Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GPT-5.4 nano
62
Ling 2.6 Flash
44
Pick GPT-5.4 nano if you want the stronger benchmark profile. Ling 2.6 Flash only becomes the better choice if knowledge is the priority or you want the cheaper token bill.
Knowledge
+5.8 difference
GPT-5.4 nano
Ling 2.6 Flash
$0.2 / $1.25
$0.1 / $0.3
191 t/s
209.5 t/s
3.64s
1.07s
400K
262K
Pick GPT-5.4 nano if you want the stronger benchmark profile. Ling 2.6 Flash only becomes the better choice if knowledge is the priority or you want the cheaper token bill.
GPT-5.4 nano is clearly ahead on the provisional aggregate, 62 to 44. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GPT-5.4 nano is also the more expensive model on tokens at $0.20 input / $1.25 output per 1M tokens, versus $0.10 input / $0.30 output per 1M tokens for Ling 2.6 Flash. That is roughly 4.2x on output cost alone. GPT-5.4 nano 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-5.4 nano gives you the larger context window at 400K, compared with 262K for Ling 2.6 Flash.
GPT-5.4 nano is ahead on BenchLM's provisional leaderboard, 62 to 44. The biggest single separator in this matchup is GPQA, where the scores are 82.8% and 59%.
Ling 2.6 Flash has the edge for knowledge tasks in this comparison, averaging 59 versus 53.2. Inside this category, GPQA is the benchmark that creates the most daylight between them.
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