# MiMo-V2.5 Usage, Cost & Rank | OpenCode Data

> MiMo-V2.5 ranked #9 by tokens across OpenCode last week, with 6.6% of tokens over the past two months. MiMo-V2.5 costs $0.16 per 1M input tokens and $0.32 per 1M output tokens.

Open MiMo model for multimodal coding agents and long-context automation

- Page: https://opencode.ai/data/xiaomi/mimo-v2-5
- JSON: https://opencode.ai/data/xiaomi/mimo-v2-5.json
- Model ID: xiaomi/mimo-v2.5
- Lab: Xiaomi
- Updated: 2026-10-02T07:36:56.000Z

## Model facts

| Fact | Value |
| --- | --- |
| Context window | 1M |
| Max output | 131K |
| Knowledge cutoff | 2024-12 |
| Release date | 2026-04-22 |
| Input modalities | text, image, audio, video |
| Output modalities | text |
| Reasoning | Yes |
| Tool calling | Yes |
| Open weights | Yes |
| Weights | [Hugging Face](https://huggingface.co/XiaomiMiMo/MiMo-V2.5) |

## Pricing: USD per 1M tokens

| Input | Output | Cached input | Cache write |
| --- | --- | --- | --- |
| $0.16 | $0.32 | $0.0040 | $0.16 |

## OpenCode usage: past 2 months

| Metric | Value |
| --- | --- |
| Rank by tokens last week | #9 |
| Tokens | 59T |
| Share of all tokens | 6.6% |
| Change vs previous 2 months | +460% |
| Unique users | 1.9M |
| Completed sessions | 58,344,339 |
| Average tokens per session | 1M |
| Average cost per session | $0.0032 |
| Total spend | $187,180 |
| Input tokens served from cache | 93.3% |
| Weekly retention | 68.8% |

## Daily usage: past 2 months

| Date | Tokens | Unique users | Sessions |
| --- | --- | --- | --- |
| 2026-08-08 | 167B | 8.5K | 33,698 |
| 2026-08-09 | 149B | 7.9K | 42,131 |
| 2026-08-10 | 153B | 10K | 36,827 |
| 2026-08-11 | 445B | 24K | 19,196 |
| 2026-08-12 | 513B | 33K | 532 |
| 2026-08-13 | 517B | 31K | 289 |
| 2026-08-14 | 509B | 28K | 309 |
| 2026-08-15 | 482B | 24K | 286 |
| 2026-08-16 | 607B | 27K | 462 |
| 2026-08-17 | 2.4T | 55K | 1,463 |
| 2026-08-18 | 2.1T | 56K | 901,409 |
| 2026-08-19 | 2.1T | 56K | 1,127,344 |
| 2026-08-20 | 2.1T | 51K | 1,131,084 |
| 2026-08-21 | 1.8T | 49K | 528,643 |
| 2026-08-22 | 1.2T | 37K | 523,878 |
| 2026-08-23 | 1.2T | 34K | 767,459 |
| 2026-08-24 | 1.5T | 44K | 851,060 |
| 2026-08-25 | 2.3T | 57K | 1,391,057 |
| 2026-08-26 | 2.8T | 65K | 1,861,797 |
| 2026-08-27 | 2.1T | 58K | 1,449,297 |
| 2026-08-28 | 1.3T | 51K | 1,444,343 |
| 2026-08-29 | 1.1T | 32K | 1,047,164 |
| 2026-08-30 | 1.3T | 37K | 1,575,216 |
| 2026-08-31 | 1.8T | 61K | 2,385,099 |
| 2026-09-01 | 1.7T | 59K | 2,090,919 |
| 2026-09-02 | 1.7T | 59K | 1,835,928 |
| 2026-09-03 | 1.5T | 59K | 1,546,919 |
| 2026-09-04 | 1.7T | 54K | 1,726,371 |
| 2026-09-05 | 1.2T | 40K | 1,292,000 |
| 2026-09-06 | 1.2T | 40K | 1,354,320 |
| 2026-09-07 | 1.4T | 49K | 2,059,268 |
| 2026-09-08 | 1.5T | 49K | 2,251,004 |
| 2026-09-09 | 1.5T | 49K | 2,287,156 |
| 2026-09-10 | 1.7T | 48K | 2,326,197 |
| 2026-09-11 | 1.3T | 45K | 1,999,453 |
| 2026-09-12 | 944B | 33K | 1,333,742 |
| 2026-09-13 | 984B | 35K | 1,526,645 |
| 2026-09-14 | 1.3T | 48K | 2,189,714 |
| 2026-09-15 | 1.3T | 49K | 2,126,600 |
| 2026-09-16 | 1.3T | 47K | 1,760,896 |
| 2026-09-17 | 973B | 36K | 1,629,297 |
| 2026-09-18 | 920B | 36K | 1,283,275 |
| 2026-09-19 | 772B | 23K | 1,199,882 |
| 2026-09-20 | 810B | 19K | 1,285,467 |
| 2026-09-21 | 927B | 27K | 881,071 |
| 2026-09-22 | 649B | 19K | 718,639 |
| 2026-09-23 | 306B | 9.9K | 619,446 |
| 2026-09-24 | 199B | 8.2K | 572,368 |
| 2026-09-25 | 151B | 6.2K | 503,724 |
| 2026-09-26 | 127B | 5K | 508,496 |
| 2026-09-27 | 121B | 4.9K | 470,813 |
| 2026-09-28 | 133B | 6.1K | 429,321 |
| 2026-09-29 | 127B | 6.1K | 446,939 |
| 2026-09-30 | 122B | 5.5K | 404,769 |
| 2026-10-01 | 124B | 5.2K | 422,622 |
| 2026-10-02 | 34B | 2.6K | 141,035 |

