Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GLM-5.2
90
Ornith-1.0-9B
52
Verified leaderboard positions: GLM-5.2 #9 · Ornith-1.0-9B unranked
Pick GLM-5.2 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.
Agentic
+37.9 difference
Coding
+7.3 difference
GLM-5.2
Ornith-1.0-9B
$1.4 / $4.4
$0 / $0
N/A
N/A
N/A
N/A
1M
262K
Pick GLM-5.2 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.
GLM-5.2 is clearly ahead on the provisional aggregate, 90 to 52. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GLM-5.2's sharpest advantage is in agentic, where it averages 81 against 43.1. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 81% to 43.1%. Ornith-1.0-9B does hit back in coding, so the answer changes if that is the part of the workload you care about most.
GLM-5.2 is also the more expensive model on tokens at $1.40 input / $4.40 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. GLM-5.2 gives you the larger context window at 1M, compared with 262K for Ornith-1.0-9B.
GLM-5.2 is ahead on BenchLM's provisional leaderboard, 90 to 52. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 81% and 43.1%.
Ornith-1.0-9B has the edge for coding in this comparison, averaging 69.4 versus 62.1. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
GLM-5.2 has the edge for agentic tasks in this comparison, averaging 81 versus 43.1. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
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