Side-by-side comparison
Qwen3 VL 235B A22B Thinking vs MiMo-V2-Omni
Qwen
Qwen3 VL 235B A22B Thinking
👁 Vision
🔧 Tools
{} JSON
Input / 1M
$0.2600
Output / 1M
$2.6000
Xiaomi
MiMo-V2-Omni
👁 Vision
🔧 Tools
{} JSON
Input / 1M
$0.4000
Output / 1M
$2.0000
| Qwen3 VL 235B A22B Thinking | MiMo-V2-Omni | |
|---|---|---|
| Provider | Qwen | Xiaomi |
| Context window Maximum tokens (input + output) the model can process in a single request. Glossary → | 131,072 | 262,144 |
| Capabilities Optional capabilities the model advertises: vision (images), tools (function calling), json_mode (structured output). | vision, tools, json_mode | vision, tools, json_mode |
| Input $ / 1M tokens Cost for tokens you send (prompt + context). Cheaper side highlighted. Glossary → | 0.2600 | 0.4000 |
| Output $ / 1M tokens Cost for tokens the model generates. Output is normally 3–5× pricier than input. Glossary → | 2.6000 | 2.0000 |
Frequently asked questions
Which is cheaper, Qwen3 VL 235B A22B Thinking or MiMo-V2-Omni?
MiMo-V2-Omni is cheaper than Qwen3 VL 235B A22B Thinking on a 50/50 input/output blend by about $0.23 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, Qwen3 VL 235B A22B Thinking or MiMo-V2-Omni?
MiMo-V2-Omni has the larger context window at 262k tokens versus 131k tokens for Qwen3 VL 235B A22B Thinking. That means MiMo-V2-Omni can ingest about 2.0x as much text per request.
What is the difference between Qwen3 VL 235B A22B Thinking and MiMo-V2-Omni?
Qwen3 VL 235B A22B Thinking comes from Qwen; 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.