Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GLM-5.1
83
ZAYA1-74B-Preview
58
Verified leaderboard positions: GLM-5.1 #21 · ZAYA1-74B-Preview unranked
Pick GLM-5.1 if you want the stronger benchmark profile. ZAYA1-74B-Preview only becomes the better choice if knowledge is the priority or you want the cheaper token bill.
Coding
+7.7 difference
Knowledge
+12.0 difference
GLM-5.1
ZAYA1-74B-Preview
$1.4 / $4.4
$0 / $0
N/A
N/A
N/A
N/A
203K
256K
Pick GLM-5.1 if you want the stronger benchmark profile. ZAYA1-74B-Preview only becomes the better choice if knowledge is the priority or you want the cheaper token bill.
GLM-5.1 is clearly ahead on the provisional aggregate, 83 to 58. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GLM-5.1's sharpest advantage is in coding, where it averages 60.9 against 53.2. ZAYA1-74B-Preview does hit back in knowledge, so the answer changes if that is the part of the workload you care about most.
GLM-5.1 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. ZAYA1-74B-Preview gives you the larger context window at 256K, compared with 203K for GLM-5.1.
GLM-5.1 is ahead on BenchLM's provisional leaderboard, 83 to 58.
ZAYA1-74B-Preview has the edge for knowledge tasks in this comparison, averaging 64.3 versus 52.3. Inside this category, GPQA-D is the benchmark that creates the most daylight between them.
GLM-5.1 has the edge for coding in this comparison, averaging 60.9 versus 53.2. ZAYA1-74B-Preview 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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