Z.ai (Zhipu), open weights

# GLM-5

> GLM-5 by Z.ai (Zhipu), released February 2026. Ranked #66 of 354 with a Noometry Index of 46.1. API: $1 in / $3.20 out per M tokens. 205K context. Scores, sources and comparisons.
- Canonical page: https://noometry.com/models/glm-5
- Last updated: 2026-10-10
- Title: GLM-5 Benchmarks, Price & Rank (October 2026) | Noometry

GLM-5 by Z.ai (Zhipu) ranks 66th of 354 ranked models on the Noometry Index as of October 2026, with a score of 46.1. Its strongest category is writing & preference, where it ranks 38th. API pricing starts at $1 per million input tokens and $3.20 per million output tokens, with a 205K-token context window.

Last verified October 10, 2026

## Specifications

- **Noometry rank:** #66 of 354
- **Index score:** 46.1
- **Evidence:** Confirmed 45 results
- **Provider:** [Z.ai (Zhipu)](https://noometry.com/providers/zai)
- **Released:** February 11, 2026
- **Weights:** Open weights
- **Reasoning:** Yes
- **Context window:** 205K
- **Max output:** 131K
- **Input price:** $1 / M
- **Output price:** $3.20 / M
- **Blended price:** $1.55 / M
- **Output speed:** 23 tokens/s [Kagi](https://help.kagi.com/kagi/ai/llm-benchmark.html)
- **Value:** #134 of 219
- **Knowledge cutoff:** December 2025
- **Input:** text
- **Hugging Face:** [zai-org/GLM-5](https://huggingface.co/zai-org/GLM-5)

## Category scores

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

GLM-5 category scores

1.  Coding 49.0
2.  Agentic & Tool Use 31.1
3.  Reasoning 27.6
4.  Math 46.4
5.  Knowledge 52.3
6.  Multilingual 53.7
7.  Instruction Following 75.2
8.  Long Context 44.7
9.  Writing & Preference 66.0
10.  020406080

GLM-5 category ranks
| Category | Score | Rank | Results |
| --- | --- | --- | --- |
| [Coding](https://noometry.com/best/coding) | 49.0 | #52 | 6 |
| [Agentic & Tool Use](https://noometry.com/best/agentic) | 31.1 | #71 | 5 |
| [Reasoning](https://noometry.com/best/reasoning) | 27.6 | #116 | 7 |
| [Math](https://noometry.com/best/math) | 46.4 | #71 | 3 |
| [Knowledge](https://noometry.com/best/knowledge) | 52.3 | #64 | 3 |
| [Multilingual](https://noometry.com/best/multilingual) | 53.7 | #58 | 1 |
| [Instruction Following](https://noometry.com/best/instruction-following) | 75.2 | #67 | 1 |
| [Long Context](https://noometry.com/best/long-context) | 44.7 | #60 | 2 |
| [Writing & Preference](https://noometry.com/best/writing) | 66.0 | #38 | 4 |

## Strengths and weaknesses

Categories where GLM-5 places highest and lowest among the models ranked in each, with its score against that category's median.

### Strongest categories

GLM-5: strongest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Writing & Preference](https://noometry.com/best/writing) | 66.0 | +12.2 | #38 of 312, top 13% |
| [Coding](https://noometry.com/best/coding) | 49.0 | +10.3 | #52 of 340, top 16% |
| [Multilingual](https://noometry.com/best/multilingual) | 53.7 | +6.3 | #58 of 297, top 20% |

### Weakest categories

GLM-5: weakest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Agentic & Tool Use](https://noometry.com/best/agentic) | 31.1 | +0.7 | #71 of 154, top 47% |
| [Reasoning](https://noometry.com/best/reasoning) | 27.6 | +4.0 | #116 of 350, top 34% |
| [Instruction Following](https://noometry.com/best/instruction-following) | 75.2 | +3.9 | #67 of 305, top 22% |

## Closest competitors

The models ranked just above and below GLM-5. When scores are this close, price and speed are often the better way to choose.

