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 17.2. The single biggest benchmark swing on the page is SWE-bench Verified, 80 to 5.
GPT-5.3-Codex-Spark is the reasoning model in the pair, while GPT-4 Turbo 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 GPT-4 Turbo.
Pick GPT-5.3-Codex-Spark if you want the stronger benchmark profile. GPT-4 Turbo 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
GPT-4 Turbo
44.7
GPT-5.3-Codex-Spark
82.3
GPT-4 Turbo
17.2
GPT-5.3-Codex-Spark
88.3
GPT-4 Turbo
55.3
GPT-5.3-Codex-Spark
92.7
GPT-4 Turbo
61
GPT-5.3-Codex-Spark
78.3
GPT-4 Turbo
41.1
GPT-5.3-Codex-Spark
92
GPT-4 Turbo
80
GPT-5.3-Codex-Spark
90.8
GPT-4 Turbo
68.5
GPT-5.3-Codex-Spark
96.7
GPT-4 Turbo
64.4
GPT-5.3-Codex-Spark is ahead overall, 87 to 47. The biggest single separator in this matchup is SWE-bench Verified, where the scores are 80 and 5.
GPT-5.3-Codex-Spark has the edge for knowledge tasks in this comparison, averaging 78.3 versus 41.1. 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 17.2. 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 64.4. 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 61. 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 44.7. 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 55.3. 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 80. 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 68.5. Inside this category, MMLU-ProX 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.