Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Claude 3.5 Sonnet
41
DeepSeek V3
36
Pick Claude 3.5 Sonnet if you want the stronger benchmark profile. DeepSeek V3 only becomes the better choice if knowledge is the priority or you want the cheaper token bill.
Coding
+9.8 difference
Knowledge
+10.6 difference
Claude 3.5 Sonnet
DeepSeek V3
$3 / $15
$0.27 / $1.1
N/A
N/A
N/A
N/A
200K
128K
Pick Claude 3.5 Sonnet if you want the stronger benchmark profile. DeepSeek V3 only becomes the better choice if knowledge is the priority or you want the cheaper token bill.
Claude 3.5 Sonnet is clearly ahead on the provisional aggregate, 41 to 36. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Claude 3.5 Sonnet's sharpest advantage is in coding, where it averages 49 against 39.2. The single biggest benchmark swing on the page is SWE-bench Verified, 49% to 42%. DeepSeek V3 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 $0.27 input / $1.10 output per 1M tokens for DeepSeek V3. That is roughly 13.6x on output cost alone. Claude 3.5 Sonnet gives you the larger context window at 200K, compared with 128K for DeepSeek V3.
Claude 3.5 Sonnet is ahead on BenchLM's provisional leaderboard, 41 to 36. The biggest single separator in this matchup is SWE-bench Verified, where the scores are 49% and 42%.
DeepSeek V3 has the edge for knowledge tasks in this comparison, averaging 70 versus 59.4. Inside this category, GPQA is the benchmark that creates the most daylight between them.
Claude 3.5 Sonnet has the edge for coding in this comparison, averaging 49 versus 39.2. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
Estimates at 50,000 req/day · 1000 tokens/req average.
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