MiMo-V2.6-Flash vs Qwen3.8-27B
MiMo-V2.6-Flash and Qwen3.8-27B are closely matched at 45.7 and 44.8 on the LLM Stats Score. MiMo-V2.6-Flash is 6.0x cheaper per token.
Xiaomi · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
MiMo-V2.6-Flash and Qwen3.8-27B are closely matched on the overall LLM Stats Score at 45.7 and 44.8.
The models split the 4 individual benchmarks reported for both models evenly.
On price, MiMo-V2.6-Flash is roughly 6.0x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
MiMo-V2.6-Flash also accepts a larger context window (1,048,576 input tokens), making it the stronger choice for long documents and large codebases.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose MiMo-V2.6-Flash
- cost matters — it's about 6.0x cheaper per token
- you process long inputs — it offers a 1,048,576 token context window
- you want the most recent training data — it shipped Sep 2026
Choose Qwen3.8-27B
- you want predictable pricing at $0.40/M input and $3.00/M output
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
16 reported for MiMo-V2.6-Flash · 26 for Qwen3.8-27B
MiMo-V2.6-Flash outperforms in 2 benchmarks (DeepSWE 1.1, Terminal-Bench 2.1), while Qwen3.8-27B is better at 2 benchmarks (Agents' Last Exam, OSWorld-Verified).
Both models are evenly matched across the benchmarks.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, MiMo-V2.6-Flash ($0.14/1M tokens) is 2.9x cheaper than Qwen3.8-27B ($0.40/1M tokens).
For output processing, MiMo-V2.6-Flash ($0.28/1M tokens) is 10.7x cheaper than Qwen3.8-27B ($3.00/1M tokens).
In conclusion, Qwen3.8-27B is more expensive than MiMo-V2.6-Flash.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
MiMo-V2.6-Flash has 281.2B more parameters than Qwen3.8-27B, making it 1012.3% larger.
Context Window
Maximum input and output token capacity
MiMo-V2.6-Flash accepts 1,048,576 input tokens compared to Qwen3.8-27B's 262,144 tokens. Only Qwen3.8-27B specifies output context (262,144 tokens).
Input capabilities
Documented input modalities across available providers
Both MiMo-V2.6-Flash and Qwen3.8-27B support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
MiMo-V2.6-Flash
Qwen3.8-27B
License
Usage and distribution terms
MiMo-V2.6-Flash is licensed under MIT, while Qwen3.8-27B uses Apache 2.0.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Apache 2.0
Open weights
Release Timeline
When each model was launched
MiMo-V2.6-Flash was released on 2026-09-22, while Qwen3.8-27B was released on 2026-08-14.
MiMo-V2.6-Flash is 1 month newer than Qwen3.8-27B.
Sep 22, 2026
0 days ago
1mo newerAug 14, 2026
1 months ago
Knowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
MiMo-V2.6-Flash is available from Xiaomi. Qwen3.8-27B is available from DeepInfra, FriendliAI.
MiMo-V2.6-Flash
Qwen3.8-27B
Outputs Comparison
Judge for yourself.
Run your own prompts against MiMo-V2.6-Flash and Qwen3.8-27B side-by-side, then vote on the output you prefer.
FAQ
Common questions about MiMo-V2.6-Flash vs Qwen3.8-27B.