidence threshold over time.
Try it yourself
Open the LLM cost calculator →
What plugsky-gemma3-nano-2b is
plugsky-gemma3-nano-2b is the ultra-compact member of the catalogue. Compact models are not a compromise for every task — they are the correct tool when the task is narrow and the call volume is high. Classification labels, intent tags, routing decisions and simple extraction rarely need deep reasoning, and a small model returns them quickly and cheaply. It serves the standard OpenAI-compatible chat endpoint with streaming and JSON mode.
The exact live limits and flags are on the catalogue at /models. Check that page before wiring assumptions about context or tools into your application.
When to choose it
Choose plugsky-gemma3-nano-2b for the high-volume first hop in a cascade: labelling incoming tickets, deciding which downstream model or queue should handle a request, tagging content, and powering simple FAQ chat where answers are short and known.
Escalate when the request needs multi-step reasoning, long-form writing or domain judgement. A good pattern is confidence-based routing: the compact model handles the easy majority, and a larger model such as plugsky-pro or plugsky-frontier handles the rest.
Compact-model trade-offs
Small models fail differently from large ones: they are usually right on the common case and can be confidently wrong on edge cases. That makes evaluation and routing discipline more important than raw capability.
- Use JSON mode with a strict schema and validate every response.
- Track a confidence signal — model logprobs where available, or schema/field checks — and escalate on low confidence.
- Keep prompts short and explicit; small models benefit most from a clear task definition.
- Build a labelled sample of real inputs and re-run it after any prompt change.
- 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-2b
Switching is a model-name change on the OpenAI-compatible endpoint: {"model": "plugsky-gemma3-nano-2b", "messages": [{"role": "user", "content": "Classify this ticket as billing, technical or general."}], "response_format": {"type": "json_object"}}.
Shadow-test on your labelled sample, tune the confidence threshold, then route the easy majority to it. Keep a larger sibling configured as the escalation target.
Honest comparison
| Dimension | plugsky-gemma3-nano-2b | Mid-tier model (e.g. plugsky-pro) | Frontier-tier model |
|---|---|---|---|
| Best fit | Classification, routing, tagging at volume | General chat, agents and coding | Hard reasoning and analysis |
| Quality on narrow tasks | Good when the task is well defined | Strong | Highest |
| Latency profile | Fastest tier | Balanced | Slowest tier |
| Context class | 128K-class | 128K-class | 128K-class |
| Routing role | First hop | Default production tier | Escalation only |
| Failover | Automatic compact peer fallback | Automatic peer fallback | Automatic peer fallback |
Frequently asked questions
Is plugsky-gemma3-nano-2b free?
No — it is a paid-plan model. The free plan includes two free models, plugsky-micro and plugsky-lite. A 14-day full-access trial is available if you want to test paid models first.
What should I use it for?
Narrow, high-volume tasks: classification, intent routing, tagging, simple extraction and short FAQ responses. Escalate reasoning-heavy requests to a larger model.
What context window does it have?
It is 128K-class today. The exact live window and output limit are published per model at /models.
Can it return structured output?
Yes — JSON mode works on the OpenAI-compatible API, so you can validate responses against a schema. Check /models for the current live feature flags too.
Will a tiny model be accurate enough?
For well-defined, repetitive tasks, usually yes — provided you validate outputs and route low-confidence cases to a larger model. Test on a labelled sample before rolling out.
How is it different from plugsky-gemma3-nano-4b?
The 4B profile is the step up in the same compact family and the companion multimodal option. Start with the smallest model that passes your eval, and move up if it does not.
What happens if the upstream has an incident?
Requests retry through a backup upstream and same-profile compact peers without code changes. Live health is on the status page.
How is pricing structured?
Self-serve plans are flat monthly with fair-use usage and no per-token billing. See the live pricing page for current plans.