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 49. 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 19.3. The single biggest benchmark swing on the page is SWE-bench Pro, 85 to 19.
GPT-5.3-Codex-Spark is the reasoning model in the pair, while Claude 3 Haiku 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 200K for Claude 3 Haiku.
Pick GPT-5.3-Codex-Spark if you want the stronger benchmark profile. Claude 3 Haiku 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
Claude 3 Haiku
44
GPT-5.3-Codex-Spark
82.3
Claude 3 Haiku
19.3
GPT-5.3-Codex-Spark
88.3
Claude 3 Haiku
68.7
GPT-5.3-Codex-Spark
92.7
Claude 3 Haiku
59.6
GPT-5.3-Codex-Spark
78.3
Claude 3 Haiku
42.8
GPT-5.3-Codex-Spark
92
Claude 3 Haiku
76
GPT-5.3-Codex-Spark
90.8
Claude 3 Haiku
71.1
GPT-5.3-Codex-Spark
96.7
Claude 3 Haiku
62.2
GPT-5.3-Codex-Spark is ahead overall, 87 to 49. The biggest single separator in this matchup is SWE-bench Pro, where the scores are 85 and 19.
GPT-5.3-Codex-Spark has the edge for knowledge tasks in this comparison, averaging 78.3 versus 42.8. 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 19.3. Inside this category, SWE-bench Pro 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 62.2. 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 59.6. 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. 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 68.7. 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 76. 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 71.1. 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.