Z.ai (Zhipu), open weights

# GLM-4.5-Air

> GLM-4.5-Air by Z.ai (Zhipu), released July 2025. Ranked #177 of 354 with a Noometry Index of 38.9. API: $0.20 in / $1.10 out per M tokens. 131K context. Scores, sources and comparisons.
- Canonical page: https://noometry.com/models/glm-4-5-air
- Last updated: 2026-10-10
- Title: GLM-4.5-Air Benchmarks, Price & Rank (October 2026)

GLM-4.5-Air by Z.ai (Zhipu) ranks 177th of 354 ranked models on the Noometry Index as of October 2026, with a score of 38.9. Its strongest category is long context, where it ranks 135th. API pricing starts at $0.20 per million input tokens and $1.10 per million output tokens, with a 131K-token context window.

Last verified October 10, 2026

## Specifications

- **Noometry rank:** #177 of 354
- **Index score:** 38.9
- **Evidence:** Confirmed 27 results
- **Provider:** [Z.ai (Zhipu)](https://noometry.com/providers/zai)
- **Released:** July 20, 2025
- **Weights:** Open weights
- **Reasoning:** Yes
- **Context window:** 131K
- **Max output:** 98K
- **Input price:** $0.20 / M
- **Output price:** $1.10 / M
- **Blended price:** $0.43 / M
- **Output speed:** 160 tokens/s [Kagi](https://help.kagi.com/kagi/ai/llm-benchmark.html)
- **Value:** #70 of 219
- **Knowledge cutoff:** April 2025
- **Input:** text
- **Hugging Face:** [zai-org/GLM-4.5-Air](https://huggingface.co/zai-org/GLM-4.5-Air)

## Category scores

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

GLM-4.5-Air category scores

1.  Coding 33.3
2.  Reasoning 24.1
3.  Math 36.2
4.  Knowledge 35.0
5.  Multilingual 49.1
6.  Instruction Following 69.6
7.  Long Context 41.6
8.  Writing & Preference 55.9
9.  020406080

GLM-4.5-Air category ranks
| Category | Score | Rank | Results |
| --- | --- | --- | --- |
| [Coding](https://noometry.com/best/coding) | 33.3 | #259 | 2 |
| [Reasoning](https://noometry.com/best/reasoning) | 24.1 | #166 | 2 |
| [Math](https://noometry.com/best/math) | 36.2 | #170 | 2 |
| [Knowledge](https://noometry.com/best/knowledge) | 35.0 | #191 | 5 |
| [Multilingual](https://noometry.com/best/multilingual) | 49.1 | #135 | 1 |
| [Instruction Following](https://noometry.com/best/instruction-following) | 69.6 | #171 | 2 |
| [Long Context](https://noometry.com/best/long-context) | 41.6 | #135 | 1 |
| [Writing & Preference](https://noometry.com/best/writing) | 55.9 | #139 | 4 |

## Strengths and weaknesses

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

### Strongest categories

GLM-4.5-Air: strongest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Writing & Preference](https://noometry.com/best/writing) | 55.9 | +2.2 | #139 of 312, top 45% |
| [Multilingual](https://noometry.com/best/multilingual) | 49.1 | +1.7 | #135 of 297, top 46% |
| [Long Context](https://noometry.com/best/long-context) | 41.6 | +0.7 | #135 of 296, top 46% |

### Weakest categories

GLM-4.5-Air: weakest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Coding](https://noometry.com/best/coding) | 33.3 | −5.4 | #259 of 340, top 77% |
| [Knowledge](https://noometry.com/best/knowledge) | 35.0 | −2.4 | #191 of 314, top 61% |
| [Instruction Following](https://noometry.com/best/instruction-following) | 69.6 | −1.7 | #171 of 305, top 57% |

