Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GLM-5.2
94
ZAYA1-74B-Preview
54
Verified leaderboard positions: GLM-5.2 #9 · ZAYA1-74B-Preview unranked
Pick GLM-5.2 if you want the stronger benchmark profile. ZAYA1-74B-Preview only becomes the better choice if you want the cheaper token bill.
Coding
+8.9 difference
Knowledge
+2.9 difference
GLM-5.2
ZAYA1-74B-Preview
$1.4 / $4.4
$0 / $0
N/A
N/A
N/A
N/A
1M
256K
Pick GLM-5.2 if you want the stronger benchmark profile. ZAYA1-74B-Preview only becomes the better choice if you want the cheaper token bill.
GLM-5.2 is clearly ahead on the provisional aggregate, 94 to 54. 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 coding, where it averages 62.1 against 53.2. The single biggest benchmark swing on the page is GPQA, 91.2% to 57.3%.
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 ZAYA1-74B-Preview. That is roughly Infinityx on output cost alone. GLM-5.2 gives you the larger context window at 1M, compared with 256K for ZAYA1-74B-Preview.
GLM-5.2 is ahead on BenchLM's provisional leaderboard, 94 to 54. The biggest single separator in this matchup is GPQA, where the scores are 91.2% and 57.3%.
GLM-5.2 has the edge for knowledge tasks in this comparison, averaging 67.2 versus 64.3. Inside this category, GPQA is the benchmark that creates the most daylight between them.
GLM-5.2 has the edge for coding in this comparison, averaging 62.1 versus 53.2. ZAYA1-74B-Preview stays close enough that the answer can still flip depending on your workload.
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