Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
DeepSeek V3.2
56
Ornith-1.0-9B
52
Pick DeepSeek V3.2 if you want the stronger benchmark profile. Ornith-1.0-9B only becomes the better choice if coding is the priority or you want the cheaper token bill.
Coding
+8.5 difference
DeepSeek V3.2
Ornith-1.0-9B
$0.28 / $0.42
$0 / $0
35 t/s
N/A
3.75s
N/A
128K
262K
Pick DeepSeek V3.2 if you want the stronger benchmark profile. Ornith-1.0-9B only becomes the better choice if coding is the priority or you want the cheaper token bill.
DeepSeek V3.2 is clearly ahead on the provisional aggregate, 56 to 52. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
DeepSeek V3.2 is also the more expensive model on tokens at $0.28 input / $0.42 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Ornith-1.0-9B. That is roughly Infinityx on output cost alone. Ornith-1.0-9B is the reasoning model in the pair, while DeepSeek V3.2 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. Ornith-1.0-9B gives you the larger context window at 262K, compared with 128K for DeepSeek V3.2.
DeepSeek V3.2 is ahead on BenchLM's provisional leaderboard, 56 to 52.
Ornith-1.0-9B has the edge for coding in this comparison, averaging 69.4 versus 60.9. DeepSeek V3.2 stays close enough that the answer can still flip depending on your workload.
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