Z.ai (Zhipu), open weights

# GLM-5.3

> GLM-5.3 by Z.ai (Zhipu), released August 2026. Ranked #26 of 354 with a Noometry Index of 54.8. API: $1.40 in / $4.40 out per M tokens. 1M context. Scores, sources and comparisons.
- Canonical page: https://noometry.com/models/glm-5-3
- Last updated: 2026-10-10
- Title: GLM-5.3 Benchmarks, Price & Rank (October 2026) | Noometry

GLM-5.3 by Z.ai (Zhipu) ranks 26th of 354 ranked models on the Noometry Index as of October 2026, with a score of 54.8. Its strongest category is writing & preference, where it ranks 6th. API pricing starts at $1.40 per million input tokens and $4.40 per million output tokens, with a 1M-token context window.

Last verified October 10, 2026

## Specifications

- **Noometry rank:** #26 of 354
- **Index score:** 54.8
- **Evidence:** Confirmed 42 results
- **Provider:** [Z.ai (Zhipu)](https://noometry.com/providers/zai)
- **Released:** August 14, 2026
- **Weights:** Open weights
- **Reasoning:** Yes
- **Context window:** 1M
- **Max output:** 131K
- **Input price:** $1.40 / M
- **Output price:** $4.40 / M
- **Blended price:** $2.15 / M
- **Output speed:** Not measured
- **Value:** #141 of 219
- **Knowledge cutoff:** Unknown
- **Input:** text
- **Hugging Face:** [zai-org/GLM-5.3](https://huggingface.co/zai-org/GLM-5.3)

## Category scores

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

GLM-5.3 category scores

1.  Coding 59.5
2.  Agentic & Tool Use 36.4
3.  Reasoning 46.1
4.  Math 62.3
5.  Knowledge 58.3
6.  Multilingual 55.7
7.  Instruction Following 77.5
8.  Long Context 45.4
9.  Writing & Preference 75.7
10.  020406080

GLM-5.3 category ranks
| Category | Score | Rank | Results |
| --- | --- | --- | --- |
| [Coding](https://noometry.com/best/coding) | 59.5 | #14 | 8 |
| [Agentic & Tool Use](https://noometry.com/best/agentic) | 36.4 | #38 | 1 |
| [Reasoning](https://noometry.com/best/reasoning) | 46.1 | #46 | 7 |
| [Math](https://noometry.com/best/math) | 62.3 | #33 | 5 |
| [Knowledge](https://noometry.com/best/knowledge) | 58.3 | #37 | 3 |
| [Multilingual](https://noometry.com/best/multilingual) | 55.7 | #28 | 1 |
| [Instruction Following](https://noometry.com/best/instruction-following) | 77.5 | #23 | 1 |
| [Long Context](https://noometry.com/best/long-context) | 45.4 | #41 | 1 |
| [Writing & Preference](https://noometry.com/best/writing) | 75.7 | #6 | 4 |

## Strengths and weaknesses

Categories where GLM-5.3 places highest and lowest among the models ranked in each, with its score against that category's median.

### Strongest categories

GLM-5.3: strongest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Writing & Preference](https://noometry.com/best/writing) | 75.7 | +21.9 | #6 of 312, top 2% |
| [Coding](https://noometry.com/best/coding) | 59.5 | +20.8 | #14 of 340, top 5% |
| [Instruction Following](https://noometry.com/best/instruction-following) | 77.5 | +6.2 | #23 of 305, top 8% |

### Weakest categories

GLM-5.3: weakest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Agentic & Tool Use](https://noometry.com/best/agentic) | 36.4 | +6.0 | #38 of 154, top 25% |
| [Long Context](https://noometry.com/best/long-context) | 45.4 | +4.5 | #41 of 296, top 14% |
| [Reasoning](https://noometry.com/best/reasoning) | 46.1 | +22.5 | #46 of 350, top 14% |

## Closest competitors

The models ranked just above and below GLM-5.3. When scores are this close, price and speed are often the better way to choose.

