Research Qwen3.8 27B addition in words — A benchmark tested whether the local `Qwen3.8-27B-Q4_K_M.gguf` model could add positive integers and express exact results solely in English words, using 5,070 reasoning-disabled cases and a paired 169-case comparison with medium reasoning. Without reasoning, it achieved 23.57% numeric accuracy, with performance dropping from 97.04% for one- to three-digit operands to 6.44% for ten- to thirteen-digit operands, despite 96.17% format compliance.
Colin Frasier posted on Bluesky about an experiment he ran over two years ago using GPT-4o to see how well it could "compute the sum but return the answer in words" across increasingly large numbers. Here's the chart he shared of those results:
I'm confident GPT-4o didn't cheat and use a calculator, especially since it got so many of the calculations wrong, but I was inspired to run the experiment again on local hardware (a DGX Spark) to explore the effect in a fully controlled environment.
I pasted his image into a Codex Remote session (GPT-6 Astra) and had it run the same experiment using Qwen3.8-27B-Q4_K_M.gguf. Here's the result for a run of 30 attempts per combination with reasoning disabled:
Then I ran it again with reasoning enabled. This took a lot longer per pair, so instead of running 30 samples per square I ran just one - which results in a much less visually appealing heatmap since each square is either 100% or 0%:
It got the right answer in 167 out of 169 attempts, and since these were one-shot I'm confident a second run would produce different results here.
Here's a version of the report that includes the reasoning traces from some of those larger calculations, which include text like this:
Wait, let me redo this more carefully.
4,299,366,105,622
6,088,794,067,970
Let me align them:
4 2 9 9 3 6 6 1 0 5 6 2 2
6 0 8 8 7 9 4 0 6 7 9 7 0
Adding from right to left:
Position 1 (units): 2 + 0 = 2
Position 2 (tens): 2 + 7 = 9
Position 3 (hundreds): 6 + 9 = 15, write 5, carry 1