Head-to-head comparison across 3benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Claude Sonnet 4.6
84
DeepSeek V4 Pro (High)
83
Verified leaderboard positions: Claude Sonnet 4.6 unranked · DeepSeek V4 Pro (High) #6
Pick Claude Sonnet 4.6 if you want the stronger benchmark profile. DeepSeek V4 Pro (High) only becomes the better choice if coding is the priority or you want the cheaper token bill.
Agentic
+4.7 difference
Coding
+7.4 difference
Knowledge
+11.1 difference
Claude Sonnet 4.6
DeepSeek V4 Pro (High)
$3 / $15
$1.74 / $3.48
44 t/s
N/A
1.48s
N/A
200K
1M
Pick Claude Sonnet 4.6 if you want the stronger benchmark profile. DeepSeek V4 Pro (High) only becomes the better choice if coding is the priority or you want the cheaper token bill.
Claude Sonnet 4.6 finishes one point ahead on BenchLM's provisional leaderboard, 84 to 83. That is enough to call, but not enough to treat as a blowout. This matchup comes down to a few meaningful edges rather than one model dominating the board.
Claude Sonnet 4.6's sharpest advantage is in knowledge, where it averages 73.7 against 62.6. The single biggest benchmark swing on the page is HLE, 49% to 34.5%. DeepSeek V4 Pro (High) does hit back in coding, so the answer changes if that is the part of the workload you care about most.
Claude Sonnet 4.6 is also the more expensive model on tokens at $3.00 input / $15.00 output per 1M tokens, versus $1.74 input / $3.48 output per 1M tokens for DeepSeek V4 Pro (High). That is roughly 4.3x on output cost alone. DeepSeek V4 Pro (High) is the reasoning model in the pair, while Claude Sonnet 4.6 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 (High) gives you the larger context window at 1M, compared with 200K for Claude Sonnet 4.6.
Claude Sonnet 4.6 is ahead on BenchLM's provisional leaderboard, 84 to 83. The biggest single separator in this matchup is HLE, where the scores are 49% and 34.5%.
Claude Sonnet 4.6 has the edge for knowledge tasks in this comparison, averaging 73.7 versus 62.6. Inside this category, HLE is the benchmark that creates the most daylight between them.
DeepSeek V4 Pro (High) has the edge for coding in this comparison, averaging 73.8 versus 66.4. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
DeepSeek V4 Pro (High) has the edge for agentic tasks in this comparison, averaging 70 versus 65.3. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
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