Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GLM-4.7
71
GPT-5.3 Codex
89
Pick GPT-5.3 Codex if you want the stronger benchmark profile. GLM-4.7 only becomes the better choice if coding is the priority or you want the cheaper token bill.
Agentic
+26.2 difference
Coding
+7.5 difference
GLM-4.7
GPT-5.3 Codex
$0 / $0
$2.5 / $10
82 t/s
79 t/s
1.10s
88.26s
200K
400K
Pick GPT-5.3 Codex if you want the stronger benchmark profile. GLM-4.7 only becomes the better choice if coding is the priority or you want the cheaper token bill.
GPT-5.3 Codex is clearly ahead on the provisional aggregate, 89 to 71. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-5.3 Codex's sharpest advantage is in agentic, where it averages 71.5 against 45.3. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 41% to 77.3%. GLM-4.7 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 $2.50 input / $10.00 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for GLM-4.7. That is roughly Infinityx on output cost alone. GPT-5.3 Codex gives you the larger context window at 400K, compared with 200K for GLM-4.7.
GPT-5.3 Codex is ahead on BenchLM's provisional leaderboard, 89 to 71. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 41% and 77.3%.
GLM-4.7 has the edge for coding in this comparison, averaging 70.6 versus 63.1. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
GPT-5.3 Codex has the edge for agentic tasks in this comparison, averaging 71.5 versus 45.3. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
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