Answer a few questions to get a personalized recommendation.
The Best Model for RAG selector recommends an LLM for retrieval-augmented generation based on two inputs: the context window your documents need and your budget tier. Choose from short, medium, long or very long context and from free, low-cost, mid-range or best-quality tiers, and the page returns a recommendation. It helps developers pick a starting model for a RAG pipeline before benchmarking candidates on their own data. No sign-up required.
It depends on how many retrieved chunks you pass with each query and how large they are. Short-document Q&A may fit in 8K, while multi-document analysis can need 128K or more. Larger windows cost more per call, so retrieve fewer, better chunks where you can and reserve very long context for when it is genuinely needed.
No. Retrieval quality usually matters more than model size: if the wrong chunks are retrieved, a larger model will still answer poorly. Choose a model that follows instructions well, supports tool or JSON output if your pipeline needs it, and fits your latency and cost budget, then improve chunking and search.
Yes. Plugsky offers chat, embedding and reranking APIs through an OpenAI-compatible interface, covering 30+ models. You can keep your existing RAG code and change the base URL and key. The free plan includes 2 free models, and there is a 14-day full-access trial.
Canonical pricing and plans: plugsky.com/#sec-pricing · Terms · SLA · Docs