Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
DeepSeek V3
35
LFM2.5-VL-450M
0
Pick DeepSeek V3 if you want the stronger benchmark profile. LFM2.5-VL-450M only becomes the better choice if you want the cheaper token bill.
Knowledge
+48.4 difference
Inst. Following
+24.9 difference
DeepSeek V3
LFM2.5-VL-450M
$0.27 / $1.1
$0 / $0
N/A
N/A
N/A
N/A
128K
128K
Pick DeepSeek V3 if you want the stronger benchmark profile. LFM2.5-VL-450M only becomes the better choice if you want the cheaper token bill.
DeepSeek V3 is clearly ahead on the provisional aggregate, 35 to 0. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
DeepSeek V3's sharpest advantage is in knowledge, where it averages 70 against 21.6. The single biggest benchmark swing on the page is MMLU-Pro, 75.9% to 19.3%.
DeepSeek V3 is also the more expensive model on tokens at $0.27 input / $1.10 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for LFM2.5-VL-450M. That is roughly Infinityx on output cost alone.
DeepSeek V3 is ahead on BenchLM's provisional leaderboard, 35 to 0. The biggest single separator in this matchup is MMLU-Pro, where the scores are 75.9% and 19.3%.
DeepSeek V3 has the edge for knowledge tasks in this comparison, averaging 70 versus 21.6. Inside this category, MMLU-Pro is the benchmark that creates the most daylight between them.
DeepSeek V3 has the edge for instruction following in this comparison, averaging 86.1 versus 61.2. Inside this category, IFEval is the benchmark that creates the most daylight between them.
Estimates at 50,000 req/day · 1000 tokens/req average.
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