MiMo-V2.6-Pro vs Qwen3.5-9B
MiMo-V2.6-Pro leads the LLM Stats Score 49.8 to 24.7. Qwen3.5-9B is 4.8x cheaper per token.
Xiaomi · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
MiMo-V2.6-Pro leads the overall LLM Stats Score 49.8 to 24.7, ranking #19 overall.
On price, Qwen3.5-9B is roughly 4.8x 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 49.8 and ranks #19 on LLM Stats
- your work emphasizes reasoning and agents — it leads those capability indexes
- 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 Qwen3.5-9B
- cost matters — it's about 4.8x cheaper per token
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 · 25 for Qwen3.5-9B
MiMo-V2.6-Pro and Qwen3.5-9Bdon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.
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 4.3x more expensive than Qwen3.5-9B ($0.10/1M tokens).
For output processing, MiMo-V2.6-Pro ($0.87/1M tokens) is 5.8x more expensive than Qwen3.5-9B ($0.15/1M tokens).
In conclusion, MiMo-V2.6-Pro is more expensive than Qwen3.5-9B.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
MiMo-V2.6-Pro has 1011.0B more parameters than Qwen3.5-9B, making it 11233.3% larger.
Context Window
Maximum input and output token capacity
MiMo-V2.6-Pro accepts 1,048,576 input tokens compared to Qwen3.5-9B's 262,144 tokens. Only Qwen3.5-9B specifies output context (262,144 tokens).
Input capabilities
Documented input modalities across available providers
Both MiMo-V2.6-Pro and Qwen3.5-9B support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
MiMo-V2.6-Pro
Qwen3.5-9B
License
Usage and distribution terms
MiMo-V2.6-Pro is licensed under MIT, while Qwen3.5-9B 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 Qwen3.5-9B was released on 2026-03-02.
MiMo-V2.6-Pro is 7 months newer than Qwen3.5-9B.
Sep 22, 2026
0 days ago
6mo newerMar 2, 2026
6 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. Qwen3.5-9B is available from DeepInfra.
MiMo-V2.6-Pro
Qwen3.5-9B
Outputs Comparison
Judge for yourself.
Run your own prompts against MiMo-V2.6-Pro and Qwen3.5-9B side-by-side, then vote on the output you prefer.
FAQ
Common questions about MiMo-V2.6-Pro vs Qwen3.5-9B.