How to split receipt totals with Nemotron
codes_astro built a Nemotron 3.5 Super VL pipeline that reads receipts into JSON, calls tools to allocate tax and tips, and validates the split. Their 300-receipt test found locale-sensitive numbers and reasoning settings materially changed accuracy and latency.
reddit.comSuggested prompt
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Read [receipt image] into structured JSON using the receipt's locale and number format. Allocate items to [people] and use deterministic tools to split tax and tips. Validate that all shares sum to the receipt total. Flag ambiguous text or ownership rather than guessing, and compare results with reasoning enabled and disabled.
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Sourced account- Source author
- u/codes_astro
- Original account
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- Reported result
- codes_astro reports 91% accuracy with reasoning off and 98% with it on across 300 receipts. Real-receipt accuracy rose from 84.7% to 97.3%, while median latency increased from 4.7s to 13.6s. Explicit locale handling had earlier reduced number-format errors from 44 to one. Results are author-reported.
- Human involvement
- The author built the pipeline, assembled the test set, found locale errors and changed number-format handling. They chose the reasoning settings and evaluated results. The post does not describe production review or payment execution.
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How the agent did it
- The author built a pipeline to read receipt images into structured JSON.
- The model used function calls to allocate tax and tips and validate the final split.
- The author tested 150 synthetic receipts and 150 real receipt photographs.
Reddit author codes_astro tested Nemotron 3.5 Super VL on a specific task: reading receipts and calculating who owed what. They built a pipeline that turns receipt images into structured JSON, uses function calls to allocate tax and tips, then validates the final split. The test contained 150 synthetic receipts and 150 real receipt photographs.
The author's first reported problem was number interpretation rather than arithmetic. Indonesian receipt notation such as 40.000 was initially read as 40 rather than 40,000. Adding explicit number-format handling raised reported real-receipt accuracy from 57.3% to 84.7% and reduced those formatting errors from 44 to one. With reasoning disabled, overall accuracy was 91%, including 97.3% on synthetic receipts.
Enabling reasoning on the same 300 receipts raised reported overall accuracy to 98% and real-receipt accuracy to 97.3%. Median latency rose from 4.7 seconds to 13.6 seconds. The author identifies this as their own application-level evaluation and says they released the benchmark, methodology and code. Those materials and results have not been independently reproduced for this entry.
Who it's for: builders testing vision-based expense or shared-bill workflows. The observed artifact is a working receipt-processing pipeline and completed comparison. The result does not establish that unattended processing is reliable for every locale, receipt format or financial use.
Prompt
Suggested prompt (written by Agents In Use, not the author's)
Source: Reddit · u/codes_astro
Frequently asked questions
What caused the largest initial real-receipt problem?
The author found that Indonesian thousands separators were being misread. Explicit number-format handling reduced those errors from 44 to one in their test and raised real-receipt accuracy from 57.3% to 84.7%.
What changed when reasoning was enabled?
On the same 300 receipts, the author reports overall accuracy rising from 91% to 98%, while median latency increased from 4.7 seconds to 13.6 seconds. These are application-level results, not independently reproduced here.
More details
Remaining steps
- They added explicit number-format handling after finding Indonesian thousands separators were misread.
- They repeated the same 300-receipt test with reasoning enabled and compared accuracy with median latency.
About this account
The vision model interprets receipt content and uses function calls in a bill-splitting pipeline. Tools allocate tax and tips and validate final amounts. The author reports completed tests rather than an unattended financial deployment.
Firsthand pipeline test by u/codes_astro: https://www.reddit.com/r/aiagents/comments/1x6yfmz/i_tested_nemotron_35_super_vl_on_real_multimodal/. Full public RSS evidence re-read on 16 October 2026; repaired-policy body gate passed at 1.00. Accuracy and latency figures are the author's, not independent verification. Suggested prompt by Agents In Use, not the author's original prompt. Original title-stage rejection is preserved.
Credit: u/codes_astro on Reddit
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Tools and topics
Nemotron 3.5 Super VL · structured JSON · tax and tip function calls · split validation
receipt processing · bill splitting · vision models · locale handling
Editorial completeness: 100/100. Measures documented fields, not agent performance or verified outcomes.