DeepSeek V4 Flash vs Qwen3.5-27B

Pick Qwen3.5-27B if you want the stronger benchmark profile. DeepSeek V4 Flash only becomes the better choice if you need the larger 1M context window or you would rather avoid the extra latency and token burn of a reasoning model.

Qwen3.5-27B is clearly ahead on the provisional aggregate, 64 to 59. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.

Qwen3.5-27B's sharpest advantage is in knowledge, where it averages 80.6 against 45.2. The single biggest benchmark swing on the page is GPQA, 71.2% to 85.5%.

DeepSeek V4 Flash is also the more expensive model on tokens at $0.14 input / $0.28 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Qwen3.5-27B. That is roughly Infinityx on output cost alone. Qwen3.5-27B is the reasoning model in the pair, while DeepSeek V4 Flash is not. That usually helps on harder chain-of-thought-heavy tests, but it can also mean more latency and more token spend in real use. DeepSeek V4 Flash gives you the larger context window at 1M, compared with 262K for Qwen3.5-27B.