# GLM-5.2 Usage, Cost & Rank | OpenCode Data

> GLM-5.2 ranked #25 by tokens across OpenCode last week, with 0.2% of tokens over the past two months. GLM-5.2 costs $1.40 per 1M input tokens and $4.40 per 1M output tokens.

Open flagship GLM for long-horizon coding agents and million-token context work

- Page: https://opencode.ai/data/zhipuai/glm-5-2
- JSON: https://opencode.ai/data/zhipuai/glm-5-2.json
- Model ID: zhipuai/glm-5.2
- Lab: Zhipu
- Updated: 2026-10-02T08:39:37.000Z

## Model facts

| Fact | Value |
| --- | --- |
| Context window | 1M |
| Max output | 131K |
| Knowledge cutoff | - |
| Release date | 2026-06-13 |
| Input modalities | text |
| Output modalities | text |
| Reasoning | Yes |
| Tool calling | Yes |
| Open weights | Yes |
| Weights | [Hugging Face](https://huggingface.co/zai-org/GLM-5.2) |

## Pricing: USD per 1M tokens

| Input | Output | Cached input | Cache write |
| --- | --- | --- | --- |
| $1.40 | $4.40 | $0.26 | $0.00 |

## OpenCode usage: past 2 months

| Metric | Value |
| --- | --- |
| Rank by tokens last week | #25 |
| Tokens | 2T |
| Share of all tokens | 0.2% |
| Change vs previous 2 months | -77% |
| Unique users | 445K |
| Completed sessions | 604,846 |
| Average tokens per session | 3.3M |
| Average cost per session | $1.6082 |
| Total spend | $972,726 |
| Input tokens served from cache | 84.4% |
| Weekly retention | 45.8% |

## Daily usage: past 2 months

| Date | Tokens | Unique users | Sessions |
| --- | --- | --- | --- |
| 2026-08-08 | 80B | 13K | 13,136 |
| 2026-08-09 | 59B | 12K | 11,821 |
| 2026-08-10 | 105B | 19K | 16,400 |
| 2026-08-11 | 77B | 25K | 8,454 |
| 2026-08-12 | 94B | 21K | 30 |
| 2026-08-13 | 89B | 17K | 46 |
| 2026-08-14 | 75B | 19K | 61 |
| 2026-08-15 | 56B | 13K | 13 |
| 2026-08-16 | 51B | 12K | 13 |
| 2026-08-17 | 91B | 20K | 160 |
| 2026-08-18 | 91B | 17K | 16,494 |
| 2026-08-19 | 79B | 17K | 14,874 |
| 2026-08-20 | 67B | 15K | 15,909 |
| 2026-08-21 | 61B | 15K | 12,029 |
| 2026-08-22 | 44B | 11K | 9,368 |
| 2026-08-23 | 34B | 8.7K | 8,124 |
| 2026-08-24 | 59B | 16K | 14,401 |
| 2026-08-25 | 54B | 14K | 12,824 |
| 2026-08-26 | 53B | 12K | 13,123 |
| 2026-08-27 | 42B | 9.6K | 9,594 |
| 2026-08-28 | 33B | 7.5K | 6,821 |
| 2026-08-29 | 22B | 5.1K | 5,143 |
| 2026-08-30 | 21B | 4.9K | 4,221 |
| 2026-08-31 | 38B | 7.1K | 8,122 |
| 2026-09-01 | 39B | 7.3K | 6,881 |
| 2026-09-02 | 31B | 6.5K | 5,948 |
| 2026-09-03 | 30B | 7.4K | 8,003 |
| 2026-09-04 | 25B | 5.6K | 8,804 |
| 2026-09-05 | 15B | 3.9K | 7,134 |
| 2026-09-06 | 15B | 3.5K | 5,975 |
| 2026-09-07 | 26B | 4.9K | 15,560 |
| 2026-09-08 | 25B | 4.5K | 20,892 |
| 2026-09-09 | 22B | 4.1K | 19,192 |
| 2026-09-10 | 21B | 5.2K | 19,143 |
| 2026-09-11 | 17B | 3.8K | 17,499 |
| 2026-09-12 | 12B | 2.3K | 11,838 |
| 2026-09-13 | 13B | 2.5K | 13,136 |
| 2026-09-14 | 21B | 3.9K | 20,063 |
| 2026-09-15 | 18B | 4K | 21,232 |
| 2026-09-16 | 19B | 3.8K | 20,536 |
| 2026-09-17 | 17B | 3.8K | 21,542 |
| 2026-09-18 | 15B | 3.2K | 17,023 |
| 2026-09-19 | 10B | 2.8K | 17,759 |
| 2026-09-20 | 11B | 3K | 15,116 |
| 2026-09-21 | 17B | 3.9K | 18,833 |
| 2026-09-22 | 15B | 3.1K | 11,924 |
| 2026-09-23 | 12B | 2.6K | 11,330 |
| 2026-09-24 | 11B | 2.3K | 8,185 |
| 2026-09-25 | 9.8B | 2K | 6,905 |
| 2026-09-26 | 6.9B | 1.7K | 5,846 |
| 2026-09-27 | 7.2B | 1.7K | 6,257 |
| 2026-09-28 | 13B | 2.5K | 10,733 |
| 2026-09-29 | 12B | 2.5K | 10,376 |
| 2026-09-30 | 11B | 2.3K | 9,551 |
| 2026-10-01 | 8.7B | 1.9K | 7,797 |
| 2026-10-02 | 2.3B | 778 | 2,652 |

