Xiaomi’s MiMo-7B: A Compact AI That Outsmarts Giants in Math and Reasoning

In a stunning debut, tech giant Xiaomi has stormed into the AI arena with MiMo-7B, a family of open-source language models that pack a mighty punch despite their modest 7-billion-parameter size. Announced on April 30, 2025, these models are turning heads by outperforming industry heavyweights in mathematical reasoning and coding tasks. With a jaw-dropping 95.8% score on the MATH-500 benchmark and a solid 55.4% on the prestigious AIME 2025, MiMo-7B is proving that bigger isn’t always better. Here’s why this release is a big deal and how you can tap into its brainpower.

Meet the MiMo-7B Family

Xiaomi’s MiMo-7B series isn’t just one model—it’s a quartet of finely tuned AI powerhouses, each built for specific strengths:

• MiMo-7B-Base: The foundation, pre-trained on a colossal 25 trillion tokens, designed to excel in reasoning tasks with a multi-token prediction approach for faster performance.
• MiMo-7B-SFT: A supervised fine-tuned version, optimized for precise instruction-following.
• MiMo-7B-RL-Zero: A reinforcement learning (RL) model trained directly from the base, hitting an impressive 93.6% on MATH-500.
• MiMo-7B-RL: The star of the show, built on the SFT model with additional RL training, scoring a remarkable 95.8% on MATH-500 and 55.4% on AIME 2025.

What makes MiMo-7B stand out is its ability to rival much larger models, like OpenAI’s o1-mini and Alibaba’s Qwen-32B, while using a fraction of the parameters. Posts on X are buzzing with excitement, with users calling it a “reasoning beast” that’s “punching way above its weight.”

Smashing Math and Coding Benchmarks

MiMo-7B’s performance is nothing short of extraordinary. On the MATH-500, a challenging high-school-level math benchmark, MiMo-7B-RL scored 95.8%, leaving Anthropic’s Claude-3.5-Sonnet-1022 (78.3%) in the dust. For context, MATH-500 tests complex problem-solving across algebra, geometry, and number theory, making this score a testament to MiMo’s razor-sharp reasoning.

On the AIME 2025, a prestigious math competition for top high-school students, MiMo-7B-RL earned 55.4%. While it trailed Alibaba’s newly released Qwen3-4B (65.6%), it still outperformed many larger models, including OpenAI’s o1-mini (50.7%). The AIME’s 15 increasingly difficult questions, each requiring an integer answer from 0 to 999, are a brutal test of logical precision, and MiMo’s score shows it’s ready to play with the big leagues.

Beyond math, MiMo shines in coding. On LiveCodeBench v5, a coding competition benchmark, it scored 57.8%, surpassing o1-mini and Qwen-32B. This versatility makes MiMo a go-to for tasks requiring rigorous logic, from academic research to software development.

Why This Matters

Mathematics and coding are the bedrock of modern innovation, but solving complex problems in these fields is time-intensive and error-prone. MiMo-7B changes the game by automating high-level reasoning with remarkable accuracy. For students, it’s a tireless tutor that can break down tricky proofs. For developers, it’s a coding partner that tackles algorithmic challenges. And for researchers, it’s a tool to accelerate discoveries in fields like cryptography and data science.

Xiaomi’s decision to open-source MiMo-7B is a bold move in a competitive AI landscape. Released just a day after Alibaba’s Qwen3 and hot on the heels of DeepSeek’s Prover-V2-671B, MiMo is part of a wave of Chinese AI innovation. By sharing the models, training framework, and technical details, Xiaomi is inviting developers worldwide to build on its work, fostering a collaborative push toward smarter, more efficient AI.

Under the Hood: A Recipe for Genius

MiMo-7B’s success lies in its innovative training. Xiaomi’s team curated 130,000 math and coding problems, verified by rule-based systems to ensure quality. They used a three-stage pre-training strategy, immersing the model in 25 trillion tokens of data rich in reasoning patterns. A “test difficulty-driven reward” system tackled sparse rewards in tough problems, while a data resampling strategy boosted training efficiency by 40%.

The model’s architecture, a decoder-only Transformer with Grouped-Query Attention (GQA), SwiGLU activation, and Rotary Position Embeddings (RoPE), is optimized for speed and precision. Multi-token prediction modules cut inference time, making MiMo practical for real-world use.

How to Get Started with MiMo-7B

Ready to unleash MiMo-7B’s potential? Here’s a step-by-step guide to get you started. Note that running a 7-billion-parameter model requires decent hardware, but cloud options make it accessible.

Step 1: Check Your Hardware

• Minimum Requirements: For local deployment, you’ll need a GPU with at least 16GB VRAM (e.g., NVIDIA RTX 3090) for 4-bit quantization. For FP16, expect to need 32GB VRAM.
• Recommended: A multi-GPU setup or cloud platforms like AWS, Google Cloud, or Novita AI for smoother performance.

Step 2: Download the Model

• Visit the Xiaomi MiMo repository to download one of the four models (Base, SFT, RL, or RL-Zero). The MiMo-7B-RL model is ideal for math and coding tasks.
• File sizes are significant—around 14GB for quantized versions—so ensure you have ample storage.

Step 3: Set Up Your Environment

• Install dependencies like PyTorch, vLLM (Xiaomi’s forked version for MiMo), and Transformers.
• Example setup (Python):
  python
  pip install torch vllm transformers
• For vLLM, clone Xiaomi’s fork from their repository.

Step 4: Run the Model

• Load the model using vLLM for optimized inference:
  python
  from vllm import LLM, SamplingParams model_path = "XiaomiMiMo/MiMo-7B-RL" llm = LLM(model=model_path, trust_remote_code=True, num_speculative_tokens=1) sampling_params = SamplingParams(temperature=0.6, max_tokens=500) conversation = [{"role": "user", "content": "Solve: x² + 5x + 6 = 0"}] outputs = llm.chat(conversation, sampling_params=sampling_params) print(outputs[0].outputs[0].text)
• For math problems, input questions in plain text or LaTeX. For coding, provide clear prompts (e.g., “Implement quicksort in Python”).

Step 5: Experiment and Optimize

• Test simple problems first, like quadratic equations, before tackling AIME-level challenges.
• Adjust temperature (0.6 is recommended) for deterministic outputs.
• Explore applications like generating math proofs, debugging code, or automating logic tasks.

For support, contact Xiaomi via email or file issues on their GitHub repository.

The Bigger Picture

MiMo-7B marks Xiaomi’s bold entry into AI, signaling its ambition to compete with global tech giants beyond smartphones and EVs. The open-source release is a strategic play, aligning with China’s push for AI sovereignty and challenging the dominance of closed-source models. X users speculate that MiMo could integrate with Xiaomi’s smart devices, creating AI-powered ecosystems for homes and vehicles.

The timing is no accident. With Alibaba and DeepSeek raising the bar, China’s AI scene is red-hot, and MiMo’s compact efficiency could inspire a new wave of lean, reasoning-focused models. As one X post put it, “Xiaomi’s showing the world that small models can do big things.”

A New Era of Reasoning AI

Xiaomi’s MiMo-7B is a wake-up call: you don’t need billions of parameters to solve complex problems. With stellar performance, open-source accessibility, and a knack for math and coding, it’s a gift to developers, educators, and innovators. Whether you’re cracking AIME problems or coding the next big app, MiMo-7B is your new best friend. So, grab your GPU and see what this tiny titan can do.