Z.ai (Zhipu), open weights

# GLM-4.5

> GLM-4.5 by Z.ai (Zhipu), released July 2025. Ranked #122 of 354 with a Noometry Index of 42.0. API: $0.60 in / $2.20 out per M tokens. 131K context. Scores, sources and comparisons.
- Canonical page: https://noometry.com/models/glm-4-5
- Last updated: 2026-10-10
- Title: GLM-4.5 Benchmarks, Price & Rank (October 2026) | Noometry

GLM-4.5 by Z.ai (Zhipu) ranks 122nd of 354 ranked models on the Noometry Index as of October 2026, with a score of 42.0. Its strongest category is multilingual, where it ranks 77th. API pricing starts at $0.60 per million input tokens and $2.20 per million output tokens, with a 131K-token context window.

Last verified October 10, 2026

## Specifications

- **Noometry rank:** #122 of 354
- **Index score:** 42.0
- **Evidence:** Confirmed 27 results
- **Provider:** [Z.ai (Zhipu)](https://noometry.com/providers/zai)
- **Released:** July 27, 2025
- **Weights:** Open weights
- **Reasoning:** Yes
- **Context window:** 131K
- **Max output:** 98K
- **Input price:** $0.60 / M
- **Output price:** $2.20 / M
- **Blended price:** $1 / M
- **Output speed:** 32 tokens/s [Kagi](https://help.kagi.com/kagi/ai/llm-benchmark.html)
- **Value:** #109 of 219
- **Knowledge cutoff:** April 2025
- **Input:** text
- **Hugging Face:** [zai-org/GLM-4.5](https://huggingface.co/zai-org/GLM-4.5)

## Category scores

Each category score combines every public result we have in that category.

GLM-4.5 category scores

1.  Coding 41.4
2.  Reasoning 28.6
3.  Math 39.0
4.  Knowledge 35.9
5.  Multilingual 52.8
6.  Instruction Following 74.1
7.  Long Context 38.2
8.  Writing & Preference 57.5
9.  020406080

GLM-4.5 category ranks
| Category | Score | Rank | Results |
| --- | --- | --- | --- |
| [Coding](https://noometry.com/best/coding) | 41.4 | #125 | 3 |
| [Reasoning](https://noometry.com/best/reasoning) | 28.6 | #100 | 2 |
| [Math](https://noometry.com/best/math) | 39.0 | #116 | 1 |
| [Knowledge](https://noometry.com/best/knowledge) | 35.9 | #179 | 3 |
| [Multilingual](https://noometry.com/best/multilingual) | 52.8 | #77 | 1 |
| [Instruction Following](https://noometry.com/best/instruction-following) | 74.1 | #104 | 1 |
| [Long Context](https://noometry.com/best/long-context) | 38.2 | #201 | 2 |
| [Writing & Preference](https://noometry.com/best/writing) | 57.5 | #127 | 5 |

## Strengths and weaknesses

Categories where GLM-4.5 places highest and lowest among the models ranked in each, with its score against that category's median.

### Strongest categories

GLM-4.5: strongest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Multilingual](https://noometry.com/best/multilingual) | 52.8 | +5.4 | #77 of 297, top 26% |
| [Reasoning](https://noometry.com/best/reasoning) | 28.6 | +5.0 | #100 of 350, top 29% |
| [Instruction Following](https://noometry.com/best/instruction-following) | 74.1 | +2.8 | #104 of 305, top 35% |

### Weakest categories

GLM-4.5: weakest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Long Context](https://noometry.com/best/long-context) | 38.2 | −2.7 | #201 of 296, top 68% |
| [Knowledge](https://noometry.com/best/knowledge) | 35.9 | −1.5 | #179 of 314, top 58% |
| [Writing & Preference](https://noometry.com/best/writing) | 57.5 | +3.7 | #127 of 312, top 41% |

## Closest competitors

The models ranked just above and below GLM-4.5. When scores are this close, price and speed are often the better way to choose.

Models ranked closest to GLM-4.5
| Model | Rank | Score | Blended $/M | Speed |  |
| --- | --- | --- | --- | --- | --- |
| [Amazon Nova Experimental Chat 10 20](https://noometry.com/models/amazon-nova-experimental-chat-10-20) | #118 | 42.1 | — | — | [Compare](https://noometry.com/compare/amazon-nova-experimental-chat-10-20-vs-glm-4-5) |
| [Qwen3.5 122B-A10B](https://noometry.com/models/qwen3-5-122b-a10b) | #119 | 42.1 | $1.10 | — | [Compare](https://noometry.com/compare/glm-4-5-vs-qwen3-5-122b-a10b) |
| [Longcat Flash Chat](https://noometry.com/models/longcat-flash-chat) | #120 | 42.1 | — | 69 | [Compare](https://noometry.com/compare/glm-4-5-vs-longcat-flash-chat) |
| [Solar Pro4](https://noometry.com/models/solar-pro4) | #121 | 42.1 | $0.52 | — | [Compare](https://noometry.com/compare/glm-4-5-vs-solar-pro4) |
| [Qwen3.5 35B-A3B](https://noometry.com/models/qwen3-5-35b-a3b) | #123 | 42.0 | $0.69 | — | [Compare](https://noometry.com/compare/glm-4-5-vs-qwen3-5-35b-a3b) |
| [GLM-4.7](https://noometry.com/models/glm-4-7) | #124 | 42.0 | $1 | — | [Compare](https://noometry.com/compare/glm-4-5-vs-glm-4-7) |
| [GPT-5.4 nano](https://noometry.com/models/gpt-5-4-nano) | #125 | 41.9 | $0.46 | 19 | [Compare](https://noometry.com/compare/glm-4-5-vs-gpt-5-4-nano) |
| [Amazon Nova Experimental Chat 10 09](https://noometry.com/models/amazon-nova-experimental-chat-10-09) | #126 | 41.9 | — | — | [Compare](https://noometry.com/compare/amazon-nova-experimental-chat-10-09-vs-glm-4-5) |