## Closest competitors

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

Models ranked closest to GLM-4.5-Air
| Model | Rank | Score | Blended $/M | Speed |  |
| --- | --- | --- | --- | --- | --- |
| [Gemini 2.0 Pro](https://noometry.com/models/gemini-2-0-pro) | #173 | 39.1 | — | — | [Compare](https://noometry.com/compare/gemini-2-0-pro-vs-glm-4-5-air) |
| [Molmo 2 8b](https://noometry.com/models/molmo-2-8b) | #174 | 39.1 | — | — | [Compare](https://noometry.com/compare/glm-4-5-air-vs-molmo-2-8b) |
| [Mercury 2](https://noometry.com/models/mercury-2) | #175 | 39.1 | $0.38 | — | [Compare](https://noometry.com/compare/glm-4-5-air-vs-mercury-2) |
| [Mistral Large 3](https://noometry.com/models/mistral-large-3) | #176 | 39.1 | $0.38 | 7 | [Compare](https://noometry.com/compare/glm-4-5-air-vs-mistral-large-3) |
| [MiniMax-M2.1](https://noometry.com/models/minimax-m2-1) | #178 | 38.9 | $0.52 | — | [Compare](https://noometry.com/compare/glm-4-5-air-vs-minimax-m2-1) |
| [Qwen3-30B-A3B](https://noometry.com/models/qwen3-30b-a3b) | #179 | 38.9 | $0.21 | 42 | [Compare](https://noometry.com/compare/glm-4-5-air-vs-qwen3-30b-a3b) |
| [GLM-4.7-Flash](https://noometry.com/models/glm-4-7-flash) | #180 | 38.8 | $0.15 | — | [Compare](https://noometry.com/compare/glm-4-5-air-vs-glm-4-7-flash) |
| [Qwen2.5 Plus 1127](https://noometry.com/models/qwen2-5-plus) | #181 | 38.8 | — | — | [Compare](https://noometry.com/compare/glm-4-5-air-vs-qwen2-5-plus) |

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-Air Coding benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [GSO](https://noometry.com/benchmarks/gso-bench) | 2.9% | #29 of 31, top 94% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Coding](https://noometry.com/benchmarks/arena-coding) | 1397 | #138 of 294, top 47% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Reasoning

GLM-4.5-Air Reasoning benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [Kagi LLM Benchmark](https://noometry.com/benchmarks/kagi-reasoning) | 43% | #73 of 99, top 74% |  | [Kagi LLM Benchmark](https://help.kagi.com/kagi/ai/llm-benchmark.html) |  |
| [LMArena Hard Prompts](https://noometry.com/benchmarks/arena-hard-prompts) | 1379 | #139 of 297, top 47% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [ForecastBench](https://noometry.com/benchmarks/forecastbench) | 59.2 | #40 of 72, top 56% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Math

GLM-4.5-Air Math benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [Omni-MATH](https://noometry.com/benchmarks/omni-math) | 39.1% | #27 of 57, top 48% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [LMArena Math](https://noometry.com/benchmarks/arena-math) | 1396 | #129 of 285, top 46% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Knowledge

GLM-4.5-Air Knowledge benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [Humanity's Last Exam](https://noometry.com/benchmarks/hle) | 8.1% | #27 of 41, top 66% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [MMLU-Pro](https://noometry.com/benchmarks/mmlu-pro) | 76.2% | #26 of 58, top 45% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [Vectara Hallucination Rate](https://noometry.com/benchmarks/vectara-hallucination) (lower is better) | 9.3% | #44 of 96, top 46% |  | [Vectara Hallucination Leaderboard](https://github.com/vectara/hallucination-leaderboard) |  |
| [GPQA (HELM)](https://noometry.com/benchmarks/helm-gpqa) | 59.4% | #24 of 57, top 43% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [LMArena Expert](https://noometry.com/benchmarks/arena-expert) | 1370 | #142 of 273, top 53% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Multilingual

GLM-4.5-Air Multilingual benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Non-English](https://noometry.com/benchmarks/arena-non-english) | 1366 | #135 of 297, top 46% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Chinese](https://noometry.com/benchmarks/arena-chinese) | 1426 | #122 of 285, top 43% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena French](https://noometry.com/benchmarks/arena-french) | 1399 | #118 of 223, top 53% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena German](https://noometry.com/benchmarks/arena-german) | 1377 | #112 of 231, top 49% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Japanese](https://noometry.com/benchmarks/arena-japanese) | 1348 | #99 of 211, top 47% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Korean](https://noometry.com/benchmarks/arena-korean) | 1308 | #126 of 213, top 60% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Russian](https://noometry.com/benchmarks/arena-russian) | 1373 | #134 of 283, top 48% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Spanish](https://noometry.com/benchmarks/arena-spanish) | 1386 | #123 of 226, top 55% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Instruction Following

GLM-4.5-Air Instruction Following benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [IFEval](https://noometry.com/benchmarks/ifeval) | 81.2% | #37 of 57, top 65% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [LMArena Instruction Following](https://noometry.com/benchmarks/arena-instruction-following) | 1354 | #142 of 298, top 48% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Long Context

