MiMo V2.5 Pro
By Xiaomi · China
Updated 2026-07-13
Overview
Xiaomi's MIT-licensed frontier agentic model: 1.02T MoE with 42B active params, 57.2% on SWE-Bench Pro, 1M context, and 6:1 hybrid attention. Released April 2026.
When to pick this model
- Frontier autonomous coding agents at MIT licensing
- Workflows chaining 1,000+ tool calls in a single session
- Million-token codebase reasoning
- Research on multi-teacher distillation outcomes
- Datacenter deployments seeking the open agentic ceiling
VRAM requirements by quantization
| Quantization | VRAM required |
|---|---|
| Q4_K_M (recommended) | 595 GB |
| Q5_K_M | 720 GB |
| Q8_0 | 1090 GB |
| FP16 (no quantization) | 2040 GB |
VRAM figures include model weights plus a typical 8k KV cache and ~600 MB runtime overhead (Ollama / llama.cpp baseline). Add headroom for higher context lengths.
In practice, MiMo V2.5 Pro is server-class even at Q4_K_M (595 GB). Stepping up to Q8_0 nearly doubles the footprint to 1090 GB, and unquantized FP16 weights take 2040 GB — plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity.
Without a GPU, MiMo V2.5 Pro needs roughly 700 GB of system RAM to run on CPU via llama.cpp or Ollama — workable for background jobs, but far slower than GPU inference. Throughput estimates from our compatibility engine: around 1 tokens/sec on entry-level GPUs, on the order of 4 tokens/sec on a mid-range card, and up to 12 tokens/sec on high-end hardware — assuming the chosen quantization fully fits in VRAM.
What hardware do you need
The table below matches MiMo V2.5 Pro to common GPU memory tiers, using the highest-fidelity quantization that fully fits each card class. Spilling layers to system RAM works but costs most of the speed, so size your card to the quantization you actually want to run.
| GPU memory | Example cards | Best fit for MiMo V2.5 Pro |
|---|---|---|
| 8 GB | RTX 5070 Laptop, RTX 5060, RTX 5060 Ti 8GB | Does not fit — needs 595 GB at Q4_K_M |
| 12 GB | RTX 5070, RTX 5070 Ti Laptop, RTX 4080 Laptop | Does not fit — needs 595 GB at Q4_K_M |
| 16 GB | RTX 5080, RTX 4080 Super, Radeon RX 9070 XT | Does not fit — needs 595 GB at Q4_K_M |
| 24 GB | RTX 4090, Radeon RX 7900 XTX, RTX 5090 Laptop | Does not fit — needs 595 GB at Q4_K_M |
| 32 GB | RTX 5090 | Does not fit — needs 595 GB at Q4_K_M |
Which GPU should you buy to run MiMo V2.5 Pro?
To run MiMo V2.5 Pro locally at Q4, you need ~595 GB of VRAM. The best value for this is a Apple Mac Studio (64+ GB unified memory).
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Published benchmark scores
| Benchmark | Score |
|---|---|
| SWE-Bench Pro | 57.2 |
| Claw-Eval | 63.8 |
| τ3-Bench | 72.9 |
Scores published by the model author or aggregated from public leaderboards. Re-measured monthly by our editorial team.
Strengths
- MIT license at frontier agentic scale
- 1M context window
- Supports 1,000+ tool calls per chain
- 57.2% on SWE-Bench Pro
- Hybrid 6:1 attention cuts KV-cache by 7x vs. full attention
Limitations
- Roughly 600 GB VRAM in Q4 — datacenter only
- No official Ollama quantization
- MTP support is uneven across inference engines
Typical workloads
In our catalog grid, MiMo V2.5 Pro is filed under Open Frontier, Tool-Calling Agents, Long Reasoning — the use cases where its size/quality trade-off makes the most sense. Its tags translate to concrete workloads: code generation and review (pair it with an editor integration like Continue.dev or Cline); multi-step reasoning and math-flavoured tasks; multilingual workloads.
The 976k-token context window is large enough to hold entire codebases' worth of files or long reports in a single prompt, which is what makes local RAG and document analysis practical. The MIT license is permissive, so shipping it inside a commercial product raises no special legal questions.
Architecture & training
Architecture: MoE 1.02T/42B active · 70 layers (1 dense + 69 MoE) · 384 experts top-8 · hybrid SWA/GA 6:1 · MTP 3 layers · FP8 E4M3
Training: Three-stage post-training: SFT → domain-specialized RL (math, safety, agentic) → Multi-Teacher On-Policy Distillation.
The open agentic frontier — MIT, million-token, thousand-call — if you have the silicon to run it.
Quick start
# HuggingFace : XiaomiMiMo/MiMo-V2.5-ProOr use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.
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Frequently asked questions
How much VRAM does MiMo V2.5 Pro need?
At the recommended Q4_K_M quantization, MiMo V2.5 Pro needs about 595 GB of VRAM. Q8_0 takes 1090 GB, and unquantized FP16 weights take 2040 GB.
Can MiMo V2.5 Pro run without a GPU?
Yes — with roughly 700 GB of system RAM it runs CPU-only through llama.cpp or Ollama. Expect a fraction of GPU speed, which is fine for background or batch jobs but slow for interactive chat.
What context window does MiMo V2.5 Pro support?
MiMo V2.5 Pro supports a 976k-token context window (1,000,000 tokens).
Can I use MiMo V2.5 Pro commercially?
Yes. MiMo V2.5 Pro is released under MIT, a permissive open-source license that allows commercial use, modification and redistribution.
How fast is MiMo V2.5 Pro on consumer hardware?
Our compatibility engine estimates on the order of 4 tokens/sec on a mid-range GPU and up to 12 tokens/sec on high-end cards, assuming the quantization fully fits in VRAM.
Which quantization of MiMo V2.5 Pro should I download first?
Start with Q4_K_M (595 GB) — the standard size/quality sweet spot. Step up to Q5_K_M or Q8_0 only if you have VRAM headroom. It does not fit a single 24 GB consumer card — plan for multi-GPU or server hardware.