AI BENCHY
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#30

MiMo-V2.5

Xiaomi Release: 2026-04-22 Tested on: 2026-05-08 15:28 xiaomi/mimo-v2.5::medium
310B total (15B active)MoEOpen source
(medium) (none)

Summary

MiMo-V2.5 scores 7.8 on AI BENCHY and ranks #30. It has 10.0 reliability, a 75.9% pass rate, $0.253 total cost, and 14.40s average response time.

What makes MiMo-V2.5 unique: Its total benchmark cost is unusually low for its score range.

Model facts

Researched on 2026-08-12

Reported
Parameters
310B total (15B active)
Architecture
MoE
Availability
Open source
License
MIT

Score

7.8

Consistency

8.6

Total Output Tokens

119,028

Total Input Tokens

0

Input Price

$0.400 / 1M

Output Price

$2.000 / 1M

Tests Correct

Wrong Tests: 6

Attempt pass rate: 75.9%

Flaky tests

3

Flaky tests had mixed outcomes across runs (at least one pass and one fail).

Response Time (avg)

14.40s

Response Time (max): 86.93s

Response Time (total): 259.20s

Hamster playing table tennis

Prompt: Create a detailed SVG illustration of a hamster playing table tennis.

#30 MiMo-V2.5

medium
Cost
$0.002
Time
54.8s
Tokens
5,247 tok

Run history

Tested on Score Reliability Tests Correct Total Cost Compare
2026-08-14 02:41 Re-test 6.4 10.0 $0.104 Compare
2026-07-16 22:46 New test added 6.5 10.0 $0.082 Compare
2026-06-04 13:56 New test added 7.3 10.0 $0.063 Compare
2026-05-22 12:59 New test added 7.4 10.0 $0.346 Compare
2026-05-08 15:28 Suite changed 7.8 10.0 $0.253 Current run
2026-04-22 22:54 First recorded run 7.8 N/A $0.253 Compare

This run used a different benchmark suite. Keep suite changes in mind when reading historical movement.

Run comparison

RunBenchmark coverageScoreConsistencyReliabilityTests CorrectFlaky testsTotal Output TokensTotal Input TokensTotal CostResponse Time (avg)
2026-05-08 15:28 · Suite changed54/54 attempts7.88.610.012/183119,0280$0.25314.40s
2026-08-14 02:41 · Re-test66/66 attempts6.47.510.011/227327,048105,456$0.10446.28s
Difference+1.4+1.10.0+1-4-208020-105456+$0.149-31878ms

Benchmark coverage differs: 54/54 attempts (Target: 3 repeats per test) versus 66/66 attempts (Target: 3 repeats per test). Totals and repeat-sensitive metrics are not directly comparable.

These two runs used different benchmark suites, so the deltas reflect both model changes and suite changes.

Charts

Choose the first model, then click a second model to open a side-by-side page.

Total Output Tokens

Score vs Total Output Tokens

Quick Compare

Category Breakdown

Category Score Consistency Tests Correct
Anti-AI Tricks 10.0 10.0
Coding 10.0 10.0
Combined 10.0 10.0
Data parsing and extraction 2.7 5.7
Domain specific 5.3 10.0
General Intelligence 5.4 2.5
Instructions following 9.9 10.0
Puzzle Solving 8.2 7.2
Tool Calling 10.0 10.0

Compared models