# Qwen3.5 Plus Usage, Cost & Rank | OpenCode Data

> Qwen3.5 Plus had 0% of tokens across OpenCode over the past two months. Qwen3.5 Plus costs $0.40 per 1M input tokens and $2.40 per 1M output tokens.

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

- Page: https://opencode.ai/data/alibaba/qwen3-5-plus
- JSON: https://opencode.ai/data/alibaba/qwen3-5-plus.json
- Model ID: alibaba/qwen3.5-plus
- Lab: Alibaba
- Updated: 2026-09-28T12:09:39.000Z

## Model facts

| Fact | Value |
| --- | --- |
| Context window | 1M |
| Max output | 66K |
| Knowledge cutoff | 2025-04 |
| Release date | 2026-02-16 |
| 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 |
| --- | --- | --- | --- |
| $0.40 | $2.40 | $0.04 | $0.50 |

## OpenCode usage: past 2 months

| Metric | Value |
| --- | --- |
| Rank by tokens last week | Unranked |
| Tokens | 14B |
| Share of all tokens | 0% |
| Change vs previous 2 months | -35% |
| Unique users | 21K |
| Completed sessions | 20,171 |
| Average tokens per session | 711K |
| Average cost per session | $0.1186 |
| Total spend | $2,393 |
| Input tokens served from cache | 48.4% |
| Weekly retention | 67.1% |

## Daily usage: past 2 months

| Date | Tokens | Unique users | Sessions |
| --- | --- | --- | --- |
| 2026-08-08 | 252M | 520 | 24 |
| 2026-08-09 | 200M | 417 | 9 |
| 2026-08-10 | 577M | 624 | 19 |
| 2026-08-11 | 613M | 948 | 16 |
| 2026-08-12 | 498M | 725 | 0 |
| 2026-08-13 | 760M | 843 | 1 |
| 2026-08-14 | 504M | 879 | 0 |
| 2026-08-15 | 422M | 937 | 0 |
| 2026-08-16 | 265M | 1K | 0 |
| 2026-08-17 | 340M | 1.3K | 3 |
| 2026-08-18 | 827M | 1K | 30 |
| 2026-08-19 | 713M | 1K | 197 |
| 2026-08-20 | 745M | 1K | 209 |
| 2026-08-21 | 481M | 783 | 158 |
| 2026-08-22 | 669M | 608 | 156 |
| 2026-08-23 | 845M | 595 | 19 |
| 2026-08-24 | 707M | 700 | 20 |
| 2026-08-25 | 788M | 637 | 15 |
| 2026-08-26 | 440M | 615 | 78 |
| 2026-08-27 | 439M | 568 | 59 |
| 2026-08-28 | 308M | 495 | 45 |
| 2026-08-29 | 187M | 439 | 151 |
| 2026-08-30 | 254M | 365 | 112 |
| 2026-08-31 | 249M | 569 | 217 |
| 2026-09-01 | 456M | 520 | 94 |
| 2026-09-02 | 245M | 440 | 70 |
| 2026-09-03 | 240M | 492 | 194 |
| 2026-09-04 | 251M | 459 | 706 |
| 2026-09-05 | 203M | 357 | 868 |
| 2026-09-06 | 274M | 342 | 1,434 |
| 2026-09-07 | 222M | 412 | 2,128 |
| 2026-09-08 | 287M | 370 | 1,560 |
| 2026-09-09 | 90M | 132 | 1,079 |
| 2026-09-10 | 0 | 0 | 1,163 |
| 2026-09-11 | 0 | 0 | 1,095 |
| 2026-09-12 | 0 | 0 | 1,025 |
| 2026-09-13 | 0 | 0 | 1,112 |
| 2026-09-14 | 0 | 0 | 830 |
| 2026-09-15 | 0 | 0 | 1,312 |
| 2026-09-16 | 0 | 0 | 1,004 |
| 2026-09-17 | 0 | 0 | 965 |
| 2026-09-18 | 0 | 0 | 799 |
| 2026-09-19 | 0 | 0 | 687 |
| 2026-09-20 | 0 | 0 | 508 |
| 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 | 3.7B | 25.7% |
| 2 | China | 2.1B | 14.6% |
| 3 | Germany | 900M | 6.5% |
| 4 | France | 600M | 4.3% |
| 5 | United Kingdom | 600M | 4.2% |
| 6 | Spain | 600M | 4% |
| 7 | Singapore | 600M | 3.9% |
| 8 | India | 500M | 3.6% |
| 9 | Czechia | 400M | 3.1% |
| 10 | Japan | 400M | 2.5% |
| 11 | Canada | 400M | 2.5% |
| 12 | Brazil | 300M | 2.3% |
| 13 | Vietnam | 300M | 2.3% |
| 14 | Colombia | 300M | 2.2% |
| 15 | Indonesia | 200M | 1.4% |

## 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 |

## 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.