Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GLM-5.2
94
GPT-5.3 Codex
85
Verified leaderboard positions: GLM-5.2 #9 · GPT-5.3 Codex unranked
Pick GLM-5.2 if you want the stronger benchmark profile. GPT-5.3 Codex only becomes the better choice if coding is the priority.
Agentic
+9.5 difference
Coding
+1.0 difference
GLM-5.2
GPT-5.3 Codex
$1.4 / $4.4
$1.75 / $14
N/A
79 t/s
N/A
88.26s
1M
400K
Pick GLM-5.2 if you want the stronger benchmark profile. GPT-5.3 Codex only becomes the better choice if coding is the priority.
GLM-5.2 is clearly ahead on the provisional aggregate, 94 to 85. 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 71.5. The single biggest benchmark swing on the page is SWE-bench Pro, 62.1% to 56.8%. GPT-5.3 Codex does hit back in coding, so the answer changes if that is the part of the workload you care about most.
GPT-5.3 Codex is also the more expensive model on tokens at $1.75 input / $14.00 output per 1M tokens, versus $1.40 input / $4.40 output per 1M tokens for GLM-5.2. That is roughly 3.2x on output cost alone. GLM-5.2 gives you the larger context window at 1M, compared with 400K for GPT-5.3 Codex.
GLM-5.2 is ahead on BenchLM's provisional leaderboard, 94 to 85. The biggest single separator in this matchup is SWE-bench Pro, where the scores are 62.1% and 56.8%.
GPT-5.3 Codex has the edge for coding in this comparison, averaging 63.1 versus 62.1. Inside this category, SWE-bench Pro 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 71.5. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
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