n8n Cohere Model nodeConfigure Cohere Model in n8n.
The n8n Cohere Model node never runs on its own. It sits beside a root node, plugs into its Model port, and answers the prompts that node sends. Version 1 exposes a single head parameter, Options, holding 3 fields. For builders wiring Cohere into a chain.
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What does the n8n Cohere Model node actually do?
It supplies the language model that a root node calls. In n8n's AI family, a root node (a chain, a classifier, an extractor) takes the workflow items through its main input, and sub-nodes clip onto its ports: one model, one memory, one parser. The Cohere Model node is one of those sub-nodes, and it occupies the Model port. Nothing else. It holds the credential and the generation settings, and hands back text.
First scenario: a support inbox summary. A chain reads the day's tickets, sends each body to Cohere through the Model port, and writes back a short recap. The Maximum Number of Tokens field is what keeps that recap short instead of letting the model ramble to the end of its budget.
Second scenario: classifying inbound messages before routing them. A text classifier node asks the model which bucket a message belongs to, and a low Sampling Temperature is what makes the same message land in the same bucket twice in a row. Downstream, the branch can post to Slack or append a row in Google Sheets.
Third scenario: pinning a specific Cohere model for a workflow that has to stay stable. The Model field takes the model name as a string, so a workflow keeps calling the same one even after someone else edits the node.
When to prefer something else: this node has no tools support, so it will not work with the AI Agent node. Connect it to the Basic LLM Chain instead. If the workflow needs a model that can call tools, a provider node such as OpenAI belongs on that port, and a search-shaped job may be better served by Perplexity.
The limits are worth knowing before building. Version 1 of the node exposes one head parameter and 3 optional fields, so anything finer than token budget, model name and temperature is not configurable here. Cohere bills its own API calls under its own terms; n8n adds nothing on top. Most models carry a context length of 2048 tokens, with the newest ones going further, which caps how much text a single call can carry.
What do you need to authenticate?
- 01
Create the Cohere credential
Open the Credentials menu in n8n and add the Cohere credential. n8n documents the authentication details for this node on its own credentials page for Cohere, and that page is the reference for what the credential asks for. You create it once: every workflow on the instance can then reuse it, on n8n Cloud and on a self-hosted instance alike, with no second setup.
- 02
Select it on the node
Drop the Cohere Model node on the canvas and pick the credential you just saved from the list at the top of its panel. The node carries no Authentication selector: there is one credential type and one choice to make. If several Cohere credentials exist on the instance, the name you gave each one is the only thing telling them apart, so name them after the account they belong to.
- 03
Wire the port and run the root node
Drag from the node's connector to the Model port of a root node, the Basic LLM Chain being the safe starting point. A sub-node has no main input and no run button of its own, so testing means executing the root node and reading what comes back. A credential problem shows up there, on the root node's execution, not on the Cohere Model node itself.
Where does this node plug in?
A root node runs the workflow items; a sub-node like this one only answers when that root node calls it, through the port below.
Output (what it plugs into)
- Model
ai_languageModel
01Model
This is the output side of the node: it exposes a language model that a root node consumes. The root node sends the prompt, this node returns the completion, and the workflow items never pass through it.
Key parameters
- Required: a root node with an open Model port, since the Cohere Model node produces nothing on its own.
- Basic LLM Chain: the node n8n's documentation points to for this model, and the one to start with.
- Question and Answer Chain: answers a question against retrieved documents.
- Text Classifier: sorts each incoming item into one of your categories.
Which parameters can you set?
The Cohere Model node has one parameter. For each one: the node as you configure it in n8n, what the parameter changes, and our field notes.
Options
optionsWhat you see in n8n
Notes & use cases
Options is the only head parameter, a collection that stays empty until you use Add Option. Left alone, the node runs on the provider's defaults.
Key parameters
- Maximum Number of Tokens: the maximum number of tokens to generate in the completion. Most models have a context length of 2048 tokens, except the newest ones.
- Model: the name of the model to use, as a string.
- Sampling Temperature: controls randomness. Lowering it gives less random completions, and near zero the model becomes deterministic and repetitive.
Need help automating Cohere Model with n8n?
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Questions builders ask next
01Is the Cohere Model node free in n8n?
02What do you need for it to work?
03What are the limits of the n8n Cohere Model node?
04What should you plug in to get started?
05n8n or Make for this kind of AI workflow?
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