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 47. 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 26.5. The single biggest benchmark swing on the page is LiveCodeBench, 80 to 22.
GPT-5.3-Codex-Spark is the reasoning model in the pair, while Nemotron-4 15B 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 32K for Nemotron-4 15B.
Pick GPT-5.3-Codex-Spark if you want the stronger benchmark profile. Nemotron-4 15B 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
Nemotron-4 15B
41.3
GPT-5.3-Codex-Spark
82.3
Nemotron-4 15B
26.5
GPT-5.3-Codex-Spark
88.3
Nemotron-4 15B
49.6
GPT-5.3-Codex-Spark
92.7
Nemotron-4 15B
53.4
GPT-5.3-Codex-Spark
78.3
Nemotron-4 15B
42.6
GPT-5.3-Codex-Spark
92
Nemotron-4 15B
79
GPT-5.3-Codex-Spark
90.8
Nemotron-4 15B
72.4
GPT-5.3-Codex-Spark
96.7
Nemotron-4 15B
61.1
GPT-5.3-Codex-Spark is ahead overall, 87 to 47. The biggest single separator in this matchup is LiveCodeBench, where the scores are 80 and 22.
GPT-5.3-Codex-Spark has the edge for knowledge tasks in this comparison, averaging 78.3 versus 42.6. Inside this category, MMLU 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 26.5. 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 61.1. Inside this category, AIME 2023 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 53.4. Inside this category, SimpleQA 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 41.3. 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 49.6. 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 79. 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 72.4. 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.