Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
DeepSeek V4 Pro (Max)
87
Ling 2.6 Flash
43
Verified leaderboard positions: DeepSeek V4 Pro (Max) #2 · Ling 2.6 Flash unranked
Pick DeepSeek V4 Pro (Max) if you want the stronger benchmark profile. Ling 2.6 Flash only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
Coding
+48.9 difference
Knowledge
+7.1 difference
DeepSeek V4 Pro (Max)
Ling 2.6 Flash
$1.74 / $3.48
$null / $null
N/A
209.5 t/s
N/A
1.07s
1M
262K
Pick DeepSeek V4 Pro (Max) if you want the stronger benchmark profile. Ling 2.6 Flash only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
DeepSeek V4 Pro (Max) is clearly ahead on the provisional aggregate, 87 to 43. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
DeepSeek V4 Pro (Max)'s sharpest advantage is in coding, where it averages 75.9 against 27. The single biggest benchmark swing on the page is GPQA, 90.1% to 59%.
DeepSeek V4 Pro (Max) is the reasoning model in the pair, while Ling 2.6 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 Pro (Max) gives you the larger context window at 1M, compared with 262K for Ling 2.6 Flash.
DeepSeek V4 Pro (Max) is ahead on BenchLM's provisional leaderboard, 87 to 43. The biggest single separator in this matchup is GPQA, where the scores are 90.1% and 59%.
DeepSeek V4 Pro (Max) has the edge for knowledge tasks in this comparison, averaging 66.1 versus 59. Inside this category, GPQA is the benchmark that creates the most daylight between them.
DeepSeek V4 Pro (Max) has the edge for coding in this comparison, averaging 75.9 versus 27. Ling 2.6 Flash stays close enough that the answer can still flip depending on your workload.
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