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

> MiMo-V2.5-Pro ranked #27 by tokens across OpenCode last week, with 0.2% of tokens over the past two months. MiMo-V2.5-Pro costs $0.47 per 1M input tokens and $0.94 per 1M output tokens.

Stronger MiMo Pro tier for multimodal reasoning and coding-agent execution

- Page: https://opencode.ai/data/xiaomi/mimo-v2-5-pro
- JSON: https://opencode.ai/data/xiaomi/mimo-v2-5-pro.json
- Model ID: xiaomi/mimo-v2.5-pro
- Lab: Xiaomi
- Updated: 2026-10-02T08:39:37.000Z

## Model facts

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

## Pricing: USD per 1M tokens

| Input | Output | Cached input | Cache write |
| --- | --- | --- | --- |
| $0.47 | $0.94 | $0.0050 | $0.47 |

## OpenCode usage: past 2 months

| Metric | Value |
| --- | --- |
| Rank by tokens last week | #27 |
| Tokens | 1.5T |
| Share of all tokens | 0.2% |
| Change vs previous 2 months | -30% |
| Unique users | 148K |
| Completed sessions | 423,687 |
| Average tokens per session | 3.6M |
| Average cost per session | $0.0786 |
| Total spend | $33,282 |
| Input tokens served from cache | 96.5% |
| Weekly retention | 60.1% |

## Daily usage: past 2 months

| Date | Tokens | Unique users | Sessions |
| --- | --- | --- | --- |
| 2026-08-08 | 25B | 3K | 10,062 |
| 2026-08-09 | 20B | 2.5K | 11,198 |
| 2026-08-10 | 28B | 3.4K | 15,485 |
| 2026-08-11 | 34B | 4.4K | 5,909 |
| 2026-08-12 | 48B | 3.7K | 251 |
| 2026-08-13 | 47B | 4K | 299 |
| 2026-08-14 | 45B | 4K | 241 |
| 2026-08-15 | 34B | 3.5K | 152 |
| 2026-08-16 | 36B | 3.8K | 185 |
| 2026-08-17 | 67B | 5.9K | 308 |
| 2026-08-18 | 66B | 5.5K | 15,658 |
| 2026-08-19 | 58B | 5.4K | 14,569 |
| 2026-08-20 | 47B | 4.8K | 24,854 |
| 2026-08-21 | 40B | 4.4K | 10,914 |
| 2026-08-22 | 26B | 3.1K | 8,980 |
| 2026-08-23 | 28B | 3.5K | 4,614 |
| 2026-08-24 | 41B | 4.7K | 6,859 |
| 2026-08-25 | 40B | 3.8K | 8,171 |
| 2026-08-26 | 37B | 3.4K | 7,227 |
| 2026-08-27 | 32B | 3.1K | 6,544 |
| 2026-08-28 | 28B | 2.9K | 5,364 |
| 2026-08-29 | 22B | 1.9K | 2,551 |
| 2026-08-30 | 22B | 1.8K | 2,711 |
| 2026-08-31 | 34B | 3.1K | 8,862 |
| 2026-09-01 | 34B | 2.8K | 6,824 |
| 2026-09-02 | 32B | 3.1K | 10,387 |
| 2026-09-03 | 30B | 2.9K | 7,348 |
| 2026-09-04 | 26B | 2.2K | 10,108 |
| 2026-09-05 | 17B | 1.8K | 7,755 |
| 2026-09-06 | 20B | 1.9K | 8,023 |
| 2026-09-07 | 28B | 2.6K | 11,537 |
| 2026-09-08 | 29B | 2.4K | 9,377 |
| 2026-09-09 | 30B | 2.2K | 9,027 |
| 2026-09-10 | 28B | 2.4K | 9,783 |
| 2026-09-11 | 24B | 1.9K | 9,172 |
| 2026-09-12 | 17B | 1.3K | 7,761 |
| 2026-09-13 | 18B | 1.5K | 7,426 |
| 2026-09-14 | 29B | 2.2K | 9,046 |
| 2026-09-15 | 26B | 2.3K | 8,381 |
| 2026-09-16 | 20B | 2.1K | 8,163 |
| 2026-09-17 | 16B | 2.2K | 9,340 |
| 2026-09-18 | 18B | 1.9K | 15,036 |
| 2026-09-19 | 14B | 1.7K | 12,207 |
| 2026-09-20 | 16B | 1.8K | 9,352 |
| 2026-09-21 | 25B | 2.6K | 10,322 |
| 2026-09-22 | 23B | 1.9K | 6,757 |
| 2026-09-23 | 20B | 1.4K | 5,370 |
| 2026-09-24 | 16B | 1.3K | 4,465 |
| 2026-09-25 | 12B | 1.1K | 4,330 |
| 2026-09-26 | 5.3B | 922 | 3,651 |
| 2026-09-27 | 3.7B | 882 | 3,854 |
| 2026-09-28 | 7.2B | 1.3K | 5,153 |
| 2026-09-29 | 7.3B | 1.3K | 6,354 |
| 2026-09-30 | 8B | 1.1K | 4,700 |
| 2026-10-01 | 8.3B | 980 | 9,316 |
| 2026-10-02 | 2.1B | 416 | 1,394 |

