Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
DeepSeek V4 Pro
68
Ornith-1.0-35B
67
Verified leaderboard positions: DeepSeek V4 Pro #31 · Ornith-1.0-35B unranked
Pick DeepSeek V4 Pro if you want the stronger benchmark profile. Ornith-1.0-35B only becomes the better choice if agentic is the priority or you want the cheaper token bill.
Agentic
+5.1 difference
DeepSeek V4 Pro
Ornith-1.0-35B
$1.74 / $3.48
$0 / $0
N/A
N/A
N/A
N/A
1M
262K
Pick DeepSeek V4 Pro if you want the stronger benchmark profile. Ornith-1.0-35B only becomes the better choice if agentic is the priority or you want the cheaper token bill.
DeepSeek V4 Pro finishes one point ahead on BenchLM's provisional leaderboard, 68 to 67. That is enough to call, but not enough to treat as a blowout. This matchup comes down to a few meaningful edges rather than one model dominating the board.
DeepSeek V4 Pro is also the more expensive model on tokens at $1.74 input / $3.48 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Ornith-1.0-35B. That is roughly Infinityx on output cost alone. Ornith-1.0-35B is the reasoning model in the pair, while DeepSeek V4 Pro 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 gives you the larger context window at 1M, compared with 262K for Ornith-1.0-35B.
DeepSeek V4 Pro is ahead on BenchLM's provisional leaderboard, 68 to 67. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 59.1% and 64.2%.
Ornith-1.0-35B has the edge for agentic tasks in this comparison, averaging 64.2 versus 59.1. Inside this category, Claw-Eval is the benchmark that creates the most daylight between them.
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