Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
DeepSeek V4 Flash Base
31
Qwen3.6-27B
75
Verified leaderboard positions: DeepSeek V4 Flash Base unranked · Qwen3.6-27B #13
Pick Qwen3.6-27B if you want the stronger benchmark profile. DeepSeek V4 Flash Base 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.
Knowledge
+10.0 difference
DeepSeek V4 Flash Base
Qwen3.6-27B
$null / $null
$0 / $0
N/A
N/A
N/A
N/A
1M
262K
Pick Qwen3.6-27B if you want the stronger benchmark profile. DeepSeek V4 Flash Base 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.
Qwen3.6-27B is clearly ahead on the provisional aggregate, 75 to 31. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Qwen3.6-27B's sharpest advantage is in knowledge, where it averages 62.2 against 52.2. The single biggest benchmark swing on the page is SuperGPQA, 46.5% to 66%.
Qwen3.6-27B is the reasoning model in the pair, while DeepSeek V4 Flash Base 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 Base gives you the larger context window at 1M, compared with 262K for Qwen3.6-27B.
Qwen3.6-27B is ahead on BenchLM's provisional leaderboard, 75 to 31. The biggest single separator in this matchup is SuperGPQA, where the scores are 46.5% and 66%.
Qwen3.6-27B has the edge for knowledge tasks in this comparison, averaging 62.2 versus 52.2. Inside this category, SuperGPQA is the benchmark that creates the most daylight between them.
Estimates at 50,000 req/day · 1000 tokens/req average.
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