Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Claude Haiku 4.5
58
DeepSeek V4 Pro (Max)
87
Verified leaderboard positions: Claude Haiku 4.5 unranked · DeepSeek V4 Pro (Max) #2
Pick DeepSeek V4 Pro (Max) if you want the stronger benchmark profile. Claude Haiku 4.5 only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
Coding
+2.6 difference
Claude Haiku 4.5
DeepSeek V4 Pro (Max)
$1 / $5
$1.74 / $3.48
N/A
N/A
N/A
N/A
200K
1M
Pick DeepSeek V4 Pro (Max) if you want the stronger benchmark profile. Claude Haiku 4.5 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 58. 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 73.3. The single biggest benchmark swing on the page is SWE-bench Verified, 73.3% to 80.6%.
Claude Haiku 4.5 is also the more expensive model on tokens at $1.00 input / $5.00 output per 1M tokens, versus $1.74 input / $3.48 output per 1M tokens for DeepSeek V4 Pro (Max). DeepSeek V4 Pro (Max) is the reasoning model in the pair, while Claude Haiku 4.5 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 200K for Claude Haiku 4.5.
DeepSeek V4 Pro (Max) is ahead on BenchLM's provisional leaderboard, 87 to 58. The biggest single separator in this matchup is SWE-bench Verified, where the scores are 73.3% and 80.6%.
DeepSeek V4 Pro (Max) has the edge for coding in this comparison, averaging 75.9 versus 73.3. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
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