Z.ai (Zhipu), open weights

# GLM-4.7

> GLM-4.7 by Z.ai (Zhipu), released December 2025. Ranked #124 of 354 with a Noometry Index of 42.0. API: $0.60 in / $2.20 out per M tokens. 205K context. Scores, sources and comparisons.
- Canonical page: https://noometry.com/models/glm-4-7
- Last updated: 2026-10-10
- Title: GLM-4.7 Benchmarks, Price & Rank (October 2026) | Noometry

GLM-4.7 by Z.ai (Zhipu) ranks 124th of 354 ranked models on the Noometry Index as of October 2026, with a score of 42.0. Its strongest category is coding, where it ranks 79th. API pricing starts at $0.60 per million input tokens and $2.20 per million output tokens, with a 205K-token context window.

Last verified October 10, 2026

## Specifications

- **Noometry rank:** #124 of 354
- **Index score:** 42.0
- **Evidence:** Confirmed 36 results
- **Provider:** [Z.ai (Zhipu)](https://noometry.com/providers/zai)
- **Released:** December 22, 2025
- **Weights:** Open weights
- **Reasoning:** Yes
- **Context window:** 205K
- **Max output:** 131K
- **Input price:** $0.60 / M
- **Output price:** $2.20 / M
- **Blended price:** $1 / M
- **Output speed:** Not measured
- **Value:** #110 of 219
- **Knowledge cutoff:** April 2025
- **Input:** text
- **Hugging Face:** [zai-org/GLM-4.7](https://huggingface.co/zai-org/GLM-4.7)

## Category scores

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

GLM-4.7 category scores

1.  Coding 44.0
2.  Agentic & Tool Use 26.5
3.  Reasoning 24.3
4.  Math 38.6
5.  Knowledge 47.0
6.  Multilingual 52.8
7.  Instruction Following 74.4
8.  Long Context 42.8
9.  Writing & Preference 60.9
10.  020406080

GLM-4.7 category ranks
| Category | Score | Rank | Results |
| --- | --- | --- | --- |
| [Coding](https://noometry.com/best/coding) | 44.0 | #79 | 3 |
| [Agentic & Tool Use](https://noometry.com/best/agentic) | 26.5 | #103 | 1 |
| [Reasoning](https://noometry.com/best/reasoning) | 24.3 | #164 | 4 |
| [Math](https://noometry.com/best/math) | 38.6 | #135 | 3 |
| [Knowledge](https://noometry.com/best/knowledge) | 47.0 | #80 | 4 |
| [Multilingual](https://noometry.com/best/multilingual) | 52.8 | #79 | 1 |
| [Instruction Following](https://noometry.com/best/instruction-following) | 74.4 | #95 | 1 |
| [Long Context](https://noometry.com/best/long-context) | 42.8 | #116 | 3 |
| [Writing & Preference](https://noometry.com/best/writing) | 60.9 | #93 | 4 |

## Strengths and weaknesses

Categories where GLM-4.7 places highest and lowest among the models ranked in each, with its score against that category's median.

### Strongest categories

GLM-4.7: strongest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Coding](https://noometry.com/best/coding) | 44.0 | +5.2 | #79 of 340, top 24% |
| [Knowledge](https://noometry.com/best/knowledge) | 47.0 | +9.7 | #80 of 314, top 26% |
| [Multilingual](https://noometry.com/best/multilingual) | 52.8 | +5.4 | #79 of 297, top 27% |

### Weakest categories

GLM-4.7: weakest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Agentic & Tool Use](https://noometry.com/best/agentic) | 26.5 | −3.8 | #103 of 154, top 67% |
| [Reasoning](https://noometry.com/best/reasoning) | 24.3 | +0.7 | #164 of 350, top 47% |
| [Math](https://noometry.com/best/math) | 38.6 | +2.0 | #135 of 327, top 42% |

## Closest competitors

The models ranked just above and below GLM-4.7. When scores are this close, price and speed are often the better way to choose.

Models ranked closest to GLM-4.7
| Model | Rank | Score | Blended $/M | Speed |  |
| --- | --- | --- | --- | --- | --- |
| [Longcat Flash Chat](https://noometry.com/models/longcat-flash-chat) | #120 | 42.1 | — | 69 | [Compare](https://noometry.com/compare/glm-4-7-vs-longcat-flash-chat) |
| [Solar Pro4](https://noometry.com/models/solar-pro4) | #121 | 42.1 | $0.52 | — | [Compare](https://noometry.com/compare/glm-4-7-vs-solar-pro4) |
| [GLM-4.5](https://noometry.com/models/glm-4-5) | #122 | 42.0 | $1 | 32 | [Compare](https://noometry.com/compare/glm-4-5-vs-glm-4-7) |
| [Qwen3.5 35B-A3B](https://noometry.com/models/qwen3-5-35b-a3b) | #123 | 42.0 | $0.69 | — | [Compare](https://noometry.com/compare/glm-4-7-vs-qwen3-5-35b-a3b) |
| [GPT-5.4 nano](https://noometry.com/models/gpt-5-4-nano) | #125 | 41.9 | $0.46 | 19 | [Compare](https://noometry.com/compare/glm-4-7-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-4-7) |
| [Qwen3.5 27B](https://noometry.com/models/qwen3-5-27b) | #127 | 41.9 | $0.82 | — | [Compare](https://noometry.com/compare/glm-4-7-vs-qwen3-5-27b) |
| [GPT-5 Mini](https://noometry.com/models/gpt-5-mini) | #128 | 41.8 | $0.69 | 3 | [Compare](https://noometry.com/compare/glm-4-7-vs-gpt-5-mini) |

