MiMo-V2.6-Flash vs Qwen2.5 7B Instruct
MiMo-V2.6-Flash leads the LLM Stats Score 45.6 to 2.5. MiMo-V2.6-Flash is 1.7x cheaper per token.
Xiaomi · Alibaba Cloud / Qwen Team · Updated for 2026
Which is better?
MiMo-V2.6-Flash leads the overall LLM Stats Score 45.6 to 2.5, ranking #29 overall.
On price, MiMo-V2.6-Flash 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-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
- overall performance matters — it scores 45.6 and ranks #29 on LLM Stats
- your work emphasizes reasoning and coding — it leads those capability indexes
- cost matters — it's about 1.7x 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 Qwen2.5 7B Instruct
- you want predictable pricing at $0.30/M input and $0.30/M output
At a glance
The differences that matter most.
Individual benchmarks
16 reported for MiMo-V2.6-Flash · 14 for Qwen2.5 7B Instruct
MiMo-V2.6-Flash and Qwen2.5 7B Instructdon'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-Flash ($0.14/1M tokens) is 2.1x cheaper than Qwen2.5 7B Instruct ($0.30/1M tokens).
For output processing, MiMo-V2.6-Flash ($0.28/1M tokens) is 1.1x cheaper than Qwen2.5 7B Instruct ($0.30/1M tokens).
In conclusion, Qwen2.5 7B Instruct 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 301.4B more parameters than Qwen2.5 7B Instruct, making it 3960.4% larger.
Context Window
Maximum input and output token capacity
MiMo-V2.6-Flash accepts 1,048,576 input tokens compared to Qwen2.5 7B Instruct's 131,072 tokens. Only Qwen2.5 7B Instruct specifies output context (8,192 tokens).
Input capabilities
Documented input modalities across available providers
MiMo-V2.6-Flash supports multimodal inputs, whereas Qwen2.5 7B Instruct does not.
MiMo-V2.6-Flash can handle both text and other forms of data like images, making it suitable for multimodal applications.
MiMo-V2.6-Flash
Qwen2.5 7B Instruct
License
Usage and distribution terms
MiMo-V2.6-Flash is licensed under MIT, while Qwen2.5 7B Instruct 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 Qwen2.5 7B Instruct was released on 2024-09-19.
MiMo-V2.6-Flash is 24 months newer than Qwen2.5 7B Instruct.
Sep 22, 2026
0 days ago
2.0yr newerSep 19, 2024
2.0 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-Flash is available from Xiaomi. Qwen2.5 7B Instruct is available from Together.
MiMo-V2.6-Flash
Qwen2.5 7B Instruct
Outputs Comparison
Judge for yourself.
Run your own prompts against MiMo-V2.6-Flash and Qwen2.5 7B Instruct side-by-side, then vote on the output you prefer.
FAQ
Common questions about MiMo-V2.6-Flash vs Qwen2.5 7B Instruct.