## Top countries: past 2 months

| Rank | Country | Tokens | Share |
| --- | --- | --- | --- |
| 1 | United States | 414B | 20.6% |
| 2 | China | 401B | 20% |
| 3 | Germany | 78B | 3.9% |
| 4 | Brazil | 75B | 3.7% |
| 5 | Hong Kong | 73B | 3.6% |
| 6 | Japan | 69B | 3.4% |
| 7 | Singapore | 61B | 3% |
| 8 | India | 46B | 2.3% |
| 9 | Spain | 45B | 2.3% |
| 10 | France | 43B | 2.1% |
| 11 | Indonesia | 41B | 2% |
| 12 | Russia | 39B | 1.9% |
| 13 | United Kingdom | 37B | 1.9% |
| 14 | Netherlands | 34B | 1.7% |
| 15 | Colombia | 28B | 1.4% |

## Nearby models by tokens last week

| Rank | Model | Lab | Tokens |
| --- | --- | --- | --- |
| 21 | [kimi-k2.7-code](https://opencode.ai/data/moonshotai/kimi-k2-7-code.md) | Moonshot | 136B |
| 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 |

## Benchmarks

| Benchmark | Score | Metric | Source |
| --- | --- | --- | --- |
| SWE-Bench Pro | 62.1 | resolve rate | https://z.ai/blog/glm-5.2 |
| Terminal-Bench | 82.7 | success rate | https://z.ai/blog/glm-5.2 |
| FrontierSWE | 74.4 | dominance | https://z.ai/blog/glm-5.2 |
| Humanity's Last Exam | 40.5 | accuracy | https://z.ai/blog/glm-5.2 |
| Humanity's Last Exam | 54.7 | accuracy | https://z.ai/blog/glm-5.2 |
| CritPt | 20.9 | accuracy | https://z.ai/blog/glm-5.2 |
| AIME | 99.2 | accuracy | https://z.ai/blog/glm-5.2 |
| HMMT | 94.4 | accuracy | https://z.ai/blog/glm-5.2 |
| HMMT | 92.5 | accuracy | https://z.ai/blog/glm-5.2 |
| IMOAnswerBench | 91 | accuracy | https://z.ai/blog/glm-5.2 |
| GPQA Diamond | 91.2 | accuracy | https://z.ai/blog/glm-5.2 |
| NL2Repo | 48.9 | resolve rate | https://z.ai/blog/glm-5.2 |
| DeepSWE | 46.2 | resolve rate | https://z.ai/blog/glm-5.2 |
| Program Bench | 63.7 | score | https://z.ai/blog/glm-5.2 |
| Terminal-Bench | 81 | success rate | https://z.ai/blog/glm-5.2 |
| PostTrainBench | 34.3 | score | https://z.ai/blog/glm-5.2 |
| SWE Marathon | 13 | resolve rate | https://z.ai/blog/glm-5.2 |
| MCP Atlas | 76.8 | score | https://z.ai/blog/glm-5.2 |
| Tool-Decathlon | 48.2 | score | https://z.ai/blog/glm-5.2 |

## 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.