[whatmodel.sh](../../README.md) / MiMo-V2.5-Pro (1T-A42B)
# MiMo-V2.5-Pro (1T-A42B)
Xiaomi · released 2026-04 · 1023B params (42B active) · mit
**score 68.6** ±0.9 · #92 of 732 · from 21 benchmark results across 6 sources
Score within the [LMArena (text)](https://whatmodel.sh/?set=lmarena-text&score=set) comparison set only.
## What it's good at
● marks MiMo-V2.5-Pro (1T-A42B) among every model on that task or test, worst to best.
task vs the others rank counts tests
agent tasks ▁▁▃▆█▄▅▅●▄▂▁ #76 of 313 61% 13
coding ▁▁▁▃▅▇█▇▅●▃▁ #61 of 469 31% 5
chat & writing ▁▁▁▃▆▄▆█▆▇●▃ #38 of 402* 8.7% 1
web search ······●····· #1 of 1* 2.2% 1
agent tasks · #76 of 313 · 61% of its score
Beats [GPT-5 Pro](gpt-5-pro.md), [GPT-5](gpt-5.md), [Gemini 3 Flash (Preview)](gemini-3-flash-preview.md) here, though they rank higher overall. Trails [MiMo-V2.5 (310B-A15B)](mimo-v2-5.md) here, though they rank lower overall.
test result vs the others in its run difficulty counts why
Arena Agent (October 2026 aggrega… -0.0893 IPS ▁▁▁●▃▂█▁▂▂▁▁ beats 6/59 avg -0.379 IPS 22% sharp
Terminal-Bench 2.1 (maker) 65.2% ▁·▁·▁▁▄▃▆●▅█ beats 34/63 hard · avg 23% 5.4% sharp · at its level · small run
SWE-bench Pro (split unspecified)… 57.2% ▂·▁▁▁▁▂▃●▇▂▁ beats 38/58 very hard · avg 9.7% 5.4% sharp · at its level · small run
SWE-bench Verified (maker) 78.9% ▁▁▁▁▁▁▂▄█●▁▁ beats 128/138 hard · avg 26% 4.9% sharp · small run
GameLogicBench solve rate (72 God… 16.7% ●▄·█·▄·██▄·▄ beats 1/11 very hard · avg 5.1% 4.8% typical
Terminal-Bench 4.0 (maker) 1.5% ▁·●··▁▆▆▅██▃ beats 1/33 very hard · avg 0.5% 4.6% sharp · small run
Terminal-Bench 2.0 (maker) 68.4% ▂▂▅▅████▆●▃▃ beats 32/36 hard · avg 17% 3.4% at its level · small run
EEGAgentBench overall (tool-based… 49.1% ▂·▁▁··▇▇●▂·▁ beats 16/25 hard · avg 28% 2.6% blunt · at its level
Humanity's Last Exam with tools (… 48.0% ▁·▁▂▂▁▄▄●█▄▅ beats 31/62 very hard · avg 15% 2.2% at its level · small run
Agents' Last Exam (metric/harness… 13.2% ●·▂·█▄▂·▂▂▄▄ beats 0/14 very hard · avg 6.5% 1.8% small run
Toolathlon-Verified (maker) 49.1% ▂·▂▂●▃···▂██ beats 5/18 hard · avg 32% 1.4% blunt · at its level · small run
τ³-bench (maker) 72.9% ▁▁▂▁·▁▁·▁▁▂● beats 23/25 medium · avg 37% 1.4% at its level · small run
DeepSWE v1.1 (resolved) (maker) 19.0% ●····▂▁▁▆███ beats 1/31 hard · avg 19% 1.0% at its level · noisy · small run
coding · #61 of 469 · 31% of its score
Beats [GPT-5.4](gpt-5-4.md), [Gemini 3.1 Pro (Preview)](gemini-3-1-pro.md), [DeepSeek V4 Flash Vision (Exp)](deepseek-v4-flash-vision-exp.md) here, though they rank higher overall.
test result vs the others in its run difficulty counts why
Arena WebDev (Code Arena) 1480.4 ▁▁▃▂▅█●▆▅▅▁▂ beats 68/129 avg 981 15% sharp
SWE-bench Pro (split unspecified)… 57.2% ▂·▁▁▁▁▂▃●▇▂▁ beats 38/58 very hard · avg 9.7% 5.4% sharp · at its level · small run
SWE-bench Verified (maker) 78.9% ▁▁▁▁▁▁▂▄█●▁▁ beats 128/138 hard · avg 26% 4.9% sharp · small run
GameLogicBench solve rate (72 God… 16.7% ●▄·█·▄·██▄·▄ beats 1/11 very hard · avg 5.1% 4.8% typical
DeepSWE v1.1 (resolved) (maker) 19.0% ●····▂▁▁▆███ beats 1/31 hard · avg 19% 1.0% at its level · noisy · small run
chat & writing · #38 of 402 · 8.7% of its score
Beats [GPT-6 Sol](gpt-6-sol.md), [MiMo-V2.6-Flash (309B-A15B)](mimo-v2-6-flash.md), [Claude Haiku 5.5](claude-haiku-5-5.md) here, though they rank higher overall.
