Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Claude 3.5 Sonnet
41
DeepSeek V4 Flash
59
Verified leaderboard positions: Claude 3.5 Sonnet unranked · DeepSeek V4 Flash #23
Pick DeepSeek V4 Flash if you want the stronger benchmark profile. Claude 3.5 Sonnet only becomes the better choice if knowledge is the priority.
Coding
+8.1 difference
Knowledge
+14.2 difference
Claude 3.5 Sonnet
DeepSeek V4 Flash
$3 / $15
$0.14 / $0.28
N/A
N/A
N/A
N/A
200K
1M
Pick DeepSeek V4 Flash if you want the stronger benchmark profile. Claude 3.5 Sonnet only becomes the better choice if knowledge is the priority.
DeepSeek V4 Flash is clearly ahead on the provisional aggregate, 59 to 41. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
DeepSeek V4 Flash's sharpest advantage is in coding, where it averages 57.1 against 49. The single biggest benchmark swing on the page is SWE-bench Verified, 49% to 73.7%. Claude 3.5 Sonnet does hit back in knowledge, so the answer changes if that is the part of the workload you care about most.
Claude 3.5 Sonnet is also the more expensive model on tokens at $3.00 input / $15.00 output per 1M tokens, versus $0.14 input / $0.28 output per 1M tokens for DeepSeek V4 Flash. That is roughly 53.6x on output cost alone. DeepSeek V4 Flash gives you the larger context window at 1M, compared with 200K for Claude 3.5 Sonnet.
DeepSeek V4 Flash is ahead on BenchLM's provisional leaderboard, 59 to 41. The biggest single separator in this matchup is SWE-bench Verified, where the scores are 49% and 73.7%.
Claude 3.5 Sonnet has the edge for knowledge tasks in this comparison, averaging 59.4 versus 45.2. Inside this category, GPQA is the benchmark that creates the most daylight between them.
DeepSeek V4 Flash has the edge for coding in this comparison, averaging 57.1 versus 49. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
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