Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
DeepSeek V4 Pro (Max)
87
Holo3-122B-A10B
100
Verified leaderboard positions: DeepSeek V4 Pro (Max) #5 · Holo3-122B-A10B unranked
Pick Holo3-122B-A10B if you want the stronger benchmark profile. DeepSeek V4 Pro (Max) only becomes the better choice if you need the larger 1M context window or you want the stronger reasoning-first profile.
Agentic
+4.9 difference
DeepSeek V4 Pro (Max)
Holo3-122B-A10B
$1.74 / $3.48
$0.4 / $3
N/A
N/A
N/A
N/A
1M
64K
Pick Holo3-122B-A10B if you want the stronger benchmark profile. DeepSeek V4 Pro (Max) only becomes the better choice if you need the larger 1M context window or you want the stronger reasoning-first profile.
Holo3-122B-A10B is clearly ahead on the provisional aggregate, 100 to 87. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Holo3-122B-A10B's sharpest advantage is in agentic, where it averages 78.9 against 74.
DeepSeek V4 Pro (Max) is also the more expensive model on tokens at $1.74 input / $3.48 output per 1M tokens, versus $0.40 input / $3.00 output per 1M tokens for Holo3-122B-A10B. DeepSeek V4 Pro (Max) is the reasoning model in the pair, while Holo3-122B-A10B 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 64K for Holo3-122B-A10B.
Holo3-122B-A10B is ahead on BenchLM's provisional leaderboard, 100 to 87.
Holo3-122B-A10B has the edge for agentic tasks in this comparison, averaging 78.9 versus 74. DeepSeek V4 Pro (Max) stays close enough that the answer can still flip depending on your workload.
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