# Zhipu AI Model Usage & Rankings | OpenCode Data

> Zhipu models processed 65T tokens across OpenCode over the past two months, 7.2% of all usage. GLM-5.3-Flash was the most-used Zhipu model.

Z.ai's GLM line focuses on open agentic engineering: long-horizon coding, terminal tasks, and hybrid reasoning at aggressive cost.

- Page: https://opencode.ai/data/zhipuai
- JSON: https://opencode.ai/data/zhipuai.json
- Updated: 2026-10-02T08:39:37.000Z
- Tokens processed: past 2 months: 65T
- Share of all usage: 7.2%

## Zhipu models

| Model | Tokens: past 2 months | Share of lab usage | Context window | Max output | Release date |
| --- | --- | --- | --- | --- | --- |
| [GLM-5.3-Flash](https://opencode.ai/data/zhipuai/glm-5-3-flash.md) | 61T | 94% | 1M | 131K | 2026-08-26 |
| [GLM-5.3](https://opencode.ai/data/zhipuai/glm-5-3.md) | 1.4T | 2.2% | 1M | 131K | 2026-08-14 |
| [GLM-5.2](https://opencode.ai/data/zhipuai/glm-5-2.md) | 2T | 3.1% | 1M | 131K | 2026-06-13 |
| [GLM-5.1](https://opencode.ai/data/zhipuai/glm-5-1.md) | 161B | 0.3% | 200K | 131K | 2026-04-07 |
| [GLM-5V-Turbo](https://opencode.ai/data/zhipuai/glm-5v-turbo.md) | - | - | 200K | 131K | 2026-04-01 |
| [GLM-5-Turbo](https://opencode.ai/data/zhipuai/glm-5-turbo.md) | - | - | 200K | 131K | 2026-03-16 |
| [GLM-5](https://opencode.ai/data/zhipuai/glm-5.md) | 30B | 0.1% | 205K | 131K | 2026-02-12 |
| [GLM-4.7-Flash](https://opencode.ai/data/zhipuai/glm-4-7-flash.md) | - | - | 200K | 131K | 2026-01-19 |
| [GLM-4.7-FlashX](https://opencode.ai/data/zhipuai/glm-4-7-flashx.md) | - | - | 200K | 131K | 2026-01-19 |
| [GLM-Image](https://opencode.ai/data/zhipuai/glm-image.md) | - | - | 10K | 0 | 2026-01-19 |
| [GLM-4.7](https://opencode.ai/data/zhipuai/glm-4-7.md) | - | - | 205K | 131K | 2025-12-22 |
| [GLM-4.6V](https://opencode.ai/data/zhipuai/glm-4-6v.md) | - | - | 128K | 33K | 2025-12-08 |
| [GLM-4.6V-Flash](https://opencode.ai/data/zhipuai/glm-4-6v-flash.md) | - | - | 128K | 33K | 2025-12-08 |
| [GLM-4.6](https://opencode.ai/data/zhipuai/glm-4-6.md) | - | - | 205K | 131K | 2025-09-30 |
| [GLM-4.5V](https://opencode.ai/data/zhipuai/glm-4-5v.md) | - | - | 64K | 16K | 2025-08-11 |
| [GLM-4.5](https://opencode.ai/data/zhipuai/glm-4-5.md) | - | - | 131K | 98K | 2025-07-28 |
| [GLM-4.5-Flash](https://opencode.ai/data/zhipuai/glm-4-5-flash.md) | - | - | 131K | 98K | 2025-07-28 |
| [GLM-4.5-Air](https://opencode.ai/data/zhipuai/glm-4-5-air.md) | - | - | 131K | 98K | 2025-07-28 |

## Daily usage: past 2 months

| Date | Tokens | Unique users |
| --- | --- | --- |
| 2026-08-08 | 83B | 16K |
| 2026-08-09 | 62B | 16K |
| 2026-08-10 | 109B | 26K |
| 2026-08-11 | 82B | 35K |
| 2026-08-12 | 101B | 28K |
| 2026-08-13 | 96B | 23K |
| 2026-08-14 | 120B | 32K |
| 2026-08-15 | 124B | 27K |
| 2026-08-16 | 108B | 27K |
| 2026-08-17 | 181B | 40K |
| 2026-08-18 | 173B | 36K |
| 2026-08-19 | 155B | 37K |
| 2026-08-20 | 218B | 37K |
| 2026-08-21 | 4.2T | 100K |
| 2026-08-22 | 6.7T | 100K |
| 2026-08-23 | 8.5T | 96K |
| 2026-08-24 | 9.9T | 133K |
| 2026-08-25 | 9.1T | 122K |
| 2026-08-26 | 6.4T | 104K |
| 2026-08-27 | 714B | 44K |
| 2026-08-28 | 690B | 40K |
| 2026-08-29 | 504B | 30K |
| 2026-08-30 | 488B | 29K |
| 2026-08-31 | 743B | 42K |
| 2026-09-01 | 763B | 42K |
| 2026-09-02 | 640B | 40K |
| 2026-09-03 | 595B | 41K |
| 2026-09-04 | 531B | 37K |
| 2026-09-05 | 393B | 26K |
| 2026-09-06 | 374B | 25K |
| 2026-09-07 | 567B | 35K |
| 2026-09-08 | 554B | 33K |
| 2026-09-09 | 587B | 30K |
| 2026-09-10 | 642B | 32K |
| 2026-09-11 | 500B | 26K |
| 2026-09-12 | 425B | 19K |
| 2026-09-13 | 430B | 19K |
| 2026-09-14 | 669B | 30K |
| 2026-09-15 | 674B | 30K |
| 2026-09-16 | 636B | 30K |
| 2026-09-17 | 606B | 30K |
| 2026-09-18 | 37B | 9.7K |
| 2026-09-19 | 408B | 23K |
| 2026-09-20 | 455B | 24K |
| 2026-09-21 | 608B | 32K |
| 2026-09-22 | 544B | 29K |
| 2026-09-23 | 477B | 26K |
| 2026-09-24 | 430B | 23K |
| 2026-09-25 | 394B | 20K |
| 2026-09-26 | 328B | 17K |
| 2026-09-27 | 322B | 17K |
| 2026-09-28 | 440B | 23K |
| 2026-09-29 | 422B | 23K |
| 2026-09-30 | 390B | 21K |
| 2026-10-01 | 300B | 19K |
| 2026-10-02 | 84B | 8.6K |

## 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.