Sponsored placements are available on pages like this one. [Advertise on Noometry](https://noometry.com/advertise)

## Benchmark results

Every published result we track, with its source. Bold rows are the ones used for ranking; where several exist we prefer independent runs over self-reported numbers.

### Coding

GLM-4.5 Coding benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [SWE-bench Verified (bash only)](https://noometry.com/benchmarks/swe-bench-bash-only) | 54.2% | #25 of 39, top 65% |  | [SWE-bench](https://www.swebench.com/) | 2025-08-22 |
| [WeirdML](https://noometry.com/benchmarks/weirdml) | 40.6% | #76 of 119, top 64% | thinking | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Coding](https://noometry.com/benchmarks/arena-coding) | 1434 | #105 of 294, top 36% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [ALE-Bench](https://noometry.com/benchmarks/ale-bench) | 344.82 | #94 of 105, top 90% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [AlgoTune](https://noometry.com/benchmarks/algotune) | 1.52 | #10 of 18, top 56% | thinking | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Reasoning

GLM-4.5 Reasoning benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [Kagi LLM Benchmark](https://noometry.com/benchmarks/kagi-reasoning) | 57.9% | #45 of 99, top 46% |  | [Kagi LLM Benchmark](https://help.kagi.com/kagi/ai/llm-benchmark.html) |  |
| [LMArena Hard Prompts](https://noometry.com/benchmarks/arena-hard-prompts) | 1429 | #90 of 297, top 31% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Math

GLM-4.5 Math benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Math](https://noometry.com/benchmarks/arena-math) | 1427 | #91 of 285, top 32% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Knowledge

GLM-4.5 Knowledge benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [Humanity's Last Exam](https://noometry.com/benchmarks/hle) | 8.3% | #26 of 41, top 64% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Confabulations](https://noometry.com/benchmarks/confabulations) (lower is better) | 11.3% | #4 of 51, top 8% |  | [Lech Mazur benchmarks](https://github.com/lechmazur/confabulations) |  |
| [LMArena Expert](https://noometry.com/benchmarks/arena-expert) | 1433 | #92 of 273, top 34% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Multilingual

GLM-4.5 Multilingual benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Non-English](https://noometry.com/benchmarks/arena-non-english) | 1417 | #77 of 297, top 26% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Chinese](https://noometry.com/benchmarks/arena-chinese) | 1465 | #79 of 285, top 28% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena French](https://noometry.com/benchmarks/arena-french) | 1418 | #104 of 223, top 47% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena German](https://noometry.com/benchmarks/arena-german) | 1407 | #89 of 231, top 39% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Japanese](https://noometry.com/benchmarks/arena-japanese) | 1415 | #45 of 211, top 22% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Korean](https://noometry.com/benchmarks/arena-korean) | 1380 | #78 of 213, top 37% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Russian](https://noometry.com/benchmarks/arena-russian) | 1414 | #90 of 283, top 32% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Spanish](https://noometry.com/benchmarks/arena-spanish) | 1454 | #44 of 226, top 20% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Instruction Following

GLM-4.5 Instruction Following benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Instruction Following](https://noometry.com/benchmarks/arena-instruction-following) | 1404 | #98 of 298, top 33% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Long Context

GLM-4.5 Long Context benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [Fiction.LiveBench](https://noometry.com/benchmarks/fiction-livebench) | 58.3% | #30 of 47, top 64% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Longer Query](https://noometry.com/benchmarks/arena-longer-query) | 1412 | #104 of 291, top 36% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Writing & Preference

GLM-4.5 Writing & Preference benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Text](https://noometry.com/benchmarks/arena-text) | 1430 | #78 of 297, top 27% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Creative Writing](https://noometry.com/benchmarks/arena-creative-writing) | 1395 | #87 of 295, top 30% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [Short-Story Creative Writing](https://noometry.com/benchmarks/lech-mazur-writing) | 73.4% | #29 of 39, top 75% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [EQ-Bench Creative Writing](https://noometry.com/benchmarks/eqbench-creative-writing) | 1343 | #75 of 115, top 66% |  | [EQ-Bench](https://eqbench.com/creative_writing.html) |  |
| [LMArena Multi-Turn](https://noometry.com/benchmarks/arena-multi-turn) | 1415 | #102 of 295, top 35% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

