# Kimi K2.5 Usage, Cost & Rank | OpenCode Data

> Kimi K2.5 had 0% of tokens across OpenCode over the past two months. Kimi K2.5 costs $0.45 per 1M input tokens and $2.80 per 1M output tokens.

Earlier Kimi frontier model for long-context agents, coding, and multimodal work

- Page: https://opencode.ai/data/moonshotai/kimi-k2-5
- JSON: https://opencode.ai/data/moonshotai/kimi-k2-5.json
- Model ID: moonshotai/kimi-k2.5
- Lab: Moonshot
- Updated: 2026-09-28T12:09:39.000Z

## Model facts

| Fact | Value |
| --- | --- |
| Context window | 262K |
| Max output | 262K |
| Knowledge cutoff | 2025-01 |
| Release date | 2026-01 |
| Input modalities | text, image, video |
| Output modalities | text |
| Reasoning | Yes |
| Tool calling | Yes |
| Open weights | Yes |
| Weights | [Hugging Face](https://huggingface.co/moonshotai/Kimi-K2.5) |

## Pricing: USD per 1M tokens

| Input | Output | Cached input | Cache write |
| --- | --- | --- | --- |
| $0.45 | $2.80 | - | - |

## OpenCode usage: past 2 months

| Metric | Value |
| --- | --- |
| Rank by tokens last week | Unranked |
| Tokens | 17B |
| Share of all tokens | 0% |
| Change vs previous 2 months | -69% |
| Unique users | 35K |
| Completed sessions | 14,717 |
| Average tokens per session | 1.2M |
| Average cost per session | $0.2758 |
| Total spend | $4,058 |
| Input tokens served from cache | 87.3% |
| Weekly retention | 62.8% |

## Daily usage: past 2 months

| Date | Tokens | Unique users | Sessions |
| --- | --- | --- | --- |
| 2026-08-08 | 607M | 1.4K | 15 |
| 2026-08-09 | 685M | 1.2K | 11 |
| 2026-08-10 | 999M | 1.6K | 25 |
| 2026-08-11 | 646M | 1.9K | 10 |
| 2026-08-12 | 858M | 1.3K | 1 |
| 2026-08-13 | 1.1B | 1.4K | 0 |
| 2026-08-14 | 1.3B | 1.5K | 1 |
| 2026-08-15 | 829M | 1.4K | 0 |
| 2026-08-16 | 975M | 1.5K | 0 |
| 2026-08-17 | 362M | 2.5K | 0 |
| 2026-08-18 | 325M | 2.2K | 38 |
| 2026-08-19 | 842M | 1.3K | 162 |
| 2026-08-20 | 639M | 1.1K | 72 |
| 2026-08-21 | 595M | 1.5K | 109 |
| 2026-08-22 | 528M | 1.2K | 118 |
| 2026-08-23 | 864M | 1.1K | 23 |
| 2026-08-24 | 1.2B | 1.6K | 260 |
| 2026-08-25 | 624M | 1.3K | 57 |
| 2026-08-26 | 565M | 1K | 37 |
| 2026-08-27 | 754M | 764 | 39 |
| 2026-08-28 | 876M | 682 | 52 |
| 2026-08-29 | 486M | 574 | 62 |
| 2026-08-30 | 434M | 522 | 65 |
| 2026-08-31 | 248M | 863 | 177 |
| 2026-09-01 | 0 | 670 | 90 |
| 2026-09-02 | 0 | 568 | 93 |
| 2026-09-03 | 0 | 570 | 104 |
| 2026-09-04 | 0 | 523 | 224 |
| 2026-09-05 | 0 | 422 | 190 |
| 2026-09-06 | 0 | 398 | 338 |
| 2026-09-07 | 0 | 440 | 671 |
| 2026-09-08 | 0 | 343 | 610 |
| 2026-09-09 | 0 | 118 | 579 |
| 2026-09-10 | 0 | 0 | 1,105 |
| 2026-09-11 | 0 | 0 | 1,061 |
| 2026-09-12 | 0 | 0 | 725 |
| 2026-09-13 | 0 | 0 | 641 |
| 2026-09-14 | 0 | 0 | 1,370 |
| 2026-09-15 | 0 | 0 | 952 |
| 2026-09-16 | 0 | 0 | 1,073 |
| 2026-09-17 | 0 | 0 | 1,185 |
| 2026-09-18 | 0 | 0 | 886 |
| 2026-09-19 | 0 | 0 | 937 |
| 2026-09-20 | 0 | 0 | 549 |
| 2026-09-21 | 0 | 0 | 0 |
| 2026-09-22 | 0 | 0 | 0 |
| 2026-09-23 | 0 | 0 | 0 |
| 2026-09-24 | 0 | 0 | 0 |
| 2026-09-25 | 0 | 0 | 0 |
| 2026-09-26 | 0 | 0 | 0 |
| 2026-09-27 | 0 | 0 | 0 |
| 2026-09-28 | 0 | 0 | 0 |
| 2026-09-29 | 0 | 0 | 0 |
| 2025-09-30 | 0 | 0 | 0 |
| 2025-10-01 | 0 | 0 | 0 |
| 2025-10-02 | 0 | 0 | 0 |

## Top countries: past 2 months

| Rank | Country | Tokens | Share |
| --- | --- | --- | --- |
| 1 | United States | 3.5B | 20% |
| 2 | China | 1.9B | 11% |
| 3 | Singapore | 1.2B | 6.8% |
| 4 | Germany | 1B | 5.6% |
| 5 | Indonesia | 900M | 5.4% |
| 6 | France | 700M | 4.2% |
| 7 | Sweden | 700M | 4.1% |
| 8 | Hong Kong | 500M | 3% |
| 9 | Japan | 500M | 2.8% |
| 10 | Brazil | 500M | 2.6% |
| 11 | United Kingdom | 400M | 2.4% |
| 12 | Poland | 400M | 2.2% |
| 13 | Mexico | 400M | 2.2% |
| 14 | Argentina | 300M | 2% |
| 15 | Malaysia | 300M | 1.9% |

## Nearby models by tokens last week

| Rank | Model | Lab | Tokens |
| --- | --- | --- | --- |
| 1 | [space-bunny](https://opencode.ai/data/unknown/space-bunny.md) | - | 57T |
| 2 | [deepseek-v4.1-flash](https://opencode.ai/data/deepseek/deepseek-v4-1-flash.md) | DeepSeek | 33T |
| 3 | [muse-spark-1.3-contributor](https://opencode.ai/data/meta/muse-spark-1-3-contributor.md) | Meta | 31T |
| 4 | [mimo-v2.6-flash](https://opencode.ai/data/xiaomi/mimo-v2-6-flash.md) | Xiaomi | 8.8T |
| 5 | [deepseek-v4-flash](https://opencode.ai/data/deepseek/deepseek-v4-flash.md) | DeepSeek | 5.7T |
| 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 | 794B |
| 10 | [muse-spark-1.2-contributor](https://opencode.ai/data/meta/muse-spark-1-2-contributor.md) | Meta | 738B |

## Benchmarks

| Benchmark | Score | Metric | Source |
| --- | --- | --- | --- |
| SWE-Bench Verified | 70.8 | resolved | https://www.swebench.com/ |
| SWE-Atlas Codebase QnA | 13.1 | score | https://labs.scale.com/leaderboard/sweatlas-qna |
| SWE-Atlas Refactoring | 20.95 | score | https://labs.scale.com/leaderboard/sweatlas-refactoring |
| SWE-Atlas Test Writing | 25.77 | score | https://labs.scale.com/leaderboard/sweatlas-tw |

## 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.