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-4.7 Coding benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena WebDev](https://noometry.com/benchmarks/arena-webdev) | 1435 | #62 of 113, top 55% |  | [LMArena](https://lmarena.ai/leaderboard/webdev) | 2026-10-08 |
| [SciCode](https://noometry.com/benchmarks/scicode) | 45.1% | #59 of 121, top 49% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Coding](https://noometry.com/benchmarks/arena-coding) | 1454 | #79 of 294, top 27% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [ALE-Bench](https://noometry.com/benchmarks/ale-bench) | 399.48 | #90 of 105, top 86% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Agentic & Tool Use

GLM-4.7 Agentic & Tool Use benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [Terminal-Bench](https://noometry.com/benchmarks/terminal-bench) | 33.4% | #30 of 41, top 74% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Vending-Bench 2](https://noometry.com/benchmarks/vending-bench-2) | 2,377 | #42 of 60, top 70% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Reasoning

GLM-4.7 Reasoning benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [SimpleBench](https://noometry.com/benchmarks/simplebench) | 47.7% | #42 of 77, top 55% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [SimpleBench](https://noometry.com/benchmarks/simplebench) | 47.7% | #42 of 77, top 55% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CritPt](https://noometry.com/benchmarks/critpt) | 1.7% | #74 of 134, top 56% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Chess Puzzles](https://noometry.com/benchmarks/chess-puzzles) | 6% | #89 of 129, top 69% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2026-01-29 |
| [LMArena Hard Prompts](https://noometry.com/benchmarks/arena-hard-prompts) | 1443 | #78 of 297, top 27% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [Epoch Capabilities Index](https://noometry.com/benchmarks/epoch-capabilities-index) | 143.51 | #91 of 213, top 43% |  | [Epoch AI](https://epoch.ai/eci) | 2025-12-22 |

### Math

GLM-4.7 Math benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [OTIS Mock AIME 2024-2025](https://noometry.com/benchmarks/otis-mock-aime) | 83.3% | #75 of 173, top 44% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2026-01-29 |
| [ProofBench](https://noometry.com/benchmarks/proofbench) | 6% | #67 of 77, top 88% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Math](https://noometry.com/benchmarks/arena-math) | 1423 | #97 of 285, top 35% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [FrontierMath (Feb 2025 set)](https://noometry.com/benchmarks/frontiermath-2025-02) | 2.4% | #53 of 68, top 78% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2026-01-30 |
| [FrontierMath Tier 4 (v1)](https://noometry.com/benchmarks/frontiermath-tier-4-v1) | 0% | #49 of 55, top 90% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2026-01-30 |

### Knowledge

GLM-4.7 Knowledge benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [GPQA Diamond](https://noometry.com/benchmarks/gpqa-diamond) | 83.3% | #71 of 186, top 39% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2026-01-29 |
| [SimpleQA Verified](https://noometry.com/benchmarks/simpleqa-verified) | 32.2% | #55 of 77, top 72% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-27 |
| [Vectara Hallucination Rate](https://noometry.com/benchmarks/vectara-hallucination) (lower is better) | 11.7% | #69 of 96, top 72% |  | [Vectara Hallucination Leaderboard](https://github.com/vectara/hallucination-leaderboard) |  |
| [LMArena Expert](https://noometry.com/benchmarks/arena-expert) | 1424 | #103 of 273, top 38% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Multilingual

GLM-4.7 Multilingual benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Non-English](https://noometry.com/benchmarks/arena-non-english) | 1417 | #79 of 297, top 27% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Chinese](https://noometry.com/benchmarks/arena-chinese) | 1495 | #50 of 285, top 18% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena French](https://noometry.com/benchmarks/arena-french) | 1432 | #89 of 223, top 40% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena German](https://noometry.com/benchmarks/arena-german) | 1424 | #72 of 231, top 32% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Japanese](https://noometry.com/benchmarks/arena-japanese) | 1439 | #30 of 211, top 15% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Korean](https://noometry.com/benchmarks/arena-korean) | 1399 | #55 of 213, top 26% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Russian](https://noometry.com/benchmarks/arena-russian) | 1423 | #77 of 283, top 28% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Spanish](https://noometry.com/benchmarks/arena-spanish) | 1434 | #74 of 226, top 33% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Instruction Following

GLM-4.7 Instruction Following benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Instruction Following](https://noometry.com/benchmarks/arena-instruction-following) | 1411 | #85 of 298, top 29% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Long Context

