Model Comparison
Qwen3.6-27B vs MiMo-V2.5Which is better in 2026?
MiMo-V2.5 significantly outperforms across most benchmarks. MiMo-V2.5 is 6.4x cheaper per token.
Verdict: Qwen3.6-27B vs MiMo-V2.5 — which is better?
Qwen3.6-27B (by Alibaba Cloud / Qwen Team) and MiMo-V2.5 (by Xiaomi) are two of the AI models people compare most. Here is how they stack up on benchmarks, price and capabilities, and which one to pick in 2026.
Qwen3.6-27B outperforms in 0 benchmarks, while MiMo-V2.5 is better at 5 benchmarks (CharXiv-R, Claw-Eval, MMMU-Pro, SWE-Bench Pro, Terminal-Bench 2.0). MiMo-V2.5 significantly outperforms across most benchmarks.
On price, MiMo-V2.5 is roughly 6.4x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
MiMo-V2.5 also accepts a larger context window (1,048,576 input tokens), making it the stronger choice for long documents and large codebases.
Choose Qwen3.6-27B if…
- you want predictable pricing at $0.60/M input and $3.60/M output
Choose MiMo-V2.5 if…
- you want the strongest raw capability — it leads on 5 of 5 shared benchmarks
- cost matters — it's about 6.4x 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 Apr 2026
Performance Benchmarks
Comparative analysis across standard metrics
Qwen3.6-27B outperforms in 0 benchmarks, while MiMo-V2.5 is better at 5 benchmarks (CharXiv-R, Claw-Eval, MMMU-Pro, SWE-Bench Pro, Terminal-Bench 2.0).
MiMo-V2.5 significantly outperforms across most benchmarks.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
For input processing, Qwen3.6-27B ($0.60/1M tokens) is 3.6x more expensive than MiMo-V2.5 ($0.17/1M tokens).
For output processing, Qwen3.6-27B ($3.60/1M tokens) is 10.7x more expensive than MiMo-V2.5 ($0.34/1M tokens).
In conclusion, Qwen3.6-27B is more expensive than MiMo-V2.5.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
MiMo-V2.5 has 283.0B more parameters than Qwen3.6-27B, making it 1018.6% larger.
Context Window
Maximum input and output token capacity
MiMo-V2.5 accepts 1,048,576 input tokens compared to Qwen3.6-27B's 262,144 tokens. MiMo-V2.5 can generate longer responses up to 131,072 tokens, while Qwen3.6-27B is limited to 65,536 tokens.
Input Capabilities
Supported data types and modalities
Both Qwen3.6-27B and MiMo-V2.5 support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
Qwen3.6-27B
MiMo-V2.5
License
Usage and distribution terms
Qwen3.6-27B is licensed under Apache 2.0, while MiMo-V2.5 uses MIT.
License differences may affect how you can use these models in commercial or open-source projects.
Apache 2.0
Open weights
MIT
Open weights
Release Timeline
When each model was launched
Qwen3.6-27B was released on 2026-04-21, while MiMo-V2.5 was released on 2026-04-22.
MiMo-V2.5 is 0 month newer than Qwen3.6-27B.
Apr 21, 2026
2 months ago
Apr 22, 2026
2 months ago
1d newerKnowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
Qwen3.6-27B is available from Novita. MiMo-V2.5 is available from Novita, DeepInfra.
Qwen3.6-27B
MiMo-V2.5
Outputs Comparison
Key Takeaways
Qwen3.6-27B
View detailsAlibaba Cloud / Qwen Team
No standout differentiators in the data we have for this pair.
MiMo-V2.5
View detailsXiaomi
Detailed Comparison
Interactive Arena
Judge for yourself.
Run your own prompts against Qwen3.6-27B and MiMo-V2.5 side-by-side, then vote on the output you prefer.
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FAQ
Common questions about Qwen3.6-27B vs MiMo-V2.5.