Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
DeepSeek V3
34
Qwen 3.6 Max (preview)
78
Pick Qwen 3.6 Max (preview) if you want the stronger benchmark profile. DeepSeek V3 only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
Coding
+14.9 difference
Knowledge
+3.9 difference
DeepSeek V3
Qwen 3.6 Max (preview)
$0.27 / $1.1
N/A
N/A
N/A
N/A
N/A
128K
256K
Pick Qwen 3.6 Max (preview) if you want the stronger benchmark profile. DeepSeek V3 only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
Qwen 3.6 Max (preview) is clearly ahead on the provisional aggregate, 78 to 34. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Qwen 3.6 Max (preview)'s sharpest advantage is in coding, where it averages 54.1 against 39.2.
Qwen 3.6 Max (preview) is the reasoning model in the pair, while DeepSeek V3 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. Qwen 3.6 Max (preview) gives you the larger context window at 256K, compared with 128K for DeepSeek V3.
Qwen 3.6 Max (preview) is ahead on BenchLM's provisional leaderboard, 78 to 34.
Qwen 3.6 Max (preview) has the edge for knowledge tasks in this comparison, averaging 73.9 versus 70. Inside this category, AA-Omniscience Index is the benchmark that creates the most daylight between them.
Qwen 3.6 Max (preview) has the edge for coding in this comparison, averaging 54.1 versus 39.2. Inside this category, Terminal-Bench Hard is the benchmark that creates the most daylight between them.
Estimates at 50,000 req/day · 1000 tokens/req average.
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