MiMo-V2.6-Pro
XiaomiMiMomittext

MiMo-V2.6-Pro

Updated Nov 7, 2026
Parameters
159.4B
Context
1,048,576
License
mit
Updated
Nov 7, 2026

Intelligence benchmarks

Artificial Analysis indexes - compared with the best open and proprietary models

Intelligence

29.9

AA Index

Coding

28.3

AA Index

Agentic

38.9

AA Index

Intelligence Index - MiMo-V2.6-Pro vs. the field

Best open-weight models (you can run locally) and leading proprietary models for context.

Gemini 4 Carbon
60
closed
Claude Opus 5.5
59.1
closed
GPT-6 Astra
56.9
closed
GPT-6.1 Sol
54.6
closed
Claude Sonnet 5.5
54.5
closed
Kimi K3.1 Pro
46.5
open
DeepSeek V4.1 Flash 1017
32.6
open
MiMo-V2.6-Pro
29.9
open

Coding Index comparison

Claude Opus 5.5
51.7
closed
Claude Sonnet 5.5
50.5
closed
Gemini 4 Carbon
50.4
closed
GPT-6 Astra
49.1
closed
GPT-6.1 Terra
47.6
closed
Kimi K3.1 Pro
42.1
open
DeepSeek V4.1 Flash 1017
30.6
open
MiMo-V2.6-Pro
28.3
open

Agentic Index comparison

Gemini 4 Carbon
53.1
closed
Kimi K3.1 Pro
52.3
open
Muse Wonder Full
52
closed
Gemini 4 Argon
49.7
closed
GPT-6 Astra
49.7
closed
Muse Wonder
49.3
closed
MiniMax-M3.1-Flash
46.7
open
MiMo-V2.6-Pro
38.9
open

Benchmark data from Artificial Analysis · updated 2026-11-09.

Hosted API cost & speed

What it costs to rent this model instead of running it locally

MetricValue
Input (per 1M tokens)$0.44
Output (per 1M tokens)$0.87
Median speed (hosted API)48 tok/s

Median across hosted providers, measured by Artificial Analysis - updated 2026-11-09. Local llama.cpp speed depends on your hardware — see the hardware pages for estimates.

Arena rating (LMArena)

Human preference wins from head-to-head chat battles

MetricValue
Overall arena rating1491.4
Rank (overall)#12
Battle votes4,037

Overall rating published by LMArena on 2026-10-28 - a pairwise human-preference Elo across live chat battles; higher is better. Fetched 2026-11-07. Open-weights models compete head-to-head with proprietary ones here — no benchmark prompts, just what people pick.

Standard benchmarks

Performance across standard evaluations

BenchmarkScore
HLE49.4
SciCode60.9

Source: Artificial Analysis component evals - updated 2026-11-09.

Will it run on your hardware?

Pick your GPU memory - see which quantizations fit, and the cheapest card for the rest

Too big for 24 GB at any quant
0 of 4 quantizations fit MiMo-V2.6-Pro with real runtime overhead.
fits tight too big

Need an exact figure for your context length? Use the VRAM calculator.

Run it locally

Copy-paste - running in under a minute

vLLMOpenAI-compatible API
vllm serve ProCreations/MiMo-V2.6-Flash-RL-NVFP4

New to this? Start with Ollama · serve to many users with vLLM.

Deep dive

Notes, sources, and the full write-up

MiMo-V2.6-Pro is a 159.4B-parameter mit model from Xiaomi. It scores 29.9 on the Artificial Analysis Intelligence Index (coding 28.3). At Q4_K_M it needs roughly 92 GB of VRAM, placing it in the 48 GB+ / multi-GPU hardware tier.

BenchmarksLink to this section

Artificial Analysis Intelligence Index - MiMo-V2.6-Pro vs. leading closed models:

ModelIntelligenceCodingGPQA
MiMo-V2.6-Pro29.928.3-
Gemini 4 Carbon6050.4-
Claude Opus 5.559.151.7-
GPT-6 Astra56.949.196.1
GPT-6.1 Sol54.646.8-
Claude Sonnet 5.554.550.5-

Source: Artificial Analysis (2026-11-09).

PopularityLink to this section

MiMo-V2.6-Pro has 4,103 downloads in the last month on HuggingFace and 5 likes.

Frequently asked

Quick answers to common questions

How much VRAM does MiMo-V2.6-Pro need?

MiMo-V2.6-Pro with 159.4B parameters needs approximately 92 GB at Q4_K_M quantization. Use our VRAM calculator for an exact estimate.

Is MiMo-V2.6-Pro better than other XiaomiMiMo models?

MiMo-V2.6-Pro has 159.4B parameters with 1,048,576 context - a strong choice for general use.

What license is MiMo-V2.6-Pro under?

MiMo-V2.6-Pro is released under the mit license, making it suitable for most commercial and personal projects.

What hardware runs MiMo-V2.6-Pro well?

With 159.4B parameters, MiMo-V2.6-Pro requires adequate VRAM. High-end GPUs like the RTX 4090 (24GB), RTX 5090 (32GB), or Mac Studio with unified memory are good options. Check our hardware directory for specific recommendations.

What is the best quantization for MiMo-V2.6-Pro?

Q4_K_M is the recommended sweet spot - ~98% of FP16 quality at ~27% of the size. Q5_K_M (~113 GB) is an option if you have spare VRAM. Use our VRAM calculator to compare.

How long can MiMo-V2.6-Pro's context window handle?

MiMo-V2.6-Pro supports a 1,048,576-token context window - enough for very long documents, codebases, or multi-turn conversations. Real-world usable context may vary by implementation.

What models compete with MiMo-V2.6-Pro?

MiMo-V2.6-Pro competes with other 80B–239B. Browse our model directory for comparisons, benchmarks, and community reviews to find the best fit.

Nearby options

Similar models and compatible hardware by spec