Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Gemma 4 E4B
37
Ling 2.6 Flash
36
Pick Gemma 4 E4B if you want the stronger benchmark profile. Ling 2.6 Flash only becomes the better choice if you need the larger 262K context window or you would rather avoid the extra latency and token burn of a reasoning model.
Knowledge
+6.6 difference
Gemma 4 E4B
Ling 2.6 Flash
$0 / $0
$null / $null
N/A
209.5 t/s
N/A
1.07s
128K
262K
Pick Gemma 4 E4B if you want the stronger benchmark profile. Ling 2.6 Flash only becomes the better choice if you need the larger 262K context window or you would rather avoid the extra latency and token burn of a reasoning model.
Gemma 4 E4B finishes one point ahead on BenchLM's provisional leaderboard, 37 to 36. That is enough to call, but not enough to treat as a blowout. This matchup comes down to a few meaningful edges rather than one model dominating the board.
Gemma 4 E4B's sharpest advantage is in knowledge, where it averages 65.6 against 59. The single biggest benchmark swing on the page is GPQA, 58.6% to 59%.
Gemma 4 E4B 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 E4B.
Gemma 4 E4B is ahead on BenchLM's provisional leaderboard, 37 to 36. The biggest single separator in this matchup is GPQA, where the scores are 58.6% and 59%.
Gemma 4 E4B has the edge for knowledge tasks in this comparison, averaging 65.6 versus 59. Inside this category, AA-Omniscience Hallucination Rate is the benchmark that creates the most daylight between them.
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