Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Claude Haiku 4.5
59
GPT-5.3 Codex
89
Pick GPT-5.3 Codex if you want the stronger benchmark profile. Claude Haiku 4.5 only becomes the better choice if coding is the priority or you want the cheaper token bill.
Coding
+10.2 difference
Claude Haiku 4.5
GPT-5.3 Codex
$1 / $5
$2.5 / $10
N/A
79 t/s
N/A
88.26s
200K
400K
Pick GPT-5.3 Codex if you want the stronger benchmark profile. Claude Haiku 4.5 only becomes the better choice if coding is the priority or you want the cheaper token bill.
GPT-5.3 Codex is clearly ahead on the provisional aggregate, 89 to 59. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-5.3 Codex is also the more expensive model on tokens at $2.50 input / $10.00 output per 1M tokens, versus $1.00 input / $5.00 output per 1M tokens for Claude Haiku 4.5. That is roughly 2.0x on output cost alone. GPT-5.3 Codex is the reasoning model in the pair, while Claude Haiku 4.5 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 gives you the larger context window at 400K, compared with 200K for Claude Haiku 4.5.
GPT-5.3 Codex is ahead on BenchLM's provisional leaderboard, 89 to 59. The biggest single separator in this matchup is SWE-bench Verified, where the scores are 73.3% and 85%.
Claude Haiku 4.5 has the edge for coding in this comparison, averaging 73.3 versus 63.1. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
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