Key facts
| Model class | Long-context workhorse in the Plugsky catalogue |
| Best for | Large document analysis, codebase Q&A, policy review and audits |
| Context class | 128K-class window; live limits are published per model |
| Capabilities | Streaming, function calling, JSON mode and long-context inputs |
| Pricing tier | Paid-plan model; free plan covers plugsky-micro and plugsky-lite |
| API | OpenAI-compatible /v1/chat/completions — no SDK changes |
| Routing role | Also used as a fallback peer in long-context routing |
| Product status | Live |
TL;DR
- Built for bulk context: document bundles, codebases and compliance material.
- A fallback peer for other long-context models, so pipelines stay available.
- Context engineering matters as much as window size — structure your prompt.
- Pair with retrieval when questions are targeted rather than cross-document.
- Check /models for the exact live window before sizing prompts.
How it works, step by step
- Read the live card at /models for the current context window and features.
- Assemble the real bundle: documents, code files or policy set plus the task.
- Structure the prompt with the task first and grouped attachments after it.
- Run your comparison questions and check answers against the source material.
- Test at several prompt sizes to see where quality changes.
- Switch long-prompt traffic to plugsky-longctx and keep a shorter-prompt default.
- Monitor latency and fallback events as prompt sizes grow.
Try it yourself
Open the LLM token calculator →
What plugsky-longctx is
plugsky-longctx is the long-context workhorse in the Plugsky catalogue. Its purpose is to hold a lot of material at once and reason across it: contract bundles, research sets, codebases, incident histories and audit trails. It speaks the standard OpenAI-compatible chat API with streaming, tool calling and JSON mode, so long-context capability is available to existing applications as a model-name change.
It also participates in long-context routing as a fallback peer, which means other long-prompt models can fail over to it. The live card at /models shows the current window, limits and routing details.
When to choose it
Choose plugsky-longctx when the task is inherently cross-document: comparing versions of an agreement, tracing a requirement across a specification set, answering questions about a repository, or summarising a case file. These tasks are awkward for retrieval pipelines because the answer depends on material spread across many sources.
When questions are targeted and the corpus is stable, retrieval plus a shorter-prompt model is often cheaper and more auditable. Compare with plugsky-kimi for bilingual or research-heavy synthesis, and choose on measured accuracy.
Making long context work
Long windows fail quietly: the model may answer from the middle of the prompt and miss the edges. Structure is your defence.
- State the task and output format before the attachments.
- Group and label documents so the model can cite them.
- Ask for evidence — section numbers, file paths, quotes — and verify a sample.
- Delete boilerplate before sending; irrelevant text dilutes attention.
- Measure latency at real prompt sizes, not just the maximum.
- Self-serve plans are flat monthly with fair-use usage — see the live pricing page for current plans.
How to switch to plugsky-longctx
Switching is one model name on the OpenAI-compatible endpoint: {"model": "plugsky-longctx", "messages": [{"role": "user", "content": ".
Start with shadow traffic on a sample of real bundles, verify citations against source sections, then route the long-prompt slice of your workload to it. Keep a shorter-context model as the default for everything else.
Honest comparison
| Dimension | plugsky-longctx | plugsky-kimi | Retrieval pipeline |
|---|---|---|---|
| Best fit | Cross-document and codebase analysis | Research synthesis and bilingual content | Targeted Q&A over indexed corpora |
| Context class | 128K-class | 128K-class | Small retrieved passages |
| Setup | Send documents directly | Send documents directly | Chunking, embeddings, index maintenance |
| Auditability | Cite sections inside the prompt | Cite sections inside the prompt | Citations from retrieved chunks |
| Cost driver | Prompt size | Prompt size | Retrieval and generation calls |
| Routing role | Long-context workhorse and fallback peer | Long-context specialist | Application-layer choice |
Frequently asked questions
Is plugsky-longctx 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 is available for paid tiers.
What context window does it have?
It is 128K-class today. The exact live window and output limit are published per model at /models, which is the authoritative source.
Should I use long context or RAG?
Use long context for cross-document reasoning where the answer spans sources. Use retrieval for targeted questions over large, stable corpora — ideally combine both.
Does it support tools and JSON mode?
Yes — streaming, function calling and JSON mode are part of the shared OpenAI-compatible surface.
How do I keep answers accurate on huge prompts?
Structure the prompt with the task first, label your sources, and require citations. Spot-check answers against the original material before trusting a workflow.
What does the fallback-peer role mean?
Long-context routing can fail over to plugsky-longctx when another long-prompt model is degraded, which keeps document pipelines running. Live routing is shown at /models.
Can it read code repositories?
It handles large code prompts, which makes repository-level questions and review possible. Use labelled file boundaries so answers can cite paths.
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.