Noometry

Model comparison

MiMo-V2.6-Flash vs Qwen3.8 27B

MiMo-V2.6-Flash is the stronger model overall, scoring 48.5 to 46.0 on the Noometry Index.

Last verified . 19 shared benchmarks.

MiMo-V2.6-Flash Xiaomi

48.5

Rank #55 Confirmed

Qwen3.8 27B Alibaba (Qwen)

46.0

Rank #68 Confirmed

Summary

  • They share 19 benchmarks with published results for both. MiMo-V2.6-Flash scores higher in 6 categories and Qwen3.8 27B in 3 categories; 5 gaps are clear of the uncertainty.
  • The widest gap is in math, where MiMo-V2.6-Flash leads 51.9 to 37.1.
  • The biggest single-benchmark swing is ProofBench: 63% for MiMo-V2.6-Flash and 16% for Qwen3.8 27B.
  • MiMo-V2.6-Flash is cheaper at $0.14 / $0.28 per million input/output tokens, against $0.99 / $1.49 for Qwen3.8 27B.
  • MiMo-V2.6-Flash accepts more context: 1.05M tokens versus 262K.

Side by side

MiMo-V2.6-FlashQwen3.8 27B
ProviderXiaomiAlibaba (Qwen)
Noometry Index48.546.0
Released2026-09-212026-08-14
WeightsOpenOpen
Context window1.05M262K
Max output131K33K
Input $ / M tokens$0.14$0.99
Output $ / M tokens$0.28$1.49
Results tracked1931

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Category by category

Coding MiMo-V2.6-Flash leads

MiMo-V2.6-Flash: 53.4 (#30), Qwen3.8 27B: 50.5 (#44)

BenchmarkMiMo-V2.6-FlashQwen3.8 27B
LMArena WebDev16371593
SciCode51.3%46.6%
LMArena Coding15041482

Agentic & Tool Use Not comparable

MiMo-V2.6-Flash: —, Qwen3.8 27B: 32.9 (#57)

BenchmarkMiMo-V2.6-FlashQwen3.8 27B
APEX-Agents—47.5%

Reasoning Qwen3.8 27B leads

MiMo-V2.6-Flash: 36.5 (#66), Qwen3.8 27B: 41.0 (#54)

BenchmarkMiMo-V2.6-FlashQwen3.8 27B
CritPt12%5.4%
LMArena Hard Prompts14821460
ARC-AGI-2—42.4%
NYT Connections (extended)—54.5%
ARC-AGI-1—87.5%
DTBench—88%
LMCA—41.4%
Surface Evolver Bench—45%
Epoch Capabilities Index—149.38

Math MiMo-V2.6-Flash leads

MiMo-V2.6-Flash: 51.9 (#52), Qwen3.8 27B: 37.1 (#161)

BenchmarkMiMo-V2.6-FlashQwen3.8 27B
ProofBench63%16%
LMArena Math14681456

Knowledge Too close to call

MiMo-V2.6-Flash: 42.2 (#99), Qwen3.8 27B: 41.6 (#109)

BenchmarkMiMo-V2.6-FlashQwen3.8 27B
LMArena Expert15011482

Multimodal Too close to call

MiMo-V2.6-Flash: 40.5 (#47), Qwen3.8 27B: 41.3 (#37)

BenchmarkMiMo-V2.6-FlashQwen3.8 27B
LMArena Vision12591271

Multilingual Too close to call

MiMo-V2.6-Flash: 54.0 (#51), Qwen3.8 27B: 53.7 (#60)

BenchmarkMiMo-V2.6-FlashQwen3.8 27B
LMArena Non-English14341430
LMArena Chinese15111504
LMArena French14751465
LMArena Russian14091415
LMArena Spanish14561448
LMArena German—1438
LMArena Japanese—1384
LMArena Korean—1393

Instruction Following MiMo-V2.6-Flash leads

MiMo-V2.6-Flash: 76.8 (#35), Qwen3.8 27B: 75.8 (#53)

BenchmarkMiMo-V2.6-FlashQwen3.8 27B
LMArena Instruction Following14631439

Long Context Too close to call

MiMo-V2.6-Flash: 44.8 (#57), Qwen3.8 27B: 44.3 (#70)

BenchmarkMiMo-V2.6-FlashQwen3.8 27B
LMArena Longer Query14631450

Writing & Preference Qwen3.8 27B leads

MiMo-V2.6-Flash: 63.1 (#67), Qwen3.8 27B: 65.8 (#43)

BenchmarkMiMo-V2.6-FlashQwen3.8 27B
LMArena Text14551441
LMArena Creative Writing14001384
LMArena Multi-Turn14511441
EQ-Bench Creative Writing—1671

Frequently asked questions

Is MiMo-V2.6-Flash better than Qwen3.8 27B?

MiMo-V2.6-Flash is the stronger model overall, scoring 48.5 to 46.0 on the Noometry Index.

Which is cheaper, MiMo-V2.6-Flash or Qwen3.8 27B?

MiMo-V2.6-Flash is cheaper. It lists at $0.14 per million input tokens and $0.28 per million output tokens; Qwen3.8 27B lists at $0.99 and $1.49.

Is MiMo-V2.6-Flash or Qwen3.8 27B better for coding?

MiMo-V2.6-Flash scores higher on coding benchmarks: 53.4 versus 50.5 in the Noometry coding category.

Which has the bigger context window?

MiMo-V2.6-Flash does, with 1.05M tokens against 262K.

How many benchmarks do MiMo-V2.6-Flash and Qwen3.8 27B share?

19 benchmarks have published results for both models. MiMo-V2.6-Flash has 19 scored results on Noometry and Qwen3.8 27B has 31.

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