Side-by-side comparison
MiMo-V2-Flash vs Llama 3.3 70B Instruct
Meta
Llama 3.3 70B Instruct
🔧 Tools
{} JSON
Input / 1M
$0.1000
Output / 1M
$0.3200
| MiMo-V2-Flash | Llama 3.3 70B Instruct | |
|---|---|---|
| Provider | Xiaomi | Meta |
| Context window Maximum tokens (input + output) the model can process in a single request. Glossary → | 262,144 | 131,072 |
| Capabilities Optional capabilities the model advertises: vision (images), tools (function calling), json_mode (structured output). | tools, json_mode | tools, json_mode |
| Input $ / 1M tokens Cost for tokens you send (prompt + context). Cheaper side highlighted. Glossary → | 0.1000 | 0.1000 |
| Output $ / 1M tokens Cost for tokens the model generates. Output is normally 3–5× pricier than input. Glossary → | 0.3000 | 0.3200 |
Frequently asked questions
Which is cheaper, MiMo-V2-Flash or Llama 3.3 70B Instruct?
MiMo-V2-Flash is cheaper than Llama 3.3 70B Instruct on a 50/50 input/output blend by about $0.01 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, MiMo-V2-Flash or Llama 3.3 70B Instruct?
MiMo-V2-Flash has the larger context window at 262k tokens versus 131k tokens for Llama 3.3 70B Instruct. That means MiMo-V2-Flash can ingest about 2.0x as much text per request.
What is the difference between MiMo-V2-Flash and Llama 3.3 70B Instruct?
MiMo-V2-Flash comes from Xiaomi; Llama 3.3 70B Instruct comes from Meta. They differ in pricing, context window, and supported capabilities — see the side-by-side table on this page for the exact figures, refreshed nightly.