CASE STUDY / Banking intent classification
An 8B specialist. Better accuracy. A fraction of the cost.
Isoquant raised banking intent accuracy from 74.7% to 91.8%, while cutting inference cost by 91.7%.
The task: understand what the customer needs.
A short banking message can hide an important distinction. A pending transfer, a missing transfer and a transfer to the wrong account require different responses. This workload asks a model to classify each request into one of 77 banking intents.
The GPT-5.4 mini baseline correctly classified 2,299 of 3,079 requests. The goal was to reduce inference cost without giving up baseline task quality.
A specialist built for the work.
Isoquant evaluated a Qwen3-8B specialist trained on labeled banking examples against the original general-purpose configuration. Specialist training taught the smaller model the distinctions that matter for this workload.
The final selection cycle reused the trained checkpoint and ran a fresh, matched comparison. It did not require an additional training run.
Lower cost. Higher accuracy. Faster completion.
The specialist answered 526 more requests correctly, lifting accuracy from 74.7% to 91.8%. Estimated inference cost fell by 91.7%, and mean completion latency fell from 931 ms to 588 ms.
That is a win across all three dimensions: cost, quality and latency. The specialist became the saved recommendation for this workload.
By the numbers.
| Metric | Baseline | With Isoquant |
|---|---|---|
| Intent accuracy | 74.7% | 91.8% |
| Correct / total | 2,299 / 3,079 | 2,825 / 3,079 |
| Inference cost / 1,000 tasks | $0.373 | $0.031 |
| Mean completion latency | 0.931 s | 0.588 s |