# GLM-5.3-Flash Usage, Cost & Rank | OpenCode Data

> GLM-5.3-Flash ranked #8 by tokens across OpenCode last week, with 6.8% of tokens over the past two months. GLM-5.3-Flash costs $0.15 per 1M input tokens and $0.50 per 1M output tokens.

Native multimodal GLM model for efficient coding and long-horizon agent tasks

- Page: https://opencode.ai/data/zhipuai/glm-5-3-flash
- JSON: https://opencode.ai/data/zhipuai/glm-5-3-flash.json
- Model ID: zhipuai/glm-5.3-flash
- Lab: Zhipu
- Updated: 2026-10-02T08:39:37.000Z

## Model facts

| Fact | Value |
| --- | --- |
| Context window | 1M |
| Max output | 131K |
| Knowledge cutoff | - |
| Release date | 2026-08-26 |
| Input modalities | text, image, video, pdf |
| Output modalities | text |
| Reasoning | Yes |
| Tool calling | Yes |
| Open weights | Yes |

## Pricing: USD per 1M tokens

| Input | Output | Cached input | Cache write |
| --- | --- | --- | --- |
| $0.15 | $0.50 | $0.03 | $0.00 |

## OpenCode usage: past 2 months

| Metric | Value |
| --- | --- |
| Rank by tokens last week | #8 |
| Tokens | 61T |
| Share of all tokens | 6.8% |
| Change vs previous 2 months | +100% |
| Unique users | 1.1M |
| Completed sessions | 18,108,542 |
| Average tokens per session | 3.4M |
| Average cost per session | $0.0383 |
| Total spend | $692,801 |
| Input tokens served from cache | 93.3% |
| Weekly retention | 63.2% |

## Daily usage: past 2 months

| Date | Tokens | Unique users | Sessions |
| --- | --- | --- | --- |
| 2026-08-20 | 74B | 4.1K | 50,924 |
| 2026-08-21 | 4.1T | 67K | 1,406,605 |
| 2026-08-22 | 6.6T | 75K | 1,785,651 |
| 2026-08-23 | 8.4T | 76K | 2,778,237 |
| 2026-08-24 | 9.8T | 98K | 2,896,914 |
| 2026-08-25 | 9T | 92K | 2,590,151 |
| 2026-08-26 | 6.3T | 78K | 1,623,018 |
| 2026-08-27 | 634B | 22K | 57,923 |
| 2026-08-28 | 620B | 22K | 66,955 |
| 2026-08-29 | 453B | 17K | 55,204 |
| 2026-08-30 | 442B | 16K | 56,194 |
| 2026-08-31 | 669B | 24K | 82,095 |
| 2026-09-01 | 695B | 24K | 77,305 |
| 2026-09-02 | 581B | 23K | 76,663 |
| 2026-09-03 | 538B | 24K | 75,809 |
| 2026-09-04 | 485B | 22K | 74,543 |
| 2026-09-05 | 364B | 16K | 58,544 |
| 2026-09-06 | 343B | 15K | 67,995 |
| 2026-09-07 | 516B | 21K | 109,473 |
| 2026-09-08 | 506B | 21K | 121,654 |
| 2026-09-09 | 545B | 20K | 116,485 |
| 2026-09-10 | 603B | 21K | 135,736 |
| 2026-09-11 | 468B | 17K | 221,265 |
| 2026-09-12 | 399B | 13K | 160,174 |
| 2026-09-13 | 403B | 12K | 107,615 |
| 2026-09-14 | 624B | 20K | 151,947 |
| 2026-09-15 | 635B | 20K | 155,049 |
| 2026-09-16 | 596B | 19K | 156,656 |
| 2026-09-17 | 566B | 20K | 261,420 |
| 2026-09-18 | 0 | 0 | 229 |
| 2026-09-19 | 380B | 15K | 144,044 |
| 2026-09-20 | 426B | 16K | 190,569 |
| 2026-09-21 | 566B | 21K | 218,287 |
| 2026-09-22 | 506B | 19K | 193,739 |
| 2026-09-23 | 446B | 18K | 178,369 |
| 2026-09-24 | 402B | 16K | 169,472 |
| 2026-09-25 | 370B | 13K | 170,292 |
| 2026-09-26 | 309B | 12K | 150,745 |
| 2026-09-27 | 299B | 12K | 271,641 |
| 2026-09-28 | 410B | 15K | 348,061 |
| 2026-09-29 | 393B | 15K | 171,684 |
| 2026-09-30 | 362B | 14K | 144,338 |
| 2026-10-01 | 276B | 13K | 137,476 |
| 2026-10-02 | 78B | 6.2K | 41,392 |

## Top countries: past 2 months

| Rank | Country | Tokens | Share |
| --- | --- | --- | --- |
| 1 | China | 9.2T | 15% |
| 2 | United States | 8.5T | 13.8% |
| 3 | India | 3.7T | 6% |
| 4 | Germany | 3T | 4.9% |
| 5 | Japan | 2.4T | 3.9% |
| 6 | Brazil | 2T | 3.2% |
| 7 | Indonesia | 1.6T | 2.6% |
| 8 | France | 1.4T | 2.3% |
| 9 | United Kingdom | 1.4T | 2.2% |
| 10 | Singapore | 1.4T | 2.2% |
| 11 | Russia | 1.4T | 2.2% |
| 12 | Hong Kong | 1.2T | 1.9% |
| 13 | Canada | 1.2T | 1.9% |
| 14 | Netherlands | 1.1T | 1.9% |
| 15 | Spain | 1.1T | 1.8% |

## Nearby models by tokens last week

| Rank | Model | Lab | Tokens |
| --- | --- | --- | --- |
| 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 |
| 11 | [deepseek-v4-flash-vision-exp](https://opencode.ai/data/deepseek/deepseek-v4-flash-vision-exp.md) | DeepSeek | 687B |
| 12 | [gpt-6-luna](https://opencode.ai/data/openai/gpt-6-luna.md) | OpenAI | 640B |
| 13 | [deepseek-v4-pro](https://opencode.ai/data/deepseek/deepseek-v4-pro.md) | DeepSeek | 465B |

## Benchmarks

| Benchmark | Score | Metric | Source |
| --- | --- | --- | --- |
| Terminal-Bench | 84.3 | pass@1 | https://huggingface.co/zai-org/GLM-5.3-Flash |
| DeepSWE | 63.4 | resolved | https://huggingface.co/zai-org/GLM-5.3-Flash |
| Agents' Last Exam | 26.3 | score | https://huggingface.co/zai-org/GLM-5.3-Flash |
| AutomationBench | 48.8 | pass@1 | https://huggingface.co/zai-org/GLM-5.3-Flash |
| Humanity's Last Exam | 55.3 | accuracy | https://huggingface.co/zai-org/GLM-5.3-Flash |
| GDPval-AA | 1773 | Elo | https://huggingface.co/zai-org/GLM-5.3-Flash |

## 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.