Head-to-head comparison across 3benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
DeepSeek V3
34
GLM-5
67
Verified leaderboard positions: DeepSeek V3 unranked · GLM-5 #24
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 34. 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 34. 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, AA-Omniscience Hallucination Rate 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, Terminal-Bench Hard 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, AA-IFBench is the benchmark that creates the most daylight between them.
Estimates at 50,000 req/day · 1000 tokens/req average.
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