Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Holo3-122B-A10B
78
Qwen3.6-27B
72
Verified leaderboard positions: Holo3-122B-A10B unranked · Qwen3.6-27B #10
Pick Holo3-122B-A10B if you want the stronger benchmark profile. Qwen3.6-27B only becomes the better choice if you want the cheaper token bill or you need the larger 262K context window.
Agentic
+19.6 difference
Holo3-122B-A10B
Qwen3.6-27B
$0.4 / $3
$0 / $0
N/A
N/A
N/A
N/A
64K
262K
Pick Holo3-122B-A10B if you want the stronger benchmark profile. Qwen3.6-27B only becomes the better choice if you want the cheaper token bill or you need the larger 262K context window.
Holo3-122B-A10B is clearly ahead on the provisional aggregate, 78 to 72. 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 59.3.
Holo3-122B-A10B is also the more expensive model on tokens at $0.40 input / $3.00 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Qwen3.6-27B. That is roughly Infinityx on output cost alone. Qwen3.6-27B 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. Qwen3.6-27B gives you the larger context window at 262K, compared with 64K for Holo3-122B-A10B.
Holo3-122B-A10B is ahead on BenchLM's provisional leaderboard, 78 to 72.
Holo3-122B-A10B has the edge for agentic tasks in this comparison, averaging 78.9 versus 59.3. Qwen3.6-27B stays close enough that the answer can still flip depending on your workload.
Estimates at 50,000 req/day · 1000 tokens/req average.
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