test result vs the others in its run difficulty counts why
LMArena (text) 1466.6 ▁▁▁▂▃▃▃▆▆▇●▃ beats 342/385 avg 1260 8.7% blunt
web search · #1 of 1 · 2.2% of its score
test result vs the others in its run difficulty counts why
Humanity's Last Exam with tools (… 48.0% ▁·▁▂▂▁▄▄●█▄▅ beats 31/62 very hard · avg 15% 2.2% at its level · small run
other tests (6)
test result vs the others in its run difficulty counts why
WildClawBench 43.0% ▃·▅▅█·▅●▅▃▃▃ beats 11/17 very hard · avg 11% 4.2% at its level
Humanity's Last Exam (maker) 34.0% ▁▁▁▄▆▆▇█●▆▁▃ beats 84/111 very hard · avg 6.0% 3.3% at its level · small run
AutomationBench v1.0.6 (maker) 16.0% ▃···●·▃▃··█▅ beats 1/8 very hard · avg 7.2% 2.2% at its level · small run
SEC-Bench Pro (pass@1) (maker) 17.7% ●·▄·▄·█▄···▄ beats 0/6 very hard · avg 7.5% 2.1% at its level · small run
Claw-Eval Pass^3 (maker) 63.8% █▄▄·▄·▄▄·●·▄ beats 7/8 hard · avg 29% 1.6% at its level · small run
ExploitGym (pass@1) (maker) 0.2% ●·▄··█·▄▄··▄ beats 0/6 very hard · avg 0.7% 1.2% too hard for it · noisy · small run
source results share of the score
LMArena leaderboard dataset 3 46% ▇▇▇▇▇▇
XiaomiMiMo/MiMo-V2.5-Pro model card (su… 7 22% ▇▇▇
Xiaomi MiMo official model cards and do… 8 20% ▇▇
GameLogicBench: Evaluating Coding Agent… 1 5% ▇
WildClawBench: A Benchmark for Real-Wor… 1 4% ▇
EEGAgentBench: Benchmarking LLM Agents … 1 3% ▇
prior: pull toward the middle 0% ▇
weighted average = 68.6
Licences: LMArena leaderboard dataset ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · XiaomiMiMo/MiMo-V2.5-Pro model card ([MIT](../../licenses/XiaomiMiMo--MiMo-V2.5-Pro.txt)) · GameLogicBench: Evaluating Coding Agents on Runtime Game Logic with Tick-Level State Assertions ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · WildClawBench: A Benchmark for Real-World ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)) · EEGAgentBench: Benchmarking LLM Agents on Short- and Long-Horizon EEG Analysis ([CC BY 4.0](https://creativecommons.org/licenses/by/4.0/))
## How to run it
### Through an API
api (Novita AI) · $0.65/M blended · $0.522 in / $1.044 out per 1M tokens · [novita.ai](https://novita.ai/models/llm/xiaomimimo-mimo-v2.5-pro)
[price source](https://novita.ai/pricing): USD per 1M tokens, Novita AI serverless chat completions, model xiaomimimo/mimo-v2.5-pro (status active; 1M context): input $0.522, output $1.044; cache read excluded. Xiaomi's own API lists mimo-v2.5-pro at $0.435/$0.87 (overseas, cache miss) but marks it 'to be deprecated' on 2026-10-21. Currency: USD. Novita AI official pricing page, observed 2026-09-25. · [context source](https://github.com/anomalyco/models.dev/blob/a668908cfe7073faa0363699486fc41a3ff52412/models/xiaomi/mimo-v2.5-pro.toml)
### llama.cpp (recommended)
1. Install llama.cpp. Homebrew and winget installs name the commands llama-cli and llama-server instead. [Other ways to install](https://github.com/ggml-org/llama.cpp/blob/master/docs/install.md)
```sh
# macOS / Linux
curl -LsSf https://llama.app/install.sh | sh
# Windows
irm https://llama.app/install.ps1 | iex
```
2. Run MiMo-V2.5-Pro (1T-A42B) (ud-q4_k_xl, needs ~614.3 GB, from [huggingface.co/unsloth](https://huggingface.co/unsloth/MiMo-V2.5-Pro-GGUF/tree/main))
```sh
llama cli -hf unsloth/MiMo-V2.5-Pro-GGUF:UD-Q4_K_XL
```
3. Or start a local server and open the address it prints (http://localhost:9931)
```sh
llama serve -hf unsloth/MiMo-V2.5-Pro-GGUF:UD-Q4_K_XL
```
Other versions: ud-q2_k_xl (~307.4 GB) `llama cli -hf unsloth/MiMo-V2.5-Pro-GGUF:UD-Q2_K_XL` · ud-iq1_m (~153.95 GB) `llama cli -hf unsloth/MiMo-V2.5-Pro-GGUF:UD-IQ1_M`
### LM Studio
1. Install LM Studio. Open the app once before using the lms command. [Download it (macOS / Windows / Linux)](https://lmstudio.ai/download)
2. Run MiMo-V2.5-Pro (1T-A42B) (ud-q4_k_xl, needs ~614.3 GB, from [huggingface.co/unsloth](https://huggingface.co/unsloth/MiMo-V2.5-Pro-GGUF/tree/main))
```sh
lms get https://huggingface.co/unsloth/MiMo-V2.5-Pro-GGUF@UD-Q4_K_XL -y
```
3. Then run lms ls to see its name, and lms chat followed by that name
Other versions: ud-q2_k_xl (~307.4 GB) `lms get https://huggingface.co/unsloth/MiMo-V2.5-Pro-GGUF@UD-Q2_K_XL -y` · ud-iq1_m (~153.95 GB) `lms get https://huggingface.co/unsloth/MiMo-V2.5-Pro-GGUF@UD-IQ1_M -y`
Model repository and documentation: [huggingface.co/XiaomiMiMo](https://huggingface.co/XiaomiMiMo/MiMo-V2.5-Pro)
[interactive version](https://whatmodel.sh/model/mimo-v2-5-pro) · [JSON](https://whatmodel.sh/api?format=json&all=1)