Model Comparison
Qwen3.5-397B-A17B 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: Qwen3.5-397B-A17B vs MiMo-V2.5 — which is better?
Qwen3.5-397B-A17B (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.
Qwen3.5-397B-A17B outperforms in 0 benchmarks, while MiMo-V2.5 is better at 1 benchmark (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 Qwen3.5-397B-A17B 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 1 of 1 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
Qwen3.5-397B-A17B outperforms in 0 benchmarks, while MiMo-V2.5 is better at 1 benchmark (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, Qwen3.5-397B-A17B ($0.60/1M tokens) is 3.6x more expensive than MiMo-V2.5 ($0.17/1M tokens).
For output processing, Qwen3.5-397B-A17B ($3.60/1M tokens) is 10.7x more expensive than MiMo-V2.5 ($0.34/1M tokens).
In conclusion, Qwen3.5-397B-A17B is more expensive than MiMo-V2.5.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Qwen3.5-397B-A17B has 86.2B more parameters than MiMo-V2.5, making it 27.7% larger.
Context Window
Maximum input and output token capacity
MiMo-V2.5 accepts 1,048,576 input tokens compared to Qwen3.5-397B-A17B's 262,144 tokens. MiMo-V2.5 can generate longer responses up to 131,072 tokens, while Qwen3.5-397B-A17B is limited to 64,000 tokens.
Input Capabilities
Supported data types and modalities
Both Qwen3.5-397B-A17B and MiMo-V2.5 support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
Qwen3.5-397B-A17B
MiMo-V2.5
License
Usage and distribution terms
Qwen3.5-397B-A17B 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
Qwen3.5-397B-A17B was released on 2026-02-16, while MiMo-V2.5 was released on 2026-04-22.
MiMo-V2.5 is 2 months newer than Qwen3.5-397B-A17B.
Feb 16, 2026
3 months ago
Apr 22, 2026
1 months ago
2mo newerKnowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
Qwen3.5-397B-A17B is available from Novita. MiMo-V2.5 is available from Novita, DeepInfra.
Qwen3.5-397B-A17B
MiMo-V2.5
Outputs Comparison
Key Takeaways
Qwen3.5-397B-A17B
View detailsAlibaba Cloud / Qwen Team
No standout differentiators in the data we have for this pair.
MiMo-V2.5
View detailsXiaomi
Detailed Comparison
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FAQ
Common questions about Qwen3.5-397B-A17B vs MiMo-V2.5.