Do you need a summary, or every response coded?

For a sense of the themes, a general assistant is enough. To cross codes with the rest of the questionnaire, you need every response coded and back in your data.

Does the result need to be the same if you run it again?

OpenAI's help center notes that ChatGPT may choose an analysis method that doesn't match your intent on the first try, and its API documentation describes model outputs as non-deterministic by default. If you will compare waves, ask how the codebook is kept fixed.

Do you measure in waves and need the same codebook in all of them?

Ask to see how the codebook carries from one wave to the next, on your own data.

Where is your data processed?

Check which ChatGPT plan you use and its data policy, especially with customer responses. Survey Coder uses Claude models, by Anthropic. Always ask.

How do I compare it with what I already did?

Take the open ends from a study you already analyzed with ChatGPT, upload them to Survey Coder — the first 250 responses are free — and compare the codebook and the coded file with what you got.

Comparison

Survey Coder vs ChatGPT

Both use language models: ChatGPT is a general assistant, and Survey Coder uses the same kind of models underneath for a single job, coding open-ended survey responses. The difference isn't the intelligence: it's where your judgement is applied and what survives afterwards.

Where they are alike

Both use large language models: ChatGPT uses OpenAI's; Survey Coder uses Anthropic's Claude models.
Both take an Excel or CSV file with your responses.
With both you can propose a codebook from the data.

Where they differ

Survey CoderChatGPT

Where your judgement goes

Survey CoderIn the codebook: AI proposes it and codes; you merge, rename and set the threshold, and that codebook is the single reference for the whole file and later waves.

ChatGPTIn the conversation: your rules live in the prompt and you review what comes back. It can apply a codebook saved in a project to a whole file, but maintaining it and checking consistency between runs is up to you.

What doesn't fit

Survey CoderResponses that fit no code stay unclassified or in "Other", visible to review and reassign; a new theme only gets its own code once it reaches your mention threshold.

ChatGPTYou can keep the rules and codebook in a project. Define how unmatched answers should be flagged, and validate that each run follows those rules.

What you take away

Survey CoderThe codes attached to each respondent, as columns in your .sav or an Excel file, ready to cross by age, region or segment, with multiple mentions in a countable shape. From a .sav, also the tab book.

ChatGPTAnswers in the conversation, and spreadsheets it can build or edit, including through its Excel add-in. Checking each row is up to you.

Waves of an ongoing program

Survey CoderThe next wave is coded against the approved codebook, with the same code numbers; by default, anything new is flagged for your approval.

ChatGPTA project keeps instructions and files, your codebook for example, and depending on plan and memory settings references earlier chats; coding every wave the same way is up to you.

How you pay

Survey CoderPer coded response, one-time purchase. Credits never expire.

ChatGPTA free plan and paid per-user plans, such as Plus.

What we say about ChatGPT comes from OpenAI's help center and documentation, as of September 23, 2026. Projects and data analysis reviewed on October 7, 2026.

When ChatGPT is enough

For exploratory work on a few hundred open ends, ChatGPT is a good tool: it gives you a first sense of the themes or a draft codebook. When you need every response coded, the codes in your file and the same codebook in every wave, something built for that is a better fit.

When Survey Coder is the better fit

If your work is a survey program — a monthly NPS, a brand tracker, concept tests — and the codes need to come back into your data as variables and end up in tables, wave after wave with the same codebook. That is what it is built for: AI does the first pass and your team puts its judgement where it's actually needed.

What one company found when it compared

A services company compared our coding of its NPS with its own, across every response. They agreed on around 96% — and when it reviewed the differences, it found several came from errors in its own coding.

A typical study of 5,000 to 8,000 comments is coded in under an hour.

Five questions that decide it

You do not need to believe either page. These questions decide it, and they apply to any vendor — including us.

1
Do you need a summary, or every response coded?
For a sense of the themes, a general assistant is enough. To cross codes with the rest of the questionnaire, you need every response coded and back in your data.
2
Does the result need to be the same if you run it again?
OpenAI's help center notes that ChatGPT may choose an analysis method that doesn't match your intent on the first try, and its API documentation describes model outputs as non-deterministic by default. If you will compare waves, ask how the codebook is kept fixed.
3
Do you measure in waves and need the same codebook in all of them?
Ask to see how the codebook carries from one wave to the next, on your own data.
4
Where is your data processed?
Check which ChatGPT plan you use and its data policy, especially with customer responses. Survey Coder uses Claude models, by Anthropic. Always ask.
5
How do I compare it with what I already did?
Take the open ends from a study you already analyzed with ChatGPT, upload them to Survey Coder — the first 250 responses are free — and compare the codebook and the coded file with what you got.

Try it on your own comments

250 responses free, no card. Upload a spreadsheet from your last NPS and compare the result with what you have today.

Try it free Request a pilot