# Qwen3.8 Flash Usage, Cost & Rank | OpenCode Data

> Qwen3.8 Flash ranked #17 by tokens across OpenCode last week, with 0.2% of tokens over the past two months. Qwen3.8 Flash costs $0.15 per 1M input tokens and $0.47 per 1M output tokens.

Qwen vision-language model for visual reasoning, documents, and agent tasks

- Page: https://opencode.ai/data/alibaba/qwen3-8-flash
- JSON: https://opencode.ai/data/alibaba/qwen3-8-flash.json
- Model ID: alibaba/qwen3.8-flash
- Lab: Alibaba
- Updated: 2026-10-02T07:36:56.000Z

## Model facts

| Fact | Value |
| --- | --- |
| Context window | 1M |
| Max output | 131K |
| Knowledge cutoff | - |
| Release date | 2026-08-26 |
| Input modalities | text, image, video |
| Output modalities | text |
| Reasoning | Yes |
| Tool calling | Yes |
| Open weights | No |

## Pricing: USD per 1M tokens

| Input | Output | Cached input | Cache write |
| --- | --- | --- | --- |
| $0.15 | $0.47 | $0.02 | $0.20 |

## OpenCode usage: past 2 months

| Metric | Value |
| --- | --- |
| Rank by tokens last week | #17 |
| Tokens | 2T |
| Share of all tokens | 0.2% |
| Change vs previous 2 months | +100% |
| Unique users | 151K |
| Completed sessions | 1,082,729 |
| Average tokens per session | 1.9M |
| Average cost per session | $0.0634 |
| Total spend | $68,605 |
| Input tokens served from cache | 95.7% |
| Weekly retention | 60.0% |

## Daily usage: past 2 months

| Date | Tokens | Unique users | Sessions |
| --- | --- | --- | --- |
| 2026-08-28 | 33B | 2.9K | 4,989 |
| 2026-08-29 | 53B | 3.3K | 6,874 |
| 2026-08-30 | 51B | 3.5K | 7,706 |
| 2026-08-31 | 77B | 5.3K | 10,732 |
| 2026-09-01 | 102B | 5.5K | 12,622 |
| 2026-09-02 | 108B | 5.6K | 14,893 |
| 2026-09-03 | 107B | 6K | 18,435 |
| 2026-09-04 | 95B | 5.6K | 17,918 |
| 2026-09-05 | 60B | 4K | 15,541 |
| 2026-09-06 | 57B | 3.8K | 12,666 |
| 2026-09-07 | 89B | 5.5K | 32,989 |
| 2026-09-08 | 94B | 5.6K | 48,005 |
| 2026-09-09 | 89B | 5.4K | 42,950 |
| 2026-09-10 | 69B | 5.3K | 35,032 |
| 2026-09-11 | 46B | 4.2K | 31,493 |
| 2026-09-12 | 34B | 3.1K | 24,290 |
| 2026-09-13 | 36B | 3K | 21,187 |
| 2026-09-14 | 53B | 4.7K | 32,135 |
| 2026-09-15 | 57B | 4.6K | 29,133 |
| 2026-09-16 | 50B | 4.3K | 100,493 |
| 2026-09-17 | 51B | 4.5K | 67,907 |
| 2026-09-18 | 46B | 4.3K | 24,614 |
| 2026-09-19 | 38B | 3.7K | 32,495 |
| 2026-09-20 | 35B | 3.6K | 30,745 |
| 2026-09-21 | 54B | 4.9K | 86,919 |
| 2026-09-22 | 51B | 4.7K | 38,084 |
| 2026-09-23 | 46B | 4.2K | 28,808 |
| 2026-09-24 | 44B | 3.8K | 32,536 |
| 2026-09-25 | 38B | 3.4K | 29,983 |
| 2026-09-26 | 34B | 3K | 25,951 |
| 2026-09-27 | 33B | 2.9K | 27,585 |
| 2026-09-28 | 46B | 4K | 36,109 |
| 2026-09-29 | 49B | 4.1K | 31,641 |
| 2026-09-30 | 43B | 3.7K | 35,902 |
| 2026-10-01 | 39B | 3.4K | 25,714 |
| 2026-10-02 | 10B | 1.4K | 7,653 |

## Top countries: past 2 months

| Rank | Country | Tokens | Share |
| --- | --- | --- | --- |
| 1 | China | 355B | 17.6% |
| 2 | United States | 256B | 12.7% |
| 3 | Germany | 109B | 5.4% |
| 4 | Brazil | 78B | 3.9% |
| 5 | Spain | 77B | 3.8% |
| 6 | Japan | 71B | 3.5% |
| 7 | Russia | 59B | 2.9% |
| 8 | Hong Kong | 53B | 2.6% |
| 9 | Singapore | 47B | 2.3% |
| 10 | France | 47B | 2.3% |
| 11 | India | 44B | 2.2% |
| 12 | United Kingdom | 42B | 2.1% |
| 13 | Netherlands | 40B | 2% |
| 14 | Colombia | 40B | 2% |
| 15 | Australia | 38B | 1.9% |

## Nearby models by tokens last week

| Rank | Model | Lab | Tokens |
| --- | --- | --- | --- |
| 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 |
| 15 | [nemotron-3.5-lightning](https://opencode.ai/data/nvidia/nemotron-3-5-lightning.md) | NVIDIA | 264B |
| 16 | [ling-3.0-flash-fin](https://opencode.ai/data/inclusionai/ling-3-0-flash-fin.md) | inclusionAI | 256B |
| 17 | [qwen3.8-flash](https://opencode.ai/data/alibaba/qwen3-8-flash.md) | Qwen | 254B |
| 18 | [gpt-5.6-luna](https://opencode.ai/data/openai/gpt-5-6-luna.md) | OpenAI | 194B |
| 19 | [minimax-m3](https://opencode.ai/data/minimax/minimax-m3.md) | MiniMax | 180B |
| 20 | [mai-experimental-test](https://opencode.ai/data/unknown/mai-experimental-test.md) | - | 171B |
| 21 | [kimi-k2.7-code](https://opencode.ai/data/moonshotai/kimi-k2-7-code.md) | Moonshot | 135B |
| 22 | [glm-5.3](https://opencode.ai/data/zhipuai/glm-5-3.md) | Zhipu | 95B |

## 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.