## API pricing by provider

GLM-4.5 API prices
| Route | Input $/M | Output $/M | Cached input $/M | Checked |
| --- | --- | --- | --- | --- |
| [openrouter](https://openrouter.ai/z-ai/glm-4.5) | $0.60 | $2.20 | $0.11 | 2026-10-10 |
| [zai](https://docs.z.ai/guides/overview/pricing) | $0.60 | $2.20 | $0.11 | 2026-10-10 |

[All Z.ai (Zhipu) API prices →](https://noometry.com/llm-pricing/zai) [Estimate your cost →](https://noometry.com/tools/cost-calculator)

## Compare GLM-4.5

-   [GLM-4.5 vs Solar Pro4](https://noometry.com/compare/glm-4-5-vs-solar-pro4)
-   [GLM-4.5 vs Qwen3.5 35B-A3B](https://noometry.com/compare/glm-4-5-vs-qwen3-5-35b-a3b)
-   [GLM-4.5 vs Longcat Flash Chat](https://noometry.com/compare/glm-4-5-vs-longcat-flash-chat)
-   [GLM-4.5 vs GLM-4.7](https://noometry.com/compare/glm-4-5-vs-glm-4-7)
-   [GLM-4.5 vs Qwen3.5 122B-A10B](https://noometry.com/compare/glm-4-5-vs-qwen3-5-122b-a10b)
-   [GLM-4.5 vs GPT-5.4 nano](https://noometry.com/compare/glm-4-5-vs-gpt-5-4-nano)
-   [GLM-4.5 vs GPT-6 Astra](https://noometry.com/compare/glm-4-5-vs-gpt-6-astra)
-   [GLM-4.5 vs Claude Fable 5.1](https://noometry.com/compare/claude-fable-5-1-vs-glm-4-5)
-   [GLM-4.5 vs Gemini 3.8 Flash](https://noometry.com/compare/gemini-3-8-flash-vs-glm-4-5)
-   [GLM-4.5 vs Kimi K3](https://noometry.com/compare/glm-4-5-vs-kimi-k3)
-   [GLM-4.5 vs Grok 4.6](https://noometry.com/compare/glm-4-5-vs-grok-4-6)
-   [GLM-4.5 vs Qwen3.8 Max](https://noometry.com/compare/glm-4-5-vs-qwen3-8-max)
-   [GLM-4.5 vs Muse Spark 1.3](https://noometry.com/compare/glm-4-5-vs-muse-spark-1-3)
-   [GLM-4.5 vs DeepSeek V4 Pro](https://noometry.com/compare/deepseek-v4-pro-vs-glm-4-5)

## Other Z.ai (Zhipu) models

-   [GLM-5.3](https://noometry.com/models/glm-5-3)54.8
-   [GLM-5.3-Flash](https://noometry.com/models/glm-5-3-flash)51.8
-   [GLM-5.2](https://noometry.com/models/glm-5-2)51.1
-   [GLM-5.1](https://noometry.com/models/glm-5-1)47.8
-   [GLM-5](https://noometry.com/models/glm-5)46.1
-   [GLM-5V-Turbo](https://noometry.com/models/glm-5v-turbo)43.8
-   [GLM-4.7](https://noometry.com/models/glm-4-7)42.0
-   [GLM-4.6](https://noometry.com/models/glm-4-6)41.4

## Frequently asked questions

### How good is GLM-4.5?

GLM-4.5 by Z.ai (Zhipu) ranks 122nd of 354 ranked models on the Noometry Index as of October 2026, with a score of 42.0. Its strongest category is multilingual, where it ranks 77th. API pricing starts at $0.60 per million input tokens and $2.20 per million output tokens, with a 131K-token context window.

### How much does GLM-4.5 cost?

GLM-4.5 costs $0.60 per million input tokens and $2.20 per million output tokens on Z.ai (Zhipu)'s own API, with cached input at $0.11.

### What is GLM-4.5's context window?

GLM-4.5 accepts up to 131K tokens of input and can write up to 98K tokens in one response.

### Is GLM-4.5 open source?

Yes. GLM-4.5's weights are downloadable from Hugging Face (zai-org/GLM-4.5); check the license for commercial terms.

### How fast is GLM-4.5?

GLM-4.5 generated about 32 output tokens per second in the Kagi LLM Benchmark's timed runs. Speed varies by provider, load and reasoning effort.

### What are GLM-4.5's strengths and weaknesses?

Relative to other ranked models, GLM-4.5 places best in multilingual, reasoning, instruction following and lowest in long context, knowledge, writing & preference.

### What is GLM-4.5 best at?

Its best category is multilingual, where it ranks 77th on Noometry.

### Cite this page

Noometry. (2026). GLM-4.5 benchmarks and pricing. Retrieved October 10, 2026, from https://noometry.com/models/glm-4-5

Quote Noometry with a link back to this page. It is also available in [Markdown](https://noometry.com/md/models/glm-4-5.md).