Models ranked closest to GLM-5.3
| Model | Rank | Score | Blended $/M | Speed |  |
| --- | --- | --- | --- | --- | --- |
| [Qwen3.8 Max](https://noometry.com/models/qwen3-8-max) | #22 | 56.8 | $3 | — | [Compare](https://noometry.com/compare/glm-5-3-vs-qwen3-8-max) |
| [Gemini 3.1 Pro Preview](https://noometry.com/models/gemini-3-1-pro-preview) | #23 | 56.7 | $4.50 | — | [Compare](https://noometry.com/compare/gemini-3-1-pro-preview-vs-glm-5-3) |
| [Gemini 4 Argon](https://noometry.com/models/gemini-4-argon) | #24 | 56.5 | — | — | [Compare](https://noometry.com/compare/gemini-4-argon-vs-glm-5-3) |
| [Grok 4.5](https://noometry.com/models/grok-4-5) | #25 | 55.0 | $3 | 4 | [Compare](https://noometry.com/compare/glm-5-3-vs-grok-4-5) |
| [Muse Spark 1.3](https://noometry.com/models/muse-spark-1-3) | #27 | 54.8 | $2 | — | [Compare](https://noometry.com/compare/glm-5-3-vs-muse-spark-1-3) |
| [Gemini 3 Pro](https://noometry.com/models/gemini-3-pro) | #28 | 54.8 | — | 1 | [Compare](https://noometry.com/compare/gemini-3-pro-vs-glm-5-3) |
| [Claude Sonnet 5](https://noometry.com/models/claude-sonnet-5) | #29 | 54.6 | $4 | — | [Compare](https://noometry.com/compare/claude-sonnet-5-vs-glm-5-3) |
| [GPT-5.6 Luna](https://noometry.com/models/gpt-5-6-luna) | #30 | 54.6 | $0.45 | 12 | [Compare](https://noometry.com/compare/glm-5-3-vs-gpt-5-6-luna) |

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-5.3 Coding benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [DeepSWE](https://noometry.com/benchmarks/deepswe) | 69% | #8 of 29, top 28% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [FrontierCode](https://noometry.com/benchmarks/frontiercode) | 40.1% | #21 of 37, top 57% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CursorBench](https://noometry.com/benchmarks/cursorbench) | 38% |  | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CursorBench](https://noometry.com/benchmarks/cursorbench) | 33.3% |  | low | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CursorBench](https://noometry.com/benchmarks/cursorbench) | 42.6% | #6 of 14, top 43% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena WebDev](https://noometry.com/benchmarks/arena-webdev) | 1622 | #17 of 113, top 16% |  | [LMArena](https://lmarena.ai/leaderboard/webdev) | 2026-10-08 |
| [FrontierSWE](https://noometry.com/benchmarks/frontierswe) | 30.2% | #9 of 18, top 50% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [SciCode](https://noometry.com/benchmarks/scicode) | 42% |  | low | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [SciCode](https://noometry.com/benchmarks/scicode) | 59% | #10 of 121, top 9% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [WeirdML](https://noometry.com/benchmarks/weirdml) | 75.4% | #15 of 119, top 13% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Coding](https://noometry.com/benchmarks/arena-coding) | 1496 | #25 of 294, top 9% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [ALE-Bench](https://noometry.com/benchmarks/ale-bench) | 1,317 | #23 of 105, top 22% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Agentic & Tool Use

GLM-5.3 Agentic & Tool Use benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [APEX-Agents](https://noometry.com/benchmarks/apex-agents) | 56.6% | #16 of 49, top 33% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Vending-Bench 2](https://noometry.com/benchmarks/vending-bench-2) | 8,164 | #11 of 60, top 19% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Reasoning

GLM-5.3 Reasoning benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [NYT Connections (extended)](https://noometry.com/benchmarks/nyt-connections) | 74.2% | #46 of 91, top 51% | high reasoning | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| [CritPt](https://noometry.com/benchmarks/critpt) | 14.6% |  | low | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CritPt](https://noometry.com/benchmarks/critpt) | 19.1% | #27 of 134, top 21% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Chess Puzzles](https://noometry.com/benchmarks/chess-puzzles) | 21% | #53 of 129, top 42% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-24 |
| [LMArena Hard Prompts](https://noometry.com/benchmarks/arena-hard-prompts) | 1489 | #16 of 297, top 6% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [Mystery Game Puzzles](https://noometry.com/benchmarks/mystery-game-puzzles) | 33% | #23 of 74, top 32% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-30 |
| [DTBench](https://noometry.com/benchmarks/dtbench) | 87.7% | #49 of 151, top 33% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMCA](https://noometry.com/benchmarks/lmca) | 55.5% | #10 of 125, top 8% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Bench to the Future 3](https://noometry.com/benchmarks/btf-3) | 0.15 | Best of 10 |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Epoch Capabilities Index](https://noometry.com/benchmarks/epoch-capabilities-index) | 155.61 | #27 of 213, top 13% |  | [Epoch AI](https://epoch.ai/eci) | 2026-08-14 |

