Head-to-head comparison across 2benchmark categories. Overall scores shown here use BenchLM's provisional ranking lane.
GLM-5
67
MiMo-V2-Flash
59
Verified leaderboard positions: GLM-5 #21 · MiMo-V2-Flash unranked
Pick GLM-5 if you want the stronger benchmark profile. MiMo-V2-Flash only becomes the better choice if knowledge is the priority or you want the cheaper token bill.
Coding
+10.2 difference
Knowledge
+13.8 difference
GLM-5
MiMo-V2-Flash
$1 / $3.2
$0 / $0
74 t/s
129 t/s
1.64s
2.14s
200K
256K
Pick GLM-5 if you want the stronger benchmark profile. MiMo-V2-Flash only becomes the better choice if knowledge is the priority or you want the cheaper token bill.
GLM-5 is clearly ahead on the provisional aggregate, 67 to 59. The gap is large enough that you do not need to squint at the spreadsheet to see the difference.
GLM-5 is also the more expensive model on tokens at $1.00 input / $3.20 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for MiMo-V2-Flash. That is roughly Infinityx on output cost alone. MiMo-V2-Flash is the reasoning model in the pair, while GLM-5 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-Flash gives you the larger context window at 256K, compared with 200K for GLM-5.
GLM-5 is ahead on BenchLM's provisional leaderboard, 67 to 59. The biggest single separator in this matchup is SWE-bench Verified, where the scores are 77.8% and 73.4%.
MiMo-V2-Flash has the edge for knowledge tasks in this comparison, averaging 84.5 versus 70.7. Inside this category, AA-Omniscience Index is the benchmark that creates the most daylight between them.
MiMo-V2-Flash has the edge for coding in this comparison, averaging 73.4 versus 63.2. Inside this category, AA-SciCode is the benchmark that creates the most daylight between them.
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