Head-to-head comparison across 3benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
DeepSeek V4 Flash
58
Qwen3.7 Max
93
Verified leaderboard positions: DeepSeek V4 Flash #25 · Qwen3.7 Max #2
Pick Qwen3.7 Max if you want the stronger benchmark profile. DeepSeek V4 Flash only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
Agentic
+20.6 difference
Coding
+16.5 difference
Knowledge
+26.0 difference
DeepSeek V4 Flash
Qwen3.7 Max
$0.14 / $0.28
$null / $null
N/A
N/A
N/A
N/A
1M
1M
Pick Qwen3.7 Max if you want the stronger benchmark profile. DeepSeek V4 Flash 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 58. 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 knowledge, where it averages 71.2 against 45.2. The single biggest benchmark swing on the page is LiveCodeBench, 55.2% to 91.6%.
Qwen3.7 Max 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.
Qwen3.7 Max is ahead on BenchLM's provisional leaderboard, 93 to 58. The biggest single separator in this matchup is LiveCodeBench, where the scores are 55.2% and 91.6%.
Qwen3.7 Max has the edge for knowledge tasks in this comparison, averaging 71.2 versus 45.2. Inside this category, HLE is the benchmark that creates the most daylight between them.
Qwen3.7 Max has the edge for coding in this comparison, averaging 73.6 versus 57.1. Inside this category, LiveCodeBench is the benchmark that creates the most daylight between them.
Qwen3.7 Max has the edge for agentic tasks in this comparison, averaging 69.7 versus 49.1. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
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