## Top countries: past 2 months

| Rank | Country | Tokens | Share |
| --- | --- | --- | --- |
| 1 | China | 10T | 17.3% |
| 2 | India | 5T | 8.4% |
| 3 | United States | 4.8T | 8.1% |
| 4 | Brazil | 3T | 5% |
| 5 | Indonesia | 2.6T | 4.4% |
| 6 | Germany | 1.9T | 3.1% |
| 7 | Hong Kong | 1.5T | 2.5% |
| 8 | Spain | 1.3T | 2.2% |
| 9 | France | 1.1T | 1.9% |
| 10 | Singapore | 1.1T | 1.8% |
| 11 | Pakistan | 1.1T | 1.8% |
| 12 | Vietnam | 1T | 1.8% |
| 13 | Netherlands | 1T | 1.7% |
| 14 | Russia | 950B | 1.6% |
| 15 | Japan | 916B | 1.6% |

## Nearby models by tokens last week

| Rank | Model | Lab | Tokens |
| --- | --- | --- | --- |
| 5 | [deepseek-v4-flash](https://opencode.ai/data/deepseek/deepseek-v4-flash.md) | DeepSeek | 5.6T |
| 6 | [nemotron-3-ultra](https://opencode.ai/data/nvidia/nemotron-3-ultra.md) | NVIDIA | 3.6T |
| 7 | [longcat-2.5-preview](https://opencode.ai/data/meituan/longcat-2-5-preview.md) | Meituan | 2.6T |
| 8 | [glm-5.3-flash](https://opencode.ai/data/zhipuai/glm-5-3-flash.md) | Zhipu | 2.1T |
| 9 | [mimo-v2.5](https://opencode.ai/data/xiaomi/mimo-v2-5.md) | Xiaomi | 788B |
| 10 | [muse-spark-1.2-contributor](https://opencode.ai/data/meta/muse-spark-1-2-contributor.md) | Meta | 733B |
| 11 | [deepseek-v4-flash-vision-exp](https://opencode.ai/data/deepseek/deepseek-v4-flash-vision-exp.md) | DeepSeek | 684B |
| 12 | [gpt-6-luna](https://opencode.ai/data/openai/gpt-6-luna.md) | OpenAI | 638B |
| 13 | [deepseek-v4-pro](https://opencode.ai/data/deepseek/deepseek-v4-pro.md) | DeepSeek | 462B |
| 14 | [mimo-v2.6-pro](https://opencode.ai/data/xiaomi/mimo-v2-6-pro.md) | Xiaomi | 461B |

## Benchmarks

| Benchmark | Score | Metric | Source |
| --- | --- | --- | --- |
| MiMo Coding Bench | 71.8 | score | https://huggingface.co/XiaomiMiMo/MiMo-V2.5 |
| Claw-Eval | 62.3 | score | https://huggingface.co/XiaomiMiMo/MiMo-V2.5 |
| Terminal-Bench | 65.8 | score | https://huggingface.co/XiaomiMiMo/MiMo-V2.5 |
| SWE-Bench Pro | 56.1 | score | https://huggingface.co/XiaomiMiMo/MiMo-V2.5 |
| CharXiv Reasoning | 81 | score | https://huggingface.co/XiaomiMiMo/MiMo-V2.5 |
| MMMU-Pro | 77.9 | score | https://huggingface.co/XiaomiMiMo/MiMo-V2.5 |
| HR-Bench | 88.5 | score | https://huggingface.co/XiaomiMiMo/MiMo-V2.5 |
| OmniDocBench | 87.2 | score | https://huggingface.co/XiaomiMiMo/MiMo-V2.5 |
| Claw-Eval | 23.8 | score | https://huggingface.co/XiaomiMiMo/MiMo-V2.5 |
| Video-MME | 87.7 | score | https://huggingface.co/XiaomiMiMo/MiMo-V2.5 |
| DailyOmni | 83.5 | score | https://huggingface.co/XiaomiMiMo/MiMo-V2.5 |
| VideoHolmes | 64 | score | https://huggingface.co/XiaomiMiMo/MiMo-V2.5 |

## Methodology

- Updates: Aggregated every hour. Days and weeks use UTC.
- Tokens: Input, output, reasoning, and cached tokens for each request.
- Users and sessions: Approximate counts of distinct users and OpenCode sessions.
- Cost: Session cost is the average cost per OpenCode session. Token prices are list prices from the OpenCode model catalog.
- Retention: The share of a model's users in one week who use it again the next week.
- Citation: Cite OpenCode Data (opencode.ai/data) with the update time shown at the top of the page.