Spojit uses two kinds of models:Documentation Index
Fetch the complete documentation index at: https://docs.spojit.com/llms.txt
Use this file to discover all available pages before exploring further.
- Chat / agent models power agent mode in connector nodes and the AI chat sidebar.
- Embedding models turn documents into vectors so the Knowledge feature can do semantic search.
Chat / agent models
Anthropic Claude
| Model | Context | Strengths | Notes |
|---|---|---|---|
| Claude Opus 4.7 | 1M tokens | Top-tier reasoning, tool use, multi-step problem solving | Latest Claude release. Recommended for complex workflows. |
| Claude Opus 4.6 | 1M tokens | Same Opus-class capability as 4.7 | Current default for new workflows. Previous-generation Opus. |
| Claude Opus 4.5 | 200K tokens | Earlier Opus generation | Kept available for comparison; you generally want 4.6 or 4.7. |
| Claude Sonnet 4.6 | 1M tokens | Balanced quality and speed at lower cost than Opus | Good middle-ground for high-throughput agent steps. |
| Claude Haiku 4.5 | 200K tokens | Fastest and lowest cost in the Claude family | Best for simple steps, lookups, and high-volume runs. |
Google Gemini
| Model | Context | Strengths | Notes |
|---|---|---|---|
| Gemini 3.1 Pro | 2M tokens | Largest context window; strong reasoning | Currently in preview — capabilities may change. |
| Gemini 2.5 Pro | 1M tokens | Strong general-purpose reasoning | Stable alternative to Claude Opus when you prefer Google. |
| Gemini 2.5 Flash | 1M tokens | Very fast and very low cost | Best for high-volume simple steps. |
Embedding models
Embedding models convert text into high-dimensional vectors that capture semantic meaning. Spojit uses them whenever you upload a document to a Knowledge collection — and again at query time so the same vector space is used for search.| Model | Dimensions | Strengths | Notes |
|---|---|---|---|
| Gemini Embedding 001 | 3072 | Higher-quality semantic search; best recall on nuanced queries | Default for new collections. Higher cost per token. |
| Text Embedding 004 | 768 | Faster and much cheaper per token | Good choice for high-volume document ingestion when peak quality isn’t required. |
Once a collection is created with a given embedding model, every document and query in that collection must use the same model — vectors from different models aren’t comparable. Choose your embedding model when you create the collection.
Where model selection appears
Connector nodes (agent mode)
When configuring a connector node in agent mode, the Model dropdown in the properties panel lets you pick which model runs that step. If you don’t select one, the default model is used. Each connector node can use a different model — you’re not locked into one choice for the entire workflow.Chat sidebar
The chat sidebar has a model picker in the header bar. Select a model from the dropdown to change which model powers your chat session. Your selection persists across page reloads.Choosing a model
- Start with the default. Claude Opus 4.6 is a strong all-rounder and a good starting point for most workflows.
- Reach for Opus 4.7 for the hardest reasoning steps — multi-tool chains, ambiguous instructions, or long-context tasks.
- Use Sonnet 4.6 or Gemini 2.5 Flash for high-throughput steps where cost and latency matter more than peak reasoning quality.
- Use Haiku 4.5 for the simplest steps — short lookups, classification, simple formatting.
- Mix models in a single workflow. Different steps can use different models — pick the right tool for each step rather than committing to one model for the whole flow.