Models ranked closest to GLM-5
| Model | Rank | Score | Blended $/M | Speed |  |
| --- | --- | --- | --- | --- | --- |
| [Qwen3.6 Plus](https://noometry.com/models/qwen3-6-plus) | #62 | 47.5 | $1.13 | — | [Compare](https://noometry.com/compare/glm-5-vs-qwen3-6-plus) |
| [Inkling-Small](https://noometry.com/models/inkling-small) | #63 | 46.5 | $0.64 | — | [Compare](https://noometry.com/compare/glm-5-vs-inkling-small) |
| [GPT-5 Pro](https://noometry.com/models/gpt-5-pro) | #64 | 46.4 | $41.25 | 5 | [Compare](https://noometry.com/compare/glm-5-vs-gpt-5-pro) |
| [Grok 4.20 Multi-Agent](https://noometry.com/models/grok-4-20-multi-agent) | #65 | 46.2 | $1.56 | — | [Compare](https://noometry.com/compare/glm-5-vs-grok-4-20-multi-agent) |
| [Qwen3.5 397B-A17B](https://noometry.com/models/qwen3-5-397b-a17b) | #67 | 46.0 | $1.35 | 9 | [Compare](https://noometry.com/compare/glm-5-vs-qwen3-5-397b-a17b) |
| [Qwen3.8 27B](https://noometry.com/models/qwen3-8-27b) | #68 | 46.0 | $1.11 | — | [Compare](https://noometry.com/compare/glm-5-vs-qwen3-8-27b) |
| [GPT-5.3 Codex](https://noometry.com/models/gpt-5-3-codex) | #69 | 45.8 | $4.81 | — | [Compare](https://noometry.com/compare/glm-5-vs-gpt-5-3-codex) |
| [Kimi K2 Thinking Turbo](https://noometry.com/models/kimi-k2-thinking-turbo) | #70 | 45.8 | — | — | [Compare](https://noometry.com/compare/glm-5-vs-kimi-k2-thinking-turbo) |

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-5 Coding benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [SWE-bench Verified](https://noometry.com/benchmarks/swe-bench-verified) | 72.1% | #22 of 32, top 69% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2026-02-15 |
| [SWE-bench Verified (bash only)](https://noometry.com/benchmarks/swe-bench-bash-only) | 72.8% | #6 of 39, top 16% | high | [SWE-bench](https://www.swebench.com/) | 2026-02-17 |
| [LMArena WebDev](https://noometry.com/benchmarks/arena-webdev) | 1434 | #63 of 113, top 56% |  | [LMArena](https://lmarena.ai/leaderboard/webdev) | 2026-10-08 |
| [SWE-bench Multilingual](https://noometry.com/benchmarks/swe-bench-multilingual) | 69.7% | #4 of 13, top 31% |  | [SWE-bench](https://www.swebench.com/) | 2026-02-13 |
| [WeirdML](https://noometry.com/benchmarks/weirdml) | 48.2% | #50 of 119, top 43% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Coding](https://noometry.com/benchmarks/arena-coding) | 1461 | #69 of 294, top 24% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [ALE-Bench](https://noometry.com/benchmarks/ale-bench) | 765.62 | #63 of 105, top 60% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Agentic & Tool Use

GLM-5 Agentic & Tool Use benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [Terminal-Bench](https://noometry.com/benchmarks/terminal-bench) | 52.4% | #16 of 41, top 40% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [τ²-bench Airline](https://noometry.com/benchmarks/tau2-airline) | 82.5% | #4 of 7, top 58% | enabled | [τ²-bench](https://taubench.com/) | 2026-03-02 |
| [τ²-bench Banking](https://noometry.com/benchmarks/tau2-banking) | 9.8% | #25 of 26, top 97% | enabled | [τ²-bench](https://taubench.com/) | 2026-03-02 |
| [τ²-bench Retail](https://noometry.com/benchmarks/tau2-retail) | 73.7% | #6 of 7, top 86% | enabled | [τ²-bench](https://taubench.com/) | 2026-03-02 |
| [τ²-bench Telecom](https://noometry.com/benchmarks/tau2-telecom) | 86.8% | #6 of 7, top 86% | enabled | [τ²-bench](https://taubench.com/) | 2026-03-02 |
| [Vending-Bench 2](https://noometry.com/benchmarks/vending-bench-2) | 4,432 | #33 of 60, top 56% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Reasoning

