Side-by-side comparison
GLM 4.5V vs MiMo-V2-Omni
Xiaomi
MiMo-V2-Omni
👁 Vision
🔧 Tools
{} JSON
Input / 1M
$0.4000
Output / 1M
$2.0000
| GLM 4.5V | MiMo-V2-Omni | |
|---|---|---|
| Provider | Z.ai | Xiaomi |
| Context window Maximum tokens (input + output) the model can process in a single request. Glossary → | 65,536 | 262,144 |
| Capabilities Optional capabilities the model advertises: vision (images), tools (function calling), json_mode (structured output). | vision, tools | vision, tools, json_mode |
| Input $ / 1M tokens Cost for tokens you send (prompt + context). Cheaper side highlighted. Glossary → | 0.6000 | 0.4000 |
| Output $ / 1M tokens Cost for tokens the model generates. Output is normally 3–5× pricier than input. Glossary → | 1.8000 | 2.0000 |
Frequently asked questions
Which is cheaper, GLM 4.5V or MiMo-V2-Omni?
GLM 4.5V is cheaper than MiMo-V2-Omni on a 50/50 input/output blend by about $0 per 1M tokens. Exact savings depend on your input-vs-output ratio — use the cost calculator on this page for a workload-specific estimate.
Which has a larger context window, GLM 4.5V or MiMo-V2-Omni?
MiMo-V2-Omni has the larger context window at 262k tokens versus 66k tokens for GLM 4.5V. That means MiMo-V2-Omni can ingest about 4.0x as much text per request.
What is the difference between GLM 4.5V and MiMo-V2-Omni?
GLM 4.5V comes from Z.ai; MiMo-V2-Omni comes from Xiaomi. They differ in pricing, context window, and supported capabilities — see the side-by-side table on this page for the exact figures, refreshed nightly.