Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GPT-5.3 Codex
89
Ling 2.6 Flash
44
Pick GPT-5.3 Codex if you want the stronger benchmark profile. Ling 2.6 Flash only becomes the better choice if you want the cheaper token bill or you would rather avoid the extra latency and token burn of a reasoning model.
Coding
+36.1 difference
GPT-5.3 Codex
Ling 2.6 Flash
$2.5 / $10
$0.1 / $0.3
79 t/s
209.5 t/s
88.26s
1.07s
400K
262K
Pick GPT-5.3 Codex if you want the stronger benchmark profile. Ling 2.6 Flash only becomes the better choice if you want the cheaper token bill or you would rather avoid the extra latency and token burn of a reasoning model.
GPT-5.3 Codex is clearly ahead on the provisional aggregate, 89 to 44. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-5.3 Codex's sharpest advantage is in coding, where it averages 63.1 against 27.
GPT-5.3 Codex is also the more expensive model on tokens at $2.50 input / $10.00 output per 1M tokens, versus $0.10 input / $0.30 output per 1M tokens for Ling 2.6 Flash. That is roughly 33.3x on output cost alone. GPT-5.3 Codex is the reasoning model in the pair, while Ling 2.6 Flash 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 262K for Ling 2.6 Flash.
GPT-5.3 Codex is ahead on BenchLM's provisional leaderboard, 89 to 44.
GPT-5.3 Codex has the edge for coding in this comparison, averaging 63.1 versus 27. Ling 2.6 Flash stays close enough that the answer can still flip depending on your workload.
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