DeepSeek-V4-Flash-0731: When Low is higher than High

Reddit r/LocalLLaMA Models

Summary

A developer benchmarks DeepSeek-V4-Flash-0731 across four reasoning effort modes (none, low, high, max), finding that Low mode is surprisingly verbose and that OpenRouter has a bug affecting reasoning effort modes.

I decided to test a few questions against DeepSeek-V4-Flash-0731. Locally, I was running Unsloth's UD-Q2_K_XL quant. After I saw the surprising shape of the results, I tested against DeepSeek's official API to confirm that I didn't do anything wrong. For anyone using OpenRouter, be aware that there is a significant bug that is breaking reasoning effort modes. I ran into that while trying to validate my local results. DeepSeek-V4-Flash-0731 supports four different effort modes, consisting of no reasoning, low, high, and max. We can also see how those are communicated to the model. As I found out, Low is surprisingly verbose. Averaged across 20 requests per mode, here is how many tokens were used by each mode: Mode Local Q2 total / reasoning / final DeepSeek API total / reasoning / final None 801.7 / 0 / 801.7 948.9 / 0 / 948.9 Low 1,227.5 / 874.4 / 353.2 1,349.2 / 889.6 / 459.7 High 605.8 / 410.5 / 195.4 481.5 / 253.9 / 227.7 Max 1,301.4 / 1,031.8 / 269.6 698.7 / 473.9 / 224.8 I really wish that DeepSeek and Artificial Analysis had posted benchmarks for all of the effort modes, instead of only max.
Original Article

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