MiMo-V2.6-Pro vs Qwen3.8-27B
MiMo-V2.6-Pro leads the LLM Stats Score 50.0 to 44.8. MiMo-V2.6-Pro is 1.9x cheaper per token.
Xiaomi · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
MiMo-V2.6-Pro leads the overall LLM Stats Score 50.0 to 44.8, ranking #18 overall.
The models split the 4 individual benchmarks reported for both models evenly.
On price, MiMo-V2.6-Pro is roughly 1.9x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
MiMo-V2.6-Pro 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-Pro
- overall performance matters — it scores 50.0 and ranks #18 on LLM Stats
- your work emphasizes coding and agents — it leads those capability indexes
- cost matters — it's about 1.9x 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
18 reported for MiMo-V2.6-Pro · 26 for Qwen3.8-27B
MiMo-V2.6-Pro 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-Pro ($0.43/1M tokens) is 1.1x more expensive than Qwen3.8-27B ($0.40/1M tokens).
For output processing, MiMo-V2.6-Pro ($0.87/1M tokens) is 3.4x cheaper than Qwen3.8-27B ($3.00/1M tokens).
In conclusion, Qwen3.8-27B is more expensive than MiMo-V2.6-Pro.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
MiMo-V2.6-Pro has 992.2B more parameters than Qwen3.8-27B, making it 3571.5% larger.
Context Window
Maximum input and output token capacity
MiMo-V2.6-Pro 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-Pro and Qwen3.8-27B support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
MiMo-V2.6-Pro
Qwen3.8-27B
License
Usage and distribution terms
MiMo-V2.6-Pro 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-Pro was released on 2026-09-22, while Qwen3.8-27B was released on 2026-08-14.
MiMo-V2.6-Pro 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-Pro is available from Xiaomi. Qwen3.8-27B is available from DeepInfra, FriendliAI.
MiMo-V2.6-Pro
Qwen3.8-27B
Outputs Comparison
Judge for yourself.
Run your own prompts against MiMo-V2.6-Pro and Qwen3.8-27B side-by-side, then vote on the output you prefer.
FAQ
Common questions about MiMo-V2.6-Pro vs Qwen3.8-27B.