Head-to-head comparison across 3benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GPT-4.1 mini
47
Ling 2.6 Flash
44
Pick GPT-4.1 mini if you want the stronger benchmark profile. Ling 2.6 Flash only becomes the better choice if coding is the priority or you want the cheaper token bill.
Coding
+3.4 difference
Knowledge
+5.2 difference
Inst. Following
+31.5 difference
GPT-4.1 mini
Ling 2.6 Flash
$0.4 / $1.6
$0.1 / $0.3
80 t/s
209.5 t/s
0.76s
1.07s
1M
262K
Pick GPT-4.1 mini if you want the stronger benchmark profile. Ling 2.6 Flash only becomes the better choice if coding is the priority or you want the cheaper token bill.
GPT-4.1 mini has the cleaner provisional overall profile here, landing at 47 versus 44. It is a real lead, but still close enough that category-level strengths matter more than the headline number.
GPT-4.1 mini's sharpest advantage is in instruction following, where it averages 88.5 against 57. The single biggest benchmark swing on the page is GPQA, 64.2% to 59%. Ling 2.6 Flash does hit back in coding, so the answer changes if that is the part of the workload you care about most.
GPT-4.1 mini is also the more expensive model on tokens at $0.40 input / $1.60 output per 1M tokens, versus $0.10 input / $0.30 output per 1M tokens for Ling 2.6 Flash. That is roughly 5.3x on output cost alone. GPT-4.1 mini gives you the larger context window at 1M, compared with 262K for Ling 2.6 Flash.
GPT-4.1 mini is ahead on BenchLM's provisional leaderboard, 47 to 44. The biggest single separator in this matchup is GPQA, where the scores are 64.2% and 59%.
GPT-4.1 mini has the edge for knowledge tasks in this comparison, averaging 64.2 versus 59. Inside this category, GPQA is the benchmark that creates the most daylight between them.
Ling 2.6 Flash has the edge for coding in this comparison, averaging 27 versus 23.6. GPT-4.1 mini stays close enough that the answer can still flip depending on your workload.
GPT-4.1 mini has the edge for instruction following in this comparison, averaging 88.5 versus 57. Ling 2.6 Flash stays close enough that the answer can still flip depending on your workload.
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