Head-to-head comparison across 3benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
DeepSeek V4 Flash Base
29
Qwen3.7 Max
93
Verified leaderboard positions: DeepSeek V4 Flash Base unranked · Qwen3.7 Max #2
Pick Qwen3.7 Max if you want the stronger benchmark profile. DeepSeek V4 Flash Base only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
Reasoning
+45.7 difference
Knowledge
+19.0 difference
Multilingual
+1.3 difference
DeepSeek V4 Flash Base
Qwen3.7 Max
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Pick Qwen3.7 Max if you want the stronger benchmark profile. DeepSeek V4 Flash Base only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
Qwen3.7 Max is clearly ahead on the provisional aggregate, 93 to 29. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Qwen3.7 Max's sharpest advantage is in reasoning, where it averages 90.4 against 44.7. The single biggest benchmark swing on the page is SuperGPQA, 46.5% to 73.6%.
Qwen3.7 Max 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.
Qwen3.7 Max is ahead on BenchLM's provisional leaderboard, 93 to 29. The biggest single separator in this matchup is SuperGPQA, where the scores are 46.5% and 73.6%.
Qwen3.7 Max has the edge for knowledge tasks in this comparison, averaging 71.2 versus 52.2. Inside this category, SuperGPQA is the benchmark that creates the most daylight between them.
Qwen3.7 Max has the edge for reasoning in this comparison, averaging 90.4 versus 44.7. DeepSeek V4 Flash Base stays close enough that the answer can still flip depending on your workload.
Qwen3.7 Max has the edge for multilingual tasks in this comparison, averaging 87 versus 85.7. DeepSeek V4 Flash Base stays close enough that the answer can still flip depending on your workload.
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