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 67. 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 46.6. The single biggest benchmark swing on the page is SWE-bench Verified, 80 to 43.
GPT-5.3-Codex-Spark gives you the larger context window at 256K, compared with 200K for GLM-4.7.
Pick GPT-5.3-Codex-Spark if you want the stronger benchmark profile. GLM-4.7 only becomes the better choice if its workflow or ecosystem matters more than the raw scoreboard.
GPT-5.3-Codex-Spark
85.6
GLM-4.7
66.1
GPT-5.3-Codex-Spark
82.3
GLM-4.7
46.6
GPT-5.3-Codex-Spark
88.3
GLM-4.7
70.5
GPT-5.3-Codex-Spark
92.7
GLM-4.7
80.2
GPT-5.3-Codex-Spark
78.3
GLM-4.7
61.8
GPT-5.3-Codex-Spark
92
GLM-4.7
85
GPT-5.3-Codex-Spark
90.8
GLM-4.7
79.1
GPT-5.3-Codex-Spark
96.7
GLM-4.7
85
GPT-5.3-Codex-Spark is ahead overall, 87 to 67. The biggest single separator in this matchup is SWE-bench Verified, where the scores are 80 and 43.
GPT-5.3-Codex-Spark has the edge for knowledge tasks in this comparison, averaging 78.3 versus 61.8. 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 46.6. Inside this category, SWE-bench Verified 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 85. 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 80.2. 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 66.1. 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 70.5. 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 79.1. Inside this category, MGSM is the benchmark that creates the most daylight between them.
Get notified when new models drop, benchmark scores change, or the leaderboard shifts. One email per week.
Free. No spam. Unsubscribe anytime. We only store derived location metadata for consent routing.