Head-to-head comparison across 3benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
DeepSeek V3
36
GLM-5
67
Verified leaderboard positions: DeepSeek V3 unranked · GLM-5 #17
Pick GLM-5 if you want the stronger benchmark profile. DeepSeek V3 only becomes the better choice if you want the cheaper token bill.
Coding
+24.0 difference
Knowledge
+0.7 difference
Inst. Following
+6.5 difference
DeepSeek V3
GLM-5
$0.27 / $1.1
$1 / $3.2
N/A
74 t/s
N/A
1.64s
128K
200K
Pick GLM-5 if you want the stronger benchmark profile. DeepSeek V3 only becomes the better choice if you want the cheaper token bill.
GLM-5 is clearly ahead on the provisional aggregate, 67 to 36. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GLM-5's sharpest advantage is in coding, where it averages 63.2 against 39.2. The single biggest benchmark swing on the page is SWE-bench Verified, 42% to 77.8%.
GLM-5 is also the more expensive model on tokens at $1.00 input / $3.20 output per 1M tokens, versus $0.27 input / $1.10 output per 1M tokens for DeepSeek V3. That is roughly 2.9x on output cost alone. GLM-5 gives you the larger context window at 200K, compared with 128K for DeepSeek V3.
GLM-5 is ahead on BenchLM's provisional leaderboard, 67 to 36. The biggest single separator in this matchup is SWE-bench Verified, where the scores are 42% and 77.8%.
GLM-5 has the edge for knowledge tasks in this comparison, averaging 70.7 versus 70. Inside this category, GPQA is the benchmark that creates the most daylight between them.
GLM-5 has the edge for coding in this comparison, averaging 63.2 versus 39.2. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
GLM-5 has the edge for instruction following in this comparison, averaging 92.6 versus 86.1. Inside this category, IFEval is the benchmark that creates the most daylight between them.
Estimates at 50,000 req/day · 1000 tokens/req average.
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