Send the same prompt twice, get two different answers. Temperature zero reduces the variation but does not eliminate it, because model inference is not guaranteed to be deterministic even at zero.
This turns up constantly: a workflow extracts fields from a document, works perfectly in testing, and then produces a slightly different shape in production. Same input, same prompt, different output. The instinct is that something in n8n is passing different data. Usually nothing is.
What follows is what each knob actually does, and the design change that makes the question stop mattering.
Table of contents
- Temperature is a sampling control, not a determinism switch
- Seed, and what it is worth
- Sources of variation that are not the model
- Constrain the output rather than chasing the sampler
- Validate, then decide what happens when it fails
- Caching identical work
- How this fits the rest of the stack
- FAQ
Temperature is a sampling control, not a determinism switch
Temperature scales how much randomness goes into choosing each next token. At 1.0 the model samples fairly freely; at 0.0 it takes the highest-probability token every time.
In the n8n model nodes this is exposed as Output Randomness or Temperature, typically on a 0.0 to 1.0 scale. Setting it to 0 is the correct first move for any extraction, classification or routing task. There is no upside to creative variation when you are pulling an invoice number out of a document.
But temperature 0 gives you greedy decoding, not deterministic inference. Two tokens can have near-identical probabilities, and which one wins depends on floating-point arithmetic that varies with batching, hardware and provider-side model updates. In practice temperature 0 gets you from wildly variable to mostly stable, which is a large improvement and not a guarantee.
Seed, and what it is worth
Some providers expose a seed parameter that fixes the random draw. Same seed plus same input plus same model version gives you a much stronger repeatability claim, and where the n8n node surfaces it, set it.
The caveats are real, though. Not every provider supports it, not every n8n model node exposes it, and it holds only while the underlying model version is unchanged. Providers update models behind stable-looking names, and when they do, your seeded output changes with no action on your side.
Treat seed as variance reduction, not a contract. If a workflow’s correctness depends on byte-identical model output, the design is wrong regardless of what parameters you set.
Sources of variation that are not the model
Before blaming inference, rule these out — in practice they explain a good share of reported cases:
- The prompt is not identical. Expressions interpolating a timestamp, an execution ID, or an item’s position change the prompt on every run. Pin the actual string sent by logging it.
- Conversation memory is attached. A memory node means run two carries run one’s context. Same visible input, different actual input.
- The input data differs subtly. Trailing whitespace, a different field order after a Merge node, or an upstream node returning items in a new order.
- The model version moved. A provider updated the model behind the alias you are calling.
Log the exact payload sent to the model for a few executions and compare them character by character. Roughly half the time the inputs were never actually identical.
Constrain the output rather than chasing the sampler
The durable fix is to stop needing identical text. What workflows almost always need is identical structure — the same fields, the same types — and that is enforceable.
Use a structured output parser under the model node with an explicit schema. Then the model’s freedom is bounded to the values, and the shape is fixed:
{
"type": "object",
"properties": {
"invoice_number": { "type": "string" },
"total": { "type": "number" },
"currency": { "type": "string", "enum": ["USD", "EUR", "GBP"] },
"confidence": { "type": "number" }
},
"required": ["invoice_number", "total", "currency"]
}The enum is the point. A free-text currency field will eventually return US Dollars, usd and $ across three runs; an enum cannot. Constrain every field that has a known value set, and the remaining variation stops being a problem.
Validate, then decide what happens when it fails
A schema tells the model what to return. It does not promise the model complies, so validate after parsing:
const out = $input.first().json;
const valid =
typeof out.invoice_number === 'string' && out.invoice_number.length > 2 &&
typeof out.total === 'number' && out.total > 0 &&
['USD', 'EUR', 'GBP'].includes(out.currency);
return [{ json: { ...out, valid } }];Route on valid with an IF node. Invalid results go to a retry, or to a human queue, or to a deterministic fallback — never straight into the database.
For high-stakes extraction, run the call twice and compare the parsed structures. Agreement is a reasonable confidence signal; disagreement routes to review. It doubles the cost of that step and is still cheaper than a wrong number in an accounts export.
Caching identical work
If the same input genuinely recurs — the same document reprocessed, the same query repeated — the strongest determinism guarantee available is not calling the model twice.
Hash the input, look it up in a database table, return the stored result on a hit and call the model only on a miss. The output for a given input is then identical by construction, permanently, regardless of what the provider does to the model next month. It also cuts the bill.
That needs a database next to the workflow. On RunxBuild, n8n runs as a managed tool with a managed Postgres or MySQL available on the same platform, which is what makes the cache table a five-minute addition rather than a project.
How this fits the rest of the stack
Set temperature to zero, set a seed where the node offers one, verify the input really is identical, and then stop trying to make inference deterministic — constrain the output schema and validate the result instead. For genuinely repeated inputs, a cache table gives you the guarantee the sampler cannot. That means an n8n instance with a database beside it, and the RunxBuild hosting calculator shows what the pair costs before you design around it.
Useful related references:
- n8n vs Make: Where the Complexity Goes
- n8n Use Cases: What It Is Genuinely Good At, and What It Is Not
- n8n Alternatives: The Honest Comparison by What You Are Escaping
- Services on RunxBuild
FAQ
Does temperature 0 guarantee identical output?
No. It switches to greedy decoding, which removes deliberate sampling randomness, but floating-point differences from batching and hardware can still flip between near-equal candidate tokens. It is a large reduction in variance, not a guarantee.
What temperature should I use for data extraction?
Zero. Creative variation has no value when pulling an invoice number or a date out of a document, and every point of temperature above zero is a chance to get a different answer to the same question.
Why does my n8n workflow give different answers to the same input?
Check whether the input is genuinely identical first — interpolated timestamps, attached conversation memory, changed item order and provider-side model updates all produce different real inputs from apparently identical visible ones. Log the exact payload sent for several runs and compare.
Does setting a seed make output reproducible?
It helps considerably where the provider and node support it, but it holds only while the model version is unchanged. Providers update models behind stable aliases, so treat seed as variance reduction rather than a contract.
How do I make a workflow reliable if the model is not deterministic?
Stop depending on identical text. Use a structured output parser with an explicit schema and enums for known value sets, validate the parsed result, and route failures to retry or human review. For repeated inputs, cache results keyed on an input hash.