Head-to-head comparison across 3benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
MiMo-V2.5
74
Qwen3.5 397B
66
Verified leaderboard positions: MiMo-V2.5 unranked · Qwen3.5 397B #11
Pick MiMo-V2.5 if you want the stronger benchmark profile. Qwen3.5 397B only becomes the better choice if coding is the priority or you want the cheaper token bill.
Agentic
+9.6 difference
Coding
+4.2 difference
Multimodal
+1.1 difference
MiMo-V2.5
Qwen3.5 397B
$0.4 / $2
$0 / $0
N/A
96 t/s
N/A
2.44s
1M
128K
Pick MiMo-V2.5 if you want the stronger benchmark profile. Qwen3.5 397B only becomes the better choice if coding is the priority or you want the cheaper token bill.
MiMo-V2.5 is clearly ahead on the provisional aggregate, 74 to 66. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
MiMo-V2.5's sharpest advantage is in agentic, where it averages 65.8 against 56.2. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 65.8% to 52.5%. Qwen3.5 397B does hit back in coding, so the answer changes if that is the part of the workload you care about most.
MiMo-V2.5 is also the more expensive model on tokens at $0.40 input / $2.00 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Qwen3.5 397B. That is roughly Infinityx on output cost alone. MiMo-V2.5 is the reasoning model in the pair, while Qwen3.5 397B 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. MiMo-V2.5 gives you the larger context window at 1M, compared with 128K for Qwen3.5 397B.
MiMo-V2.5 is ahead on BenchLM's provisional leaderboard, 74 to 66. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 65.8% and 52.5%.
Qwen3.5 397B has the edge for coding in this comparison, averaging 60.3 versus 56.1. Inside this category, SWE-bench Pro is the benchmark that creates the most daylight between them.
MiMo-V2.5 has the edge for agentic tasks in this comparison, averaging 65.8 versus 56.2. Inside this category, Claw-Eval is the benchmark that creates the most daylight between them.
Qwen3.5 397B has the edge for multimodal and grounded tasks in this comparison, averaging 79 versus 77.9. Inside this category, MMMU-Pro is the benchmark that creates the most daylight between them.
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