# Kimi K3 Usage, Cost & Rank | OpenCode Data

> Kimi K3 ranked #26 by tokens across OpenCode last week, with 0.1% of tokens over the past two months. Kimi K3 costs $3.00 per 1M input tokens and $15.00 per 1M output tokens.

Multimodal Kimi model with 1M context and toggleable max-effort thinking for long-horizon agent work

- Page: https://opencode.ai/data/moonshotai/kimi-k3
- JSON: https://opencode.ai/data/moonshotai/kimi-k3.json
- Model ID: moonshotai/kimi-k3
- Lab: Moonshot
- Updated: 2026-10-02T08:39:37.000Z

## Model facts

| Fact | Value |
| --- | --- |
| Context window | 1M |
| Max output | 131K |
| Knowledge cutoff | - |
| Release date | 2026-07-16 |
| Input modalities | text, image, video |
| Output modalities | text |
| Reasoning | Yes |
| Tool calling | Yes |
| Open weights | Yes |

## Pricing: USD per 1M tokens

| Input | Output | Cached input | Cache write |
| --- | --- | --- | --- |
| $3.00 | $15.00 | $0.30 | - |

## OpenCode usage: past 2 months

| Metric | Value |
| --- | --- |
| Rank by tokens last week | #26 |
| Tokens | 1T |
| Share of all tokens | 0.1% |
| Change vs previous 2 months | -4% |
| Unique users | 463K |
| Completed sessions | 715,061 |
| Average tokens per session | 1.4M |
| Average cost per session | $1.1861 |
| Total spend | $848,153 |
| Input tokens served from cache | 87.7% |
| Weekly retention | 36.4% |

## Daily usage: past 2 months

| Date | Tokens | Unique users | Sessions |
| --- | --- | --- | --- |
| 2026-08-08 | 25B | 12K | 9,801 |
| 2026-08-09 | 24B | 11K | 8,732 |
| 2026-08-10 | 31B | 15K | 13,863 |
| 2026-08-11 | 35B | 21K | 7,224 |
| 2026-08-12 | 41B | 17K | 623 |
| 2026-08-13 | 38B | 17K | 292 |
| 2026-08-14 | 35B | 14K | 256 |
| 2026-08-15 | 29B | 12K | 233 |
| 2026-08-16 | 30B | 12K | 176 |
| 2026-08-17 | 44B | 18K | 381 |
| 2026-08-18 | 36B | 19K | 10,173 |
| 2026-08-19 | 27B | 17K | 11,529 |
| 2026-08-20 | 22B | 13K | 11,069 |
| 2026-08-21 | 26B | 15K | 31,829 |
| 2026-08-22 | 32B | 16K | 10,202 |
| 2026-08-23 | 24B | 10K | 10,275 |
| 2026-08-24 | 41B | 18K | 23,017 |
| 2026-08-25 | 54B | 13K | 45,559 |
| 2026-08-26 | 34B | 11K | 26,444 |
| 2026-08-27 | 19B | 7.8K | 11,695 |
| 2026-08-28 | 13B | 6.3K | 7,248 |
| 2026-08-29 | 11B | 4.7K | 6,730 |
| 2026-08-30 | 9.2B | 4.7K | 6,025 |
| 2026-08-31 | 16B | 7.7K | 8,629 |
| 2026-09-01 | 13B | 6.5K | 7,434 |
| 2026-09-02 | 13B | 6.5K | 14,847 |
| 2026-09-03 | 15B | 7.6K | 32,425 |
| 2026-09-04 | 17B | 6.7K | 9,597 |
| 2026-09-05 | 10B | 4.4K | 9,193 |
| 2026-09-06 | 10B | 4.4K | 9,677 |
| 2026-09-07 | 15B | 6.2K | 35,892 |
| 2026-09-08 | 13B | 5.2K | 25,690 |
| 2026-09-09 | 11B | 5K | 15,024 |
| 2026-09-10 | 11B | 5.7K | 15,017 |
| 2026-09-11 | 9.2B | 4.8K | 13,414 |
| 2026-09-12 | 8.1B | 4.1K | 13,850 |
| 2026-09-13 | 7.9B | 4.1K | 11,490 |
| 2026-09-14 | 11B | 5.8K | 17,178 |
| 2026-09-15 | 11B | 5.4K | 13,296 |
| 2026-09-16 | 12B | 5.2K | 14,450 |
| 2026-09-17 | 12B | 5.3K | 16,477 |
| 2026-09-18 | 11B | 4.8K | 14,777 |
| 2026-09-19 | 10B | 4K | 13,779 |
| 2026-09-20 | 10B | 4.3K | 17,155 |
| 2026-09-21 | 12B | 5.2K | 16,517 |
| 2026-09-22 | 10B | 5.1K | 13,908 |
| 2026-09-23 | 8.9B | 4.1K | 10,853 |
| 2026-09-24 | 9.2B | 3.7K | 9,872 |
| 2026-09-25 | 8.4B | 3.2K | 8,762 |
| 2026-09-26 | 6.7B | 3K | 8,636 |
| 2026-09-27 | 5.6B | 2.9K | 8,787 |
| 2026-09-28 | 9B | 4.2K | 13,421 |
| 2026-09-29 | 8.3B | 4.2K | 14,428 |
| 2026-09-30 | 7.4B | 3.8K | 13,022 |
| 2026-10-01 | 6.4B | 3.3K | 10,875 |
| 2026-10-02 | 1.5B | 1.2K | 3,313 |