GLM-5 Reasoning benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 4.9% | #57 of 83, top 69% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [SimpleBench](https://noometry.com/benchmarks/simplebench) | 53.2% | #36 of 77, top 47% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Kagi LLM Benchmark](https://noometry.com/benchmarks/kagi-reasoning) | 75% | #12 of 99, top 13% |  | [Kagi LLM Benchmark](https://help.kagi.com/kagi/ai/llm-benchmark.html) |  |
| [Kagi LLM Benchmark](https://noometry.com/benchmarks/kagi-reasoning) | 51.7% |  |  | [Kagi LLM Benchmark](https://help.kagi.com/kagi/ai/llm-benchmark.html) |  |
| [NYT Connections (extended)](https://noometry.com/benchmarks/nyt-connections) | 74.8% | #43 of 91, top 48% |  | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 44.7% | #59 of 83, top 72% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Chess Puzzles](https://noometry.com/benchmarks/chess-puzzles) | 10% | #82 of 129, top 64% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2026-02-12 |
| [LMArena Hard Prompts](https://noometry.com/benchmarks/arena-hard-prompts) | 1452 | #58 of 297, top 20% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [Epoch Capabilities Index](https://noometry.com/benchmarks/epoch-capabilities-index) | 145.83 | #78 of 213, top 37% |  | [Epoch AI](https://epoch.ai/eci) | 2026-02-11 |
| [ForecastBench](https://noometry.com/benchmarks/forecastbench) | 61 | #18 of 72, top 25% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Math

GLM-5 Math benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [MathArena Final-Answer Competitions](https://noometry.com/benchmarks/matharena) | 65.7% | #20 of 29, top 69% |  | [MathArena](https://matharena.ai/) |  |
| [OTIS Mock AIME 2024-2025](https://noometry.com/benchmarks/otis-mock-aime) | 80% | #81 of 173, top 47% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2026-02-12 |
| [LMArena Math](https://noometry.com/benchmarks/arena-math) | 1440 | #73 of 285, top 26% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [FrontierMath (Feb 2025 set)](https://noometry.com/benchmarks/frontiermath-2025-02) | 16.4% | #33 of 68, top 49% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2026-02-19 |
| [FrontierMath Tier 4 (v1)](https://noometry.com/benchmarks/frontiermath-tier-4-v1) | 2.1% | #38 of 55, top 70% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2026-02-19 |

### Knowledge

GLM-5 Knowledge benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [GPQA Diamond](https://noometry.com/benchmarks/gpqa-diamond) | 87.8% | #51 of 186, top 28% |  | [Epoch AI](https://epoch.ai/benchmarks) | 2026-02-12 |
| [Vectara Hallucination Rate](https://noometry.com/benchmarks/vectara-hallucination) (lower is better) | 10.1% | #55 of 96, top 58% |  | [Vectara Hallucination Leaderboard](https://github.com/vectara/hallucination-leaderboard) |  |
| [LMArena Expert](https://noometry.com/benchmarks/arena-expert) | 1454 | #67 of 273, top 25% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Multilingual

GLM-5 Multilingual benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Non-English](https://noometry.com/benchmarks/arena-non-english) | 1430 | #58 of 297, top 20% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Chinese](https://noometry.com/benchmarks/arena-chinese) | 1511 | #35 of 285, top 13% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena French](https://noometry.com/benchmarks/arena-french) | 1455 | #60 of 223, top 27% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena German](https://noometry.com/benchmarks/arena-german) | 1445 | #52 of 231, top 23% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Japanese](https://noometry.com/benchmarks/arena-japanese) | 1416 | #43 of 211, top 21% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Korean](https://noometry.com/benchmarks/arena-korean) | 1423 | #35 of 213, top 17% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Russian](https://noometry.com/benchmarks/arena-russian) | 1436 | #56 of 283, top 20% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Spanish](https://noometry.com/benchmarks/arena-spanish) | 1454 | #48 of 226, top 22% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Instruction Following

GLM-5 Instruction Following benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Instruction Following](https://noometry.com/benchmarks/arena-instruction-following) | 1428 | #63 of 298, top 22% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Long Context

GLM-5 Long Context benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [CL-bench](https://noometry.com/benchmarks/cl-bench) | 18.7% | #10 of 19, top 53% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Longer Query](https://noometry.com/benchmarks/arena-longer-query) | 1446 | #57 of 291, top 20% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Writing & Preference

GLM-5 Writing & Preference benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Text](https://noometry.com/benchmarks/arena-text) | 1446 | #51 of 297, top 18% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Creative Writing](https://noometry.com/benchmarks/arena-creative-writing) | 1439 | #39 of 295, top 14% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [EQ-Bench Creative Writing](https://noometry.com/benchmarks/eqbench-creative-writing) | 1601 | #42 of 115, top 37% |  | [EQ-Bench](https://eqbench.com/creative_writing.html) |  |
| [LMArena Multi-Turn](https://noometry.com/benchmarks/arena-multi-turn) | 1456 | #42 of 295, top 15% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

