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 38. 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 14.3. The single biggest benchmark swing on the page is LiveCodeBench, 80 to 11.
GPT-5.3-Codex-Spark is the reasoning model in the pair, while Qwen2.5-VL-32B 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 Qwen2.5-VL-32B.
Pick GPT-5.3-Codex-Spark if you want the stronger benchmark profile. Qwen2.5-VL-32B 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
Qwen2.5-VL-32B
33.5
GPT-5.3-Codex-Spark
82.3
Qwen2.5-VL-32B
14.3
GPT-5.3-Codex-Spark
88.3
Qwen2.5-VL-32B
52.2
GPT-5.3-Codex-Spark
92.7
Qwen2.5-VL-32B
43.2
GPT-5.3-Codex-Spark
78.3
Qwen2.5-VL-32B
34.7
GPT-5.3-Codex-Spark
92
Qwen2.5-VL-32B
67
GPT-5.3-Codex-Spark
90.8
Qwen2.5-VL-32B
60.4
GPT-5.3-Codex-Spark
96.7
Qwen2.5-VL-32B
49.7
GPT-5.3-Codex-Spark is ahead overall, 87 to 38. The biggest single separator in this matchup is LiveCodeBench, where the scores are 80 and 11.
GPT-5.3-Codex-Spark has the edge for knowledge tasks in this comparison, averaging 78.3 versus 34.7. 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 14.3. 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 49.7. 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 43.2. 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 33.5. 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 52.2. Inside this category, OfficeQA 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 67. 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 60.4. 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.