Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
DeepSeek V4 Flash (High)
71
Holo3-122B-A10B
100
Verified leaderboard positions: DeepSeek V4 Flash (High) #26 · Holo3-122B-A10B unranked
Pick Holo3-122B-A10B if you want the stronger benchmark profile. DeepSeek V4 Flash (High) only becomes the better choice if you want the cheaper token bill or you need the larger 1M context window.
Agentic
+23.5 difference
DeepSeek V4 Flash (High)
Holo3-122B-A10B
$0.14 / $0.28
$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 Flash (High) only becomes the better choice if you want the cheaper token bill or you need the larger 1M context window.
Holo3-122B-A10B is clearly ahead on the provisional aggregate, 100 to 71. 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 55.4.
Holo3-122B-A10B is also the more expensive model on tokens at $0.40 input / $3.00 output per 1M tokens, versus $0.14 input / $0.28 output per 1M tokens for DeepSeek V4 Flash (High). That is roughly 10.7x on output cost alone. DeepSeek V4 Flash (High) 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 Flash (High) 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 71.
Holo3-122B-A10B has the edge for agentic tasks in this comparison, averaging 78.9 versus 55.4. DeepSeek V4 Flash (High) stays close enough that the answer can still flip depending on your workload.
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