GPT-6 Luna
Four correct rows. One wrong decision.
The proposed amount and the calculated total disagreed. Inspect why the evaluator expected escalation after all four rows matched.
Public research · Indian GST
We test how AI models handle Indian GST reconciliation. Follow each model from the records it reads to the decision it makes, and see exactly where it succeeds or fails.
GST reconciliation
01 / Research index
Each note opens a recorded attempt from its original release. Follow the actions and compare the submitted fields with the expected result.
GPT-6 Luna
The proposed amount and the calculated total disagreed. Inspect why the evaluator expected escalation after all four rows matched.
GLM 5.3 Flash
In the earlier run, GLM used all six steps reading the same two sources. Compare that trace with the same case under a protocol that allows each read only once.
Inspect the read-once run ↗DeepSeek V4.1 Flash
The decision and ₹900 total matched. On a row contributing nothing, DeepSeek supplied null where the published format required “0.00”.
The model reads the synthetic records and supplied rules, then submits its decision. A deterministic evaluator checks each required field. The release contains one attempt per model and case.
Each scenario pairs synthetic records with a dated statutory source. We score the calculation, the individual rows and the decision separately, so a correct total can still reveal a missed escalation.
Explore the scoring method and case notes ↗03 / Collaborate
Have a model or finance workflow to test? Bring one decision your team cares about. We can agree on the evidence, run the evaluation and inspect the failures together.
04 / Expert network
We’re expanding the range of GST cases we test. We’re recruiting practising Chartered Accountants and CA Final candidates for paid projects to develop and review new cases for private evaluations.