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MiMo V2 Flash (free)
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MiMo V2 Flash (free)

mimo-v2-flash-freellms.txt
Xiaomi
MiMo-V2-Flash is an open-source foundation language model developed by Xiaomi. It adopts a MoE architecture with 309B total parameters and 15B active parameters per inference, balancing performance and efficiency. The model features a hybrid attention architecture, supports a hybrid-thinking toggle, and offers a 256K context window, enabling strong capabilities in complex reasoning, code generation, and agent-based scenarios. On SWE-bench Verified and SWE-bench Multilingual, MiMo-V2-Flash ranks #1 among open-source models globally, delivering performance comparable to Claude Sonnet 4.5 while costing only about 3.5% as much.

Pricing

  • Input Tokens: $0.000 /M tokens
  • Output Tokens: $0.000 /M tokens
  • Cache Read: $0.000 /M tokens

Input Modalities

  • Text

Output Modalities

  • Text

Capabilities

  • Web

Providers

Xiaomi mimo-v2-flash-free
Pricing$0.000$0.000
Cache$0.000
Context262K
Max output262K
Latency1.6S
Throughput130.4TPS
Uptime
0.00% uptime 2 days ago
0.00% uptime yesterday
0.00% uptime today

Performance for mimo-v2-flash-free

Uptime is the percentage of requests that succeeded over the past 72 hours. AIHubMix continuously monitors every provider and automatically retries with the next-best provider when one returns an error or responds too slowly; Latency is total round-trip time (lower is better); Throughput is how fast the model writes (tokens per second, higher is better).

Uptime
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Latency
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Throughput
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Try this model

Python
import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["AIHUBMIX_API_KEY"],
    base_url="https://aihubmix.com/v1",
)

response = client.chat.completions.create(
    model="mimo-v2-flash-free",
    messages=[
      {
        "role": "user",
        "content": "Hello, how are you?"
      }
    ],
    max_tokens=1024,
    stream=False,
)

print(response.choices[0].message.content)

Frequently asked questions

What is MiMo V2 Flash (free)?

MiMo-V2-Flash is an open-source foundation language model developed by Xiaomi. It adopts a MoE architecture with 309B total parameters and 15B active parameters per inference, balancing performance and efficiency. The model features a hybrid attention architecture, supports a hybrid-thinking toggle, and offers a 256K context window, enabling strong capabilities in complex reasoning, code generation, and agent-based scenarios. On SWE-bench Verified and SWE-bench Multilingual, MiMo-V2-Flash ranks #1 among open-source models globally, delivering performance comparable to Claude Sonnet 4.5 while costing only about 3.5% as much.
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