MiMo-V2.6-Pro vs Qwen3 14B
MiMo-V2.6-Pro leads the LLM Stats Score 50.0 to 18.1. Qwen3 14B is 3.6x 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 18.1, ranking #18 overall.
On price, Qwen3 14B is roughly 3.6x 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 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 14B
- cost matters — it's about 3.6x cheaper per token
At a glance
The differences that matter most.
Individual benchmarks
18 reported for MiMo-V2.6-Pro · 17 for Qwen3 14B
MiMo-V2.6-Pro and Qwen3 14Bdon'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 3.6x more expensive than Qwen3 14B ($0.12/1M tokens).
For output processing, MiMo-V2.6-Pro ($0.87/1M tokens) is 3.6x more expensive than Qwen3 14B ($0.24/1M tokens).
In conclusion, MiMo-V2.6-Pro is more expensive than Qwen3 14B.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
MiMo-V2.6-Pro has 1006.0B more parameters than Qwen3 14B, making it 7185.7% larger.
Context Window
Maximum input and output token capacity
MiMo-V2.6-Pro accepts 1,048,576 input tokens compared to Qwen3 14B's 40,960 tokens. Only Qwen3 14B specifies output context (40,960 tokens).
Input capabilities
Documented input modalities across available providers
MiMo-V2.6-Pro supports multimodal inputs, whereas Qwen3 14B does not.
MiMo-V2.6-Pro can handle both text and other forms of data like images, making it suitable for multimodal applications.
MiMo-V2.6-Pro
Qwen3 14B
License
Usage and distribution terms
MiMo-V2.6-Pro is licensed under MIT, while Qwen3 14B 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 14B was released on 2025-04-28.
MiMo-V2.6-Pro is 17 months newer than Qwen3 14B.
Sep 22, 2026
0 days ago
1.4yr newerApr 28, 2025
1.4 years 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 14B is available from DeepInfra.
MiMo-V2.6-Pro
Qwen3 14B
Outputs Comparison
Judge for yourself.
Run your own prompts against MiMo-V2.6-Pro and Qwen3 14B side-by-side, then vote on the output you prefer.
FAQ
Common questions about MiMo-V2.6-Pro vs Qwen3 14B.