## Top countries: past 2 months

| Rank | Country | Tokens | Share |
| --- | --- | --- | --- |
| 1 | United States | 303B | 29.7% |
| 2 | China | 113B | 11.1% |
| 3 | Germany | 40B | 3.9% |
| 4 | Singapore | 37B | 3.6% |
| 5 | Brazil | 34B | 3.3% |
| 6 | Japan | 34B | 3.3% |
| 7 | Hong Kong | 32B | 3.2% |
| 8 | Spain | 28B | 2.7% |
| 9 | India | 24B | 2.4% |
| 10 | France | 23B | 2.3% |
| 11 | Netherlands | 21B | 2% |
| 12 | United Kingdom | 19B | 1.9% |
| 13 | Canada | 19B | 1.8% |
| 14 | Mexico | 15B | 1.4% |
| 15 | Colombia | 14B | 1.3% |

## Nearby models by tokens last week

| Rank | Model | Lab | Tokens |
| --- | --- | --- | --- |
| 22 | [glm-5.3](https://opencode.ai/data/zhipuai/glm-5-3.md) | Zhipu | 95B |
| 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 |

## Benchmarks

| Benchmark | Score | Metric | Source |
| --- | --- | --- | --- |
| DeepSWE | 67.5 | resolve rate | https://www.kimi.com/blog/kimi-k3 |
| Terminal-Bench | 88.3 | accuracy | https://www.kimi.com/blog/kimi-k3 |
| FrontierSWE | 81.2 | dominance score | https://www.kimi.com/blog/kimi-k3 |
| Program Bench | 77.8 | score | https://www.kimi.com/blog/kimi-k3 |
| SWE Marathon | 42 | resolve rate | https://www.kimi.com/blog/kimi-k3 |
| GDPval-AA | 1668 | Elo | https://www.kimi.com/blog/kimi-k3 |
| AA-Briefcase | 1548 | Elo | https://www.kimi.com/blog/kimi-k3 |
| AutomationBench | 30.8 | success rate | https://www.kimi.com/blog/kimi-k3 |
| JobBench | 52.9 | score | https://www.kimi.com/blog/kimi-k3 |
| SpreadsheetBench | 34.8 | score | https://www.kimi.com/blog/kimi-k3 |
| BrowseComp | 91.2 | accuracy | https://www.kimi.com/blog/kimi-k3 |
| CharXiv Reasoning | 91.3 | accuracy | https://www.kimi.com/blog/kimi-k3 |
| ZeroBench | 41 | pass@5 | https://www.kimi.com/blog/kimi-k3 |

## 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.