research · 2026-07-15 · 2 min read
GPT-5.6 vs GPT-5.1: what changed in our test
Our classification results show fewer missed cases, more false positives, and a cost trade-off to evaluate before switching.
We compared GPT-5.6 with GPT-5.1 on an internal binary classification task before deciding whether to change models. The results show why it is useful to look at the types of errors, not just an overall score.
Results on our test task
Here is the actual comparison on our binary task:
| Model | Accuracy | F1 | Precision | Recall | FP | FN |
|---|
| baseline: gpt-5.1 | 92.29% | 94.70% | 94.70% | 94.70% | 55 | 55 |
| candidate: gpt-5.6 | 93.13% | 95.35% | 93.84% | 96.91% | 66 | 32 |
GPT-5.6 reduced false negatives from 55 to 32, while false positives increased from 55 to 66. Whether that trade-off is useful depends on the cost of each error in the workflow. These are results from our test, not a general model ranking; the full dataset and evaluation method are not published here.
Cost in this setup
The cost figures below describe this test setup and our estimate at the time, not current provider pricing.
The tradeoff is cost. GPT-5.6 is about twice as expensive as 5.1 in this setup, and across the stack we expect total cost to rise by roughly 20-30%. So the question is not “is the new model better?” The real question is whether the improvement is worth the extra spend in our actual workflow. That depends on how painful false negatives are compared with the added cost and the extra review work.
What to check before switching
Test the existing prompt first, then change it only where the evaluation shows a problem. Compare versions on the same cases and check output format, missed cases, and unnecessary follow-up work. A model change should earn its place through better results on the task.
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