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 58. 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 33.2. The single biggest benchmark swing on the page is LiveCodeBench, 80 to 29. Aion-2.0 does hit back in instruction following, so the answer changes if that is the part of the workload you care about most.
GPT-5.3-Codex-Spark is also the more expensive model on tokens at $2.00 input / $8.00 output per 1M tokens, versus $0.80 input / $1.60 output per 1M tokens for Aion-2.0. That is roughly 5.0x on output cost alone. GPT-5.3-Codex-Spark is the reasoning model in the pair, while Aion-2.0 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 128K for Aion-2.0.
Pick GPT-5.3-Codex-Spark if you want the stronger benchmark profile. Aion-2.0 only becomes the better choice if instruction following is the priority or you want the cheaper token bill.
GPT-5.3-Codex-Spark
85.6
Aion-2.0
51.7
GPT-5.3-Codex-Spark
82.3
Aion-2.0
33.2
GPT-5.3-Codex-Spark
88.3
Aion-2.0
66
GPT-5.3-Codex-Spark
92.7
Aion-2.0
70.3
GPT-5.3-Codex-Spark
78.3
Aion-2.0
54
GPT-5.3-Codex-Spark
92
Aion-2.0
93
GPT-5.3-Codex-Spark
90.8
Aion-2.0
78.1
GPT-5.3-Codex-Spark
96.7
Aion-2.0
72.1
GPT-5.3-Codex-Spark is ahead overall, 87 to 58. The biggest single separator in this matchup is LiveCodeBench, where the scores are 80 and 29.
GPT-5.3-Codex-Spark has the edge for knowledge tasks in this comparison, averaging 78.3 versus 54. 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 33.2. 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 72.1. 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 70.3. Inside this category, LongBench v2 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 51.7. 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 66. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
Aion-2.0 has the edge for instruction following in this comparison, averaging 93 versus 92. 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 78.1. Inside this category, MGSM is the benchmark that creates the most daylight between them.
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