Last refreshed 2026-06-29. Next refresh: weekly.
Why use Xiaomi MiMo-V2-Flash on Vercel AI Gateway?
Vercel AI Gateway offers Xiaomi MiMo-V2-Flash with pay-as-you-go pricing at $0.10/1M input tokens. Vercel AI Gateway is a unified AI proxy providing a single OpenAI-compatible API endpoint to 275+ models from 25+ providers including Anthropic, OpenAI, Google, Meta, Mistral, DeepSeek, xAI, Alibaba, Amazon, ByteDance, Cohere, MiniMax, MoonshotAI, KwaiPilot, Black Forest Labs, Recraft, Voyage AI, NVIDIA, and more.
Compare Xiaomi MiMo-V2-Flash across 2 providers to find the best fit for your use caseSetup recipe
Python + curlpip install openaiexport AI_GATEWAY_API_KEY=...import os
from openai import OpenAI
client = OpenAI(
api_key=os.environ["AI_GATEWAY_API_KEY"],xiaomi/mimo-v2-flashRequest example
import os
from openai import OpenAI
client = OpenAI(
api_key=os.environ["AI_GATEWAY_API_KEY"],
base_url="https://ai-gateway.vercel.sh/v1"
)
response = client.chat.completions.create(
model="xiaomi/mimo-v2-flash",
messages=[{"role": "user", "content": "Hello"}]
)
print(response.choices[0].message.content)Gotchas
- Use provider model ID "xiaomi/mimo-v2-flash", not the LLMReference slug "xiaomi-mimo-v2-flash".
- creator/model-name e.g. kwaipilot/kat-coder-pro-v2
- The examples expect AI_GATEWAY_API_KEY; rename it only if your application config maps the new variable.
Compare Xiaomi MiMo-V2-Flash Across Providers
| Provider | Input (per 1M) | Output (per 1M) |
|---|---|---|
| Vercel AI Gateway | $0.10 | $0.30 |
| Novita AI | $0.10 | $0.30 |
Pricing
| Type | Price (per 1M) |
|---|---|
| Input tokens | $0.10 |
| Output tokens | $0.30 |
Capabilities
About Xiaomi MiMo-V2-Flash
MiMo-V2-Flash is Xiaomi's efficient open-source Mixture-of-Experts model, announced December 17, 2025 at Xiaomi's Human-Car-Home Ecosystem Partner Conference. It has 309B total parameters with 15B active, uses hybrid attention that interleaves Sliding Window Attention and Global Attention, and extends native 32K context to 256K. Multi-Token Prediction enables about 2.6x speculative decoding speedup. The model was distributed with weights on Hugging Face and ranked highly on SWE-Bench Verified and multilingual benchmarks at research time.