Key facts
| Model class | Long-context profile in the Plugsky catalogue |
| Best for | Document analysis, research synthesis and bilingual English plus Chinese work |
| 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 — keep your SDK |
| Availability | Routed with same-profile fallback; live upstream is shown at /models |
| Product status | Live |
TL;DR
- Built for long prompts: multi-document analysis and research synthesis.
- Useful for bilingual English plus Chinese content and code explanations.
- Context limits and current engine are published live at /models.
- Same-profile fallback keeps long-running research workflows alive.
- Chunk strategically even with a large window — relevance still beats volume.
How it works, step by step
- Read the live card at /models for the current context window and feature flags.
- Assemble a realistic long prompt: several documents plus a clear task.
- Run it on plugsky-kimi and on your current model for comparison.
- Check whether quality holds at the edges of the context, not just the middle.
- Use JSON mode for structured summaries and citations.
- Switch the model name and keep a general-purpose model for short prompts.
- Monitor latency and cost as prompt sizes grow in production.
Try it yourself
Open the LLM token calculator →
What plugsky-kimi is
plugsky-kimi is the long-context profile in the Plugsky catalogue. Long-context models are for prompts that carry a lot of material: contracts, research papers, codebases, support histories and meeting archives. Instead of pre-chunking everything into a retrieval pipeline, you can hand the model a large set of documents and ask for synthesis, comparison or extraction.
The exact context window and the engine currently serving the profile are published on the live card at /models. Because long-context routing can change, verify both before designing a pipeline around a fixed number.
When to choose it
Choose plugsky-kimi for document-heavy work: summarising a set of reports, extracting obligations from contracts, comparing several versions of a policy, or answering questions that require holding many pages at once. It is also a sensible fit for bilingual English plus Chinese material and for explaining code with a lot of surrounding context.
For short prompts, a general-purpose model such as plugsky-pro is faster and simpler. For extreme document workloads, compare with plugsky-longctx and choose on your own evaluation set.
Long-context trade-offs
A big window does not remove the need for good context engineering. Models still attend unevenly across very long inputs, and latency grows with prompt size.
- Put the task and the most relevant material first, bulk appendices last.
- Ask for citations or section references so you can verify claims against the source.
- Split by document when the question is per-document; use long context for cross-document reasoning.
- Measure quality at different prompt sizes, not just at 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-kimi
Switching is one model name on the OpenAI-compatible endpoint: {"model": "plugsky-kimi", "messages": [{"role": "user", "content": ".
Test on real document bundles, verify that the model cites sections correctly, then route long-prompt traffic to it. Keep a short-prompt model as the default for the rest of your traffic.
Honest comparison
| Dimension | plugsky-kimi | plugsky-longctx | Retrieval plus short-context model |
|---|---|---|---|
| Best fit | Cross-document synthesis and bilingual content | Bulk long-context workloads | Targeted Q&A over indexed corpora |
| Context class | 128K-class | 128K-class | Smaller prompts, retrieved passages |
| Cost profile | Scales with prompt size | Scales with prompt size | Scales with retrieval calls |
| Setup effort | Send documents directly | Send documents directly | Chunking, embeddings, index operations |
| Citation quality | Model can cite sections from the prompt | Model can cite sections from the prompt | Citations come from retrieved chunks |
| Failover | Automatic same-profile fallback | Automatic same-profile fallback | Depends on your stack |
Frequently asked questions
Is plugsky-kimi 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 lets you evaluate paid models first.
What is its context window?
It is 128K-class today, but the exact live window and output limit are published per model at /models. Check there before designing long-prompt pipelines.
Does it handle Chinese content?
Yes — bilingual English plus Chinese work is one of the profile's intended uses, alongside document analysis and research synthesis.
Should I still use RAG with a long-context model?
Often yes. Retrieval keeps prompts focused and auditable; long context is best for cross-document reasoning that retrieval alone cannot assemble.
Does it support tools and JSON mode?
Yes — function calling, streaming and JSON mode are part of the shared OpenAI-compatible surface.
How do I know which engine serves the profile?
The live model card at /models shows the upstream currently serving plugsky-kimi. Treat that page as the source of truth.
What happens if the upstream degrades?
The platform retries through same-profile fallback peers so long-running workflows complete. 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.