Head-to-head comparison across 1benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
Gemma 4 E4B
41
Ling 2.6 Flash
44
Pick Ling 2.6 Flash if you want the stronger benchmark profile. Gemma 4 E4B only becomes the better choice if knowledge is the priority or you want the cheaper token bill.
Knowledge
+6.6 difference
Gemma 4 E4B
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 E4B only becomes the better choice if knowledge is the priority or you want the cheaper token bill.
Ling 2.6 Flash has the cleaner provisional overall profile here, landing at 44 versus 41. It is a real lead, but still close enough that category-level strengths matter more than the headline number.
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 E4B. That is roughly Infinityx on output cost alone. 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.
Ling 2.6 Flash is ahead on BenchLM's provisional leaderboard, 44 to 41. 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, GPQA is the benchmark that creates the most daylight between them.
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