GLM-4.5-Air Long Context benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Longer Query](https://noometry.com/benchmarks/arena-longer-query) | 1366 | #142 of 291, top 49% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Writing & Preference

GLM-4.5-Air Writing & Preference benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Text](https://noometry.com/benchmarks/arena-text) | 1384 | #133 of 297, top 45% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Creative Writing](https://noometry.com/benchmarks/arena-creative-writing) | 1343 | #135 of 295, top 46% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [WildBench](https://noometry.com/benchmarks/wildbench) | 78.9% | #37 of 57, top 65% |  | [HELM Capabilities](https://crfm.stanford.edu/helm/capabilities/latest/) |  |
| [LMArena Multi-Turn](https://noometry.com/benchmarks/arena-multi-turn) | 1371 | #138 of 295, top 47% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

## API pricing by provider

GLM-4.5-Air API prices
| Route | Input $/M | Output $/M | Cached input $/M | Checked |
| --- | --- | --- | --- | --- |
| [openrouter](https://openrouter.ai/z-ai/glm-4.5-air) | $0.13 | $0.85 | $0.025 | 2026-10-10 |
| [zai](https://docs.z.ai/guides/overview/pricing) | $0.20 | $1.10 | $0.03 | 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-Air

-   [GLM-4.5-Air vs Mistral Large 3](https://noometry.com/compare/glm-4-5-air-vs-mistral-large-3)
-   [GLM-4.5-Air vs MiniMax-M2.1](https://noometry.com/compare/glm-4-5-air-vs-minimax-m2-1)
-   [GLM-4.5-Air vs Mercury 2](https://noometry.com/compare/glm-4-5-air-vs-mercury-2)
-   [GLM-4.5-Air vs Qwen3-30B-A3B](https://noometry.com/compare/glm-4-5-air-vs-qwen3-30b-a3b)
-   [GLM-4.5-Air vs Molmo 2 8b](https://noometry.com/compare/glm-4-5-air-vs-molmo-2-8b)
-   [GLM-4.5-Air vs GLM-4.7-Flash](https://noometry.com/compare/glm-4-5-air-vs-glm-4-7-flash)
-   [GLM-4.5-Air vs GPT-6 Astra](https://noometry.com/compare/glm-4-5-air-vs-gpt-6-astra)
-   [GLM-4.5-Air vs Claude Fable 5.1](https://noometry.com/compare/claude-fable-5-1-vs-glm-4-5-air)
-   [GLM-4.5-Air vs Gemini 3.8 Flash](https://noometry.com/compare/gemini-3-8-flash-vs-glm-4-5-air)
-   [GLM-4.5-Air vs Kimi K3](https://noometry.com/compare/glm-4-5-air-vs-kimi-k3)
-   [GLM-4.5-Air vs Grok 4.6](https://noometry.com/compare/glm-4-5-air-vs-grok-4-6)
-   [GLM-4.5-Air vs Qwen3.8 Max](https://noometry.com/compare/glm-4-5-air-vs-qwen3-8-max)
-   [GLM-4.5-Air vs Muse Spark 1.3](https://noometry.com/compare/glm-4-5-air-vs-muse-spark-1-3)
-   [GLM-4.5-Air vs DeepSeek V4 Pro](https://noometry.com/compare/deepseek-v4-pro-vs-glm-4-5-air)

## 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-4.5](https://noometry.com/models/glm-4-5)42.0
-   [GLM-4.7](https://noometry.com/models/glm-4-7)42.0

## Frequently asked questions

### How good is GLM-4.5-Air?

GLM-4.5-Air by Z.ai (Zhipu) ranks 177th of 354 ranked models on the Noometry Index as of October 2026, with a score of 38.9. Its strongest category is long context, where it ranks 135th. API pricing starts at $0.20 per million input tokens and $1.10 per million output tokens, with a 131K-token context window.

### How much does GLM-4.5-Air cost?

GLM-4.5-Air costs $0.20 per million input tokens and $1.10 per million output tokens on Z.ai (Zhipu)'s own API, with cached input at $0.03.

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

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

### Is GLM-4.5-Air open source?

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

### How fast is GLM-4.5-Air?

GLM-4.5-Air generated about 160 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-Air's strengths and weaknesses?

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

### What is GLM-4.5-Air best at?

Its best category is long context, where it ranks 135th on Noometry.

### Cite this page

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

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