GLM-4.7 Long Context benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [CL-bench](https://noometry.com/benchmarks/cl-bench) | 15.9% | #14 of 19, top 74% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CL-bench Life](https://noometry.com/benchmarks/cl-bench-life) | 10.9% | #10 of 13, top 77% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Longer Query](https://noometry.com/benchmarks/arena-longer-query) | 1432 | #76 of 291, top 27% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Writing & Preference

GLM-4.7 Writing & Preference benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Text](https://noometry.com/benchmarks/arena-text) | 1435 | #72 of 297, top 25% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Creative Writing](https://noometry.com/benchmarks/arena-creative-writing) | 1401 | #81 of 295, top 28% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [EQ-Bench Creative Writing](https://noometry.com/benchmarks/eqbench-creative-writing) | 1413 | #68 of 115, top 60% |  | [EQ-Bench](https://eqbench.com/creative_writing.html) |  |
| [LMArena Multi-Turn](https://noometry.com/benchmarks/arena-multi-turn) | 1446 | #62 of 295, top 22% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

## API pricing by provider

GLM-4.7 API prices
| Route | Input $/M | Output $/M | Cached input $/M | Checked |
| --- | --- | --- | --- | --- |
| [bedrock](https://docs.aws.amazon.com/bedrock/latest/userguide/models-supported.html) | $0.60 | $2.20 | — | 2026-10-10 |
| [deepinfra](https://deepinfra.com/models) | $0.40 | $1.75 | $0.08 | 2026-10-10 |
| [openrouter](https://openrouter.ai/z-ai/glm-4.7) | $0.60 | $2.20 | $0.11 | 2026-10-10 |
| [vertex](https://cloud.google.com/vertex-ai/generative-ai/docs/models) | $0.60 | $2.20 | $0.06 | 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-4.7

-   [GLM-4.7 vs GLM-4.6V](https://noometry.com/compare/glm-4-6v-vs-glm-4-7)
-   [GLM-4.7 vs Qwen3.5 35B-A3B](https://noometry.com/compare/glm-4-7-vs-qwen3-5-35b-a3b)
-   [GLM-4.7 vs GPT-5.4 nano](https://noometry.com/compare/glm-4-7-vs-gpt-5-4-nano)
-   [GLM-4.7 vs GLM-4.5](https://noometry.com/compare/glm-4-5-vs-glm-4-7)
-   [GLM-4.7 vs Amazon Nova Experimental Chat 10 09](https://noometry.com/compare/amazon-nova-experimental-chat-10-09-vs-glm-4-7)
-   [GLM-4.7 vs Solar Pro4](https://noometry.com/compare/glm-4-7-vs-solar-pro4)
-   [GLM-4.7 vs Qwen3.5 27B](https://noometry.com/compare/glm-4-7-vs-qwen3-5-27b)
-   [GLM-4.7 vs GPT-6 Astra](https://noometry.com/compare/glm-4-7-vs-gpt-6-astra)
-   [GLM-4.7 vs Claude Fable 5.1](https://noometry.com/compare/claude-fable-5-1-vs-glm-4-7)
-   [GLM-4.7 vs Gemini 3.8 Flash](https://noometry.com/compare/gemini-3-8-flash-vs-glm-4-7)
-   [GLM-4.7 vs Kimi K3](https://noometry.com/compare/glm-4-7-vs-kimi-k3)
-   [GLM-4.7 vs Grok 4.6](https://noometry.com/compare/glm-4-7-vs-grok-4-6)
-   [GLM-4.7 vs Qwen3.8 Max](https://noometry.com/compare/glm-4-7-vs-qwen3-8-max)
-   [GLM-4.7 vs Muse Spark 1.3](https://noometry.com/compare/glm-4-7-vs-muse-spark-1-3)

## 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-4.6](https://noometry.com/models/glm-4-6)41.4

## Frequently asked questions

### How good is GLM-4.7?

GLM-4.7 by Z.ai (Zhipu) ranks 124th of 354 ranked models on the Noometry Index as of October 2026, with a score of 42.0. Its strongest category is coding, where it ranks 79th. API pricing starts at $0.60 per million input tokens and $2.20 per million output tokens, with a 205K-token context window.

### How much does GLM-4.7 cost?

GLM-4.7 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-4.7's context window?

GLM-4.7 accepts up to 205K tokens of input and can write up to 131K tokens in one response.

### Is GLM-4.7 open source?

Yes. GLM-4.7's weights are downloadable from Hugging Face (zai-org/GLM-4.7); check the license for commercial terms.

### What are GLM-4.7's strengths and weaknesses?

Relative to other ranked models, GLM-4.7 places best in coding, knowledge, multilingual and lowest in agentic & tool use, reasoning, math.

### What is GLM-4.7 best at?

Its best category is coding, where it ranks 79th on Noometry.

### Cite this page

Noometry. (2026). GLM-4.7 benchmarks and pricing. Retrieved October 10, 2026, from https://noometry.com/models/glm-4-7

Quote Noometry with a link back to this page. It is also available in [Markdown](https://noometry.com/md/models/glm-4-7.md).