Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
GPT-5.3-Codex-Spark is clearly ahead on the aggregate, 87 to 65. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-5.3-Codex-Spark's sharpest advantage is in coding, where it averages 82.3 against 41.1. The single biggest benchmark swing on the page is LiveCodeBench, 80 to 35.
GPT-5.3-Codex-Spark is the reasoning model in the pair, while GLM-5 is not. That usually helps on harder chain-of-thought-heavy tests, but it can also mean more latency and more token spend in real use. GPT-5.3-Codex-Spark gives you the larger context window at 256K, compared with 200K for GLM-5.
Pick GPT-5.3-Codex-Spark if you want the stronger benchmark profile. GLM-5 only becomes the better choice if you would rather avoid the extra latency and token burn of a reasoning model.
GPT-5.3-Codex-Spark
85.6
GLM-5
62.3
GPT-5.3-Codex-Spark
82.3
GLM-5
41.1
GPT-5.3-Codex-Spark
88.3
GLM-5
69.2
GPT-5.3-Codex-Spark
92.7
GLM-5
79.4
GPT-5.3-Codex-Spark
78.3
GLM-5
62.1
GPT-5.3-Codex-Spark
92
GLM-5
85
GPT-5.3-Codex-Spark
90.8
GLM-5
82.1
GPT-5.3-Codex-Spark
96.7
GLM-5
84.8
GPT-5.3-Codex-Spark is ahead overall, 87 to 65. The biggest single separator in this matchup is LiveCodeBench, where the scores are 80 and 35.
GPT-5.3-Codex-Spark has the edge for knowledge tasks in this comparison, averaging 78.3 versus 62.1. Inside this category, HLE is the benchmark that creates the most daylight between them.
GPT-5.3-Codex-Spark has the edge for coding in this comparison, averaging 82.3 versus 41.1. Inside this category, LiveCodeBench is the benchmark that creates the most daylight between them.
GPT-5.3-Codex-Spark has the edge for math in this comparison, averaging 96.7 versus 84.8. Inside this category, MATH-500 is the benchmark that creates the most daylight between them.
GPT-5.3-Codex-Spark has the edge for reasoning in this comparison, averaging 92.7 versus 79.4. Inside this category, MRCRv2 is the benchmark that creates the most daylight between them.
GPT-5.3-Codex-Spark has the edge for agentic tasks in this comparison, averaging 85.6 versus 62.3. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
GPT-5.3-Codex-Spark has the edge for multimodal and grounded tasks in this comparison, averaging 88.3 versus 69.2. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
GPT-5.3-Codex-Spark has the edge for instruction following in this comparison, averaging 92 versus 85. Inside this category, IFEval is the benchmark that creates the most daylight between them.
GPT-5.3-Codex-Spark has the edge for multilingual tasks in this comparison, averaging 90.8 versus 82.1. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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