Model Comparison
GLM-5 vs MiMo-V2-OmniWhich is better in 2026?
GLM-5 significantly outperforms across most benchmarks. MiMo-V2-Omni is 1.9x cheaper per token.
Verdict: GLM-5 vs MiMo-V2-Omni — which is better?
GLM-5 (by Zhipu AI) and MiMo-V2-Omni (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.
GLM-5 outperforms in 1 benchmarks (SWE-Bench Verified), while MiMo-V2-Omni is better at 0 benchmarks. GLM-5 significantly outperforms across most benchmarks.
On price, MiMo-V2-Omni is roughly 1.9x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
MiMo-V2-Omni also accepts a larger context window (262,000 input tokens), making it the stronger choice for long documents and large codebases.
Choose GLM-5 if…
- you want the strongest raw capability — it leads on 1 of 1 shared benchmarks
- you need open weights you can self-host or fine-tune
Choose MiMo-V2-Omni if…
- cost matters — it's about 1.9x cheaper per token
- you process long inputs — it offers a 262,000 token context window
- you want the most recent training data — it shipped Mar 2026
Performance Benchmarks
Comparative analysis across standard metrics
GLM-5 outperforms in 1 benchmarks (SWE-Bench Verified), while MiMo-V2-Omni is better at 0 benchmarks.
GLM-5 significantly outperforms across most benchmarks.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
For input processing, GLM-5 ($1.00/1M tokens) is 2.5x more expensive than MiMo-V2-Omni ($0.40/1M tokens).
For output processing, GLM-5 ($3.20/1M tokens) is 1.6x more expensive than MiMo-V2-Omni ($2.00/1M tokens).
In conclusion, GLM-5 is more expensive than MiMo-V2-Omni.*
* Using a 3:1 ratio of input to output tokens
Context Window
Maximum input and output token capacity
MiMo-V2-Omni accepts 262,000 input tokens compared to GLM-5's 200,000 tokens. GLM-5 can generate longer responses up to 128,000 tokens, while MiMo-V2-Omni is limited to 16,384 tokens.
Input Capabilities
Supported data types and modalities
MiMo-V2-Omni supports multimodal inputs, whereas GLM-5 does not.
MiMo-V2-Omni can handle both text and other forms of data like images, making it suitable for multimodal applications.
GLM-5
MiMo-V2-Omni
License
Usage and distribution terms
GLM-5 is licensed under MIT, while MiMo-V2-Omni uses a proprietary license.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Proprietary
Closed source
Release Timeline
When each model was launched
GLM-5 was released on 2026-02-11, while MiMo-V2-Omni was released on 2026-03-18.
MiMo-V2-Omni is 1 month newer than GLM-5.
Feb 11, 2026
4 months ago
Mar 18, 2026
3 months ago
1mo newerKnowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
GLM-5 is available from FriendliAI, ZAI. MiMo-V2-Omni is available from Xiaomi.
GLM-5
MiMo-V2-Omni
Outputs Comparison
Key Takeaways
GLM-5
View detailsZhipu AI
MiMo-V2-Omni
View detailsXiaomi
Detailed Comparison
Interactive Arena
Judge for yourself.
Run your own prompts against GLM-5 and MiMo-V2-Omni side-by-side, then vote on the output you prefer.
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FAQ
Common questions about GLM-5 vs MiMo-V2-Omni.