## Top countries: past 2 months

| Rank | Country | Tokens | Share |
| --- | --- | --- | --- |
| 1 | United States | 208B | 13.7% |
| 2 | China | 152B | 10.1% |
| 3 | Brazil | 95B | 6.3% |
| 4 | Germany | 72B | 4.7% |
| 5 | Indonesia | 64B | 4.2% |
| 6 | Spain | 59B | 3.9% |
| 7 | India | 57B | 3.8% |
| 8 | Colombia | 45B | 3% |
| 9 | United Kingdom | 37B | 2.4% |
| 10 | Argentina | 35B | 2.3% |
| 11 | Mexico | 33B | 2.2% |
| 12 | Hong Kong | 30B | 1.9% |
| 13 | France | 28B | 1.9% |
| 14 | Singapore | 26B | 1.7% |
| 15 | Japan | 26B | 1.7% |

## Nearby models by tokens last week

| Rank | Model | Lab | Tokens |
| --- | --- | --- | --- |
| 23 | [fledge-alpha](https://opencode.ai/data/unknown/fledge-alpha.md) | - | 90B |
| 24 | [qwen3.7-plus](https://opencode.ai/data/alibaba/qwen3-7-plus.md) | Qwen | 76B |
| 25 | [glm-5.2](https://opencode.ai/data/zhipuai/glm-5-2.md) | Zhipu | 61B |
| 26 | [kimi-k3](https://opencode.ai/data/moonshotai/kimi-k3.md) | Moonshot | 45B |
| 27 | [mimo-v2.5-pro](https://opencode.ai/data/xiaomi/mimo-v2-5-pro.md) | Xiaomi | 42B |
| 28 | [longcat-2.0](https://opencode.ai/data/meituan/longcat-2-0.md) | Meituan | 26B |
| 29 | [qwen3.8-max](https://opencode.ai/data/alibaba/qwen3-8-max.md) | Qwen | 26B |
| 30 | [hy3](https://opencode.ai/data/tencent/hy3.md) | Tencent | 17B |
| 31 | [minimax-m2.7](https://opencode.ai/data/minimax/minimax-m2-7.md) | MiniMax | 15B |
| 32 | [hy4-preview](https://opencode.ai/data/tencent/hy4-preview.md) | Tencent | 13B |

## Benchmarks

| Benchmark | Score | Metric | Source |
| --- | --- | --- | --- |
| SWE-Bench Verified | 78.9 | resolved | https://huggingface.co/XiaomiMiMo/MiMo-V2.5-Pro |
| SWE-Bench Pro | 57.2 | resolve rate | https://mimo.xiaomi.com/mimo-v2-5-pro/ |
| GPQA Diamond | 86.6 | accuracy | https://mimo.xiaomi.com/mimo-v2-5-pro/ |
| DeepSWE | 19 | score | https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Flash-RL |
| ProgramBench | 12.5 | score | https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Flash-RL |
| MiMo Code Bench | 40.4 | score | https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Flash-RL |
| AutomationBench | 16 | score | https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Flash-RL |
| Toolathlon-Verified | 49.1 | score | https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Flash-RL |
| GDPval-AA | 1107 | Elo | https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Flash-RL |
| Agents' Last Exam | 13.2 | score | https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Flash-RL |
| Terminal-Bench | 1.5 | score | https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Flash-RL |
| Terminal-Bench | 65.2 | score | https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Flash-RL |
| JobBench | 25 | score | https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Flash-RL |
| CyberGym | 40 | score | https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Flash-RL |
| MiMo Cyber Bench | 0 | score | https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Flash-RL |
| ExploitGym | 0.2 | score | https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Flash-RL |
| ExploitBench | 16.6 | score | https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Flash-RL |
| SEC-Bench Pro | 17.7 | score | https://huggingface.co/XiaomiMiMo/MiMo-V2.6-Flash-RL |

## 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.