MiMo-V2.6-Pro vs Qwen3 VL 4B Thinking
MiMo-V2.6-Pro leads the LLM Stats Score 49.8 to 12.9. Qwen3 VL 4B Thinking is 1.7x 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 12.9, ranking #19 overall.
On price, Qwen3 VL 4B Thinking is roughly 1.7x 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 VL 4B Thinking
- cost matters — it's about 1.7x cheaper per token
At a glance
The differences that matter most.
Individual benchmarks
18 reported for MiMo-V2.6-Pro · 48 for Qwen3 VL 4B Thinking
MiMo-V2.6-Pro and Qwen3 VL 4B Thinkingdon'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 VL 4B Thinking ($0.10/1M tokens).
For output processing, MiMo-V2.6-Pro ($0.87/1M tokens) is 1.1x cheaper than Qwen3 VL 4B Thinking ($1.00/1M tokens).
In conclusion, MiMo-V2.6-Pro is more expensive than Qwen3 VL 4B Thinking.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
MiMo-V2.6-Pro has 1016.0B more parameters than Qwen3 VL 4B Thinking, making it 25400.0% larger.
Context Window
Maximum input and output token capacity
MiMo-V2.6-Pro accepts 1,048,576 input tokens compared to Qwen3 VL 4B Thinking's 262,144 tokens. Only Qwen3 VL 4B Thinking specifies output context (262,144 tokens).
Input capabilities
Documented input modalities across available providers
Both MiMo-V2.6-Pro and Qwen3 VL 4B Thinking support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
MiMo-V2.6-Pro
Qwen3 VL 4B Thinking
License
Usage and distribution terms
MiMo-V2.6-Pro is licensed under MIT, while Qwen3 VL 4B Thinking 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 VL 4B Thinking was released on 2025-09-22.
MiMo-V2.6-Pro is 12 months newer than Qwen3 VL 4B Thinking.
Sep 22, 2026
0 days ago
1.0yr newerSep 22, 2025
1.0 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 VL 4B Thinking is available from DeepInfra.
MiMo-V2.6-Pro
Qwen3 VL 4B Thinking
Outputs Comparison
Judge for yourself.
Run your own prompts against MiMo-V2.6-Pro and Qwen3 VL 4B Thinking side-by-side, then vote on the output you prefer.
FAQ
Common questions about MiMo-V2.6-Pro vs Qwen3 VL 4B Thinking.