## API pricing by provider

GLM-5 API prices
| Route | Input $/M | Output $/M | Cached input $/M | Checked |
| --- | --- | --- | --- | --- |
| [bedrock](https://docs.aws.amazon.com/bedrock/latest/userguide/models-supported.html) | $1 | $3.20 | — | 2026-10-10 |
| [deepinfra](https://deepinfra.com/models) | $0.60 | $2.08 | $0.12 | 2026-10-10 |
| [openrouter](https://openrouter.ai/z-ai/glm-5) | $0.60 | $1.92 | $0.12 | 2026-10-10 |
| [together](https://docs.together.ai/docs/serverless-models) | $1 | $3.20 | — | 2026-10-10 |
| [vertex](https://cloud.google.com/vertex-ai/generative-ai/docs/models) | $1 | $3.20 | $0.10 | 2026-10-10 |
| [zai](https://docs.z.ai/guides/overview/pricing) | $1 | $3.20 | $0.20 | 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-5

-   [GLM-5 vs GLM-4.7](https://noometry.com/compare/glm-4-7-vs-glm-5)
-   [GLM-5 vs Grok 4.20 Multi-Agent](https://noometry.com/compare/glm-5-vs-grok-4-20-multi-agent)
-   [GLM-5 vs Qwen3.5 397B-A17B](https://noometry.com/compare/glm-5-vs-qwen3-5-397b-a17b)
-   [GLM-5 vs GPT-5 Pro](https://noometry.com/compare/glm-5-vs-gpt-5-pro)
-   [GLM-5 vs Qwen3.8 27B](https://noometry.com/compare/glm-5-vs-qwen3-8-27b)
-   [GLM-5 vs Inkling-Small](https://noometry.com/compare/glm-5-vs-inkling-small)
-   [GLM-5 vs GPT-5.3 Codex](https://noometry.com/compare/glm-5-vs-gpt-5-3-codex)
-   [GLM-5 vs GPT-6 Astra](https://noometry.com/compare/glm-5-vs-gpt-6-astra)
-   [GLM-5 vs Claude Fable 5.1](https://noometry.com/compare/claude-fable-5-1-vs-glm-5)
-   [GLM-5 vs Gemini 3.8 Flash](https://noometry.com/compare/gemini-3-8-flash-vs-glm-5)
-   [GLM-5 vs Kimi K3](https://noometry.com/compare/glm-5-vs-kimi-k3)
-   [GLM-5 vs Grok 4.6](https://noometry.com/compare/glm-5-vs-grok-4-6)
-   [GLM-5 vs Qwen3.8 Max](https://noometry.com/compare/glm-5-vs-qwen3-8-max)
-   [GLM-5 vs Muse Spark 1.3](https://noometry.com/compare/glm-5-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-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
-   [GLM-4.6](https://noometry.com/models/glm-4-6)41.4

## Frequently asked questions

### How good is GLM-5?

GLM-5 by Z.ai (Zhipu) ranks 66th of 354 ranked models on the Noometry Index as of October 2026, with a score of 46.1. Its strongest category is writing & preference, where it ranks 38th. API pricing starts at $1 per million input tokens and $3.20 per million output tokens, with a 205K-token context window.

### How much does GLM-5 cost?

GLM-5 costs $1 per million input tokens and $3.20 per million output tokens on Z.ai (Zhipu)'s own API, with cached input at $0.20.

### What is GLM-5's context window?

GLM-5 accepts up to 205K tokens of input and can write up to 131K tokens in one response.

### Is GLM-5 open source?

Yes. GLM-5's weights are downloadable from Hugging Face (zai-org/GLM-5); check the license for commercial terms.

### How fast is GLM-5?

GLM-5 generated about 23 output tokens per second in the Kagi LLM Benchmark's timed runs. Speed varies by provider, load and reasoning effort.

### What are GLM-5's strengths and weaknesses?

Relative to other ranked models, GLM-5 places best in writing & preference, coding, multilingual and lowest in agentic & tool use, reasoning, instruction following.

### What is GLM-5 best at?

Its best category is writing & preference, where it ranks 38th on Noometry.

### Cite this page

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

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