Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GPT-5.4 mini
73
MiMo-V2.5
74
Pick MiMo-V2.5 if you want the stronger benchmark profile. GPT-5.4 mini only becomes the better choice if its workflow or ecosystem matters more than the raw scoreboard.
Agentic
+0.2 difference
Multimodal
+1.3 difference
GPT-5.4 mini
MiMo-V2.5
$0.75 / $4.5
$0.4 / $2
201 t/s
N/A
3.85s
N/A
400K
1M
Pick MiMo-V2.5 if you want the stronger benchmark profile. GPT-5.4 mini only becomes the better choice if its workflow or ecosystem matters more than the raw scoreboard.
MiMo-V2.5 finishes one point ahead on BenchLM's provisional leaderboard, 74 to 73. 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.
MiMo-V2.5's sharpest advantage is in multimodal & grounded, where it averages 77.9 against 76.6. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 60% to 65.8%.
GPT-5.4 mini is also the more expensive model on tokens at $0.75 input / $4.50 output per 1M tokens, versus $0.40 input / $2.00 output per 1M tokens for MiMo-V2.5. That is roughly 2.3x on output cost alone. MiMo-V2.5 gives you the larger context window at 1M, compared with 400K for GPT-5.4 mini.
MiMo-V2.5 is ahead on BenchLM's provisional leaderboard, 74 to 73. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 60% and 65.8%.
MiMo-V2.5 has the edge for agentic tasks in this comparison, averaging 65.8 versus 65.6. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
MiMo-V2.5 has the edge for multimodal and grounded tasks in this comparison, averaging 77.9 versus 76.6. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
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