Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Gemma 4 E2B
28
Ling 2.6 Flash
44
Pick Ling 2.6 Flash if you want the stronger benchmark profile. Gemma 4 E2B only becomes the better choice if you want the cheaper token bill or you want the stronger reasoning-first profile.
Knowledge
+4.9 difference
Gemma 4 E2B
Ling 2.6 Flash
$0 / $0
$0.1 / $0.3
N/A
209.5 t/s
N/A
1.07s
128K
262K
Pick Ling 2.6 Flash if you want the stronger benchmark profile. Gemma 4 E2B only becomes the better choice if you want the cheaper token bill or you want the stronger reasoning-first profile.
Ling 2.6 Flash is clearly ahead on the provisional aggregate, 44 to 28. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
Ling 2.6 Flash's sharpest advantage is in knowledge, where it averages 59 against 54.1. The single biggest benchmark swing on the page is GPQA, 43.4% to 59%.
Ling 2.6 Flash is also the more expensive model on tokens at $0.10 input / $0.30 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Gemma 4 E2B. That is roughly Infinityx on output cost alone. Gemma 4 E2B 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. Ling 2.6 Flash gives you the larger context window at 262K, compared with 128K for Gemma 4 E2B.
Ling 2.6 Flash is ahead on BenchLM's provisional leaderboard, 44 to 28. The biggest single separator in this matchup is GPQA, where the scores are 43.4% and 59%.
Ling 2.6 Flash has the edge for knowledge tasks in this comparison, averaging 59 versus 54.1. Inside this category, GPQA is the benchmark that creates the most daylight between them.
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