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 78. 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 62.5. The single biggest benchmark swing on the page is LiveCodeBench, 80 to 58.
GPT-5.3-Codex-Spark gives you the larger context window at 256K, compared with 200K for GLM-5 (Reasoning).
Pick GPT-5.3-Codex-Spark if you want the stronger benchmark profile. GLM-5 (Reasoning) only becomes the better choice if its workflow or ecosystem matters more than the raw scoreboard.
GPT-5.3-Codex-Spark
85.6
GLM-5 (Reasoning)
78.3
GPT-5.3-Codex-Spark
82.3
GLM-5 (Reasoning)
62.5
GPT-5.3-Codex-Spark
88.3
GLM-5 (Reasoning)
78.5
GPT-5.3-Codex-Spark
92.7
GLM-5 (Reasoning)
88.9
GPT-5.3-Codex-Spark
78.3
GLM-5 (Reasoning)
72
GPT-5.3-Codex-Spark
92
GLM-5 (Reasoning)
92
GPT-5.3-Codex-Spark
90.8
GLM-5 (Reasoning)
86.4
GPT-5.3-Codex-Spark
96.7
GLM-5 (Reasoning)
94.4
GPT-5.3-Codex-Spark is ahead overall, 87 to 78. The biggest single separator in this matchup is LiveCodeBench, where the scores are 80 and 58.
GPT-5.3-Codex-Spark has the edge for knowledge tasks in this comparison, averaging 78.3 versus 72. 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 62.5. 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 94.4. 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 88.9. Inside this category, BBH 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 78.3. 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 78.5. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
GPT-5.3-Codex-Spark and GLM-5 (Reasoning) are effectively tied for instruction following here, both landing at 92 on average.
GPT-5.3-Codex-Spark has the edge for multilingual tasks in this comparison, averaging 90.8 versus 86.4. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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