### Math

GLM-5.3 Math benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [FrontierMath (Tiers 1-3)](https://noometry.com/benchmarks/frontiermath) | 68.8% | #25 of 81, top 31% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-25 |
| [FrontierMath Tier 4](https://noometry.com/benchmarks/frontiermath-tier-4) | 29.3% | #31 of 63, top 50% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-25 |
| [OTIS Mock AIME 2024-2025](https://noometry.com/benchmarks/otis-mock-aime) | 91.1% | #49 of 173, top 29% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-24 |
| [ProofBench](https://noometry.com/benchmarks/proofbench) | 49% | #31 of 77, top 41% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Math](https://noometry.com/benchmarks/arena-math) | 1489 | #19 of 285, top 7% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Knowledge

GLM-5.3 Knowledge benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [GPQA Diamond](https://noometry.com/benchmarks/gpqa-diamond) | 90.9% | #29 of 186, top 16% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-24 |
| [SimpleQA Verified](https://noometry.com/benchmarks/simpleqa-verified) | 41% | #41 of 77, top 54% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-28 |
| [LMArena Expert](https://noometry.com/benchmarks/arena-expert) | 1516 | #14 of 273, top 6% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Multilingual

GLM-5.3 Multilingual benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Non-English](https://noometry.com/benchmarks/arena-non-english) | 1457 | #28 of 297, top 10% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Chinese](https://noometry.com/benchmarks/arena-chinese) | 1528 | #19 of 285, top 7% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena French](https://noometry.com/benchmarks/arena-french) | 1499 | #12 of 223, top 6% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena German](https://noometry.com/benchmarks/arena-german) | 1499 | #6 of 231, top 3% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Japanese](https://noometry.com/benchmarks/arena-japanese) | 1453 | #22 of 211, top 11% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Korean](https://noometry.com/benchmarks/arena-korean) | 1472 | #6 of 213, top 3% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Russian](https://noometry.com/benchmarks/arena-russian) | 1463 | #31 of 283, top 11% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Spanish](https://noometry.com/benchmarks/arena-spanish) | 1460 | #35 of 226, top 16% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Instruction Following

GLM-5.3 Instruction Following benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Instruction Following](https://noometry.com/benchmarks/arena-instruction-following) | 1477 | #20 of 298, top 7% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Long Context

GLM-5.3 Long Context benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Longer Query](https://noometry.com/benchmarks/arena-longer-query) | 1482 | #22 of 291, top 8% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Writing & Preference

GLM-5.3 Writing & Preference benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Text](https://noometry.com/benchmarks/arena-text) | 1471 | #24 of 297, top 9% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Creative Writing](https://noometry.com/benchmarks/arena-creative-writing) | 1457 | #20 of 295, top 7% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [EQ-Bench Creative Writing](https://noometry.com/benchmarks/eqbench-creative-writing) | 2075 | #6 of 115, top 6% |  | [EQ-Bench](https://eqbench.com/creative_writing.html) |  |
| [LMArena Multi-Turn](https://noometry.com/benchmarks/arena-multi-turn) | 1472 | #28 of 295, top 10% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

## API pricing by provider

GLM-5.3 API prices
| Route | Input $/M | Output $/M | Cached input $/M | Checked |
| --- | --- | --- | --- | --- |
| [bedrock](https://docs.aws.amazon.com/bedrock/latest/userguide/models-supported.html) | $1.68 | $5.28 | $0.31 | 2026-10-10 |
| [deepinfra](https://deepinfra.com/models) | $0.90 | $4 | $0.20 | 2026-10-10 |
| [fireworks](https://fireworks.ai/docs/) | $1.40 | $4.40 | $0.26 | 2026-10-10 |
| [mistral](https://docs.mistral.ai/getting-started/models/) | $1.40 | $4.40 | $0.14 | 2026-10-10 |
| [openrouter](https://openrouter.ai/z-ai/glm-5.3) | $0.039 | $4.80 | $0.038 | 2026-10-10 |
| [together](https://docs.together.ai/docs/serverless-models) | $1.40 | $4.40 | $0.26 | 2026-10-10 |
| [zai](https://docs.z.ai/guides/overview/pricing) | $1.40 | $4.40 | $0.26 | 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-5.3

-   [GLM-5.3 vs GLM-5.2](https://noometry.com/compare/glm-5-2-vs-glm-5-3)
-   [GLM-5.3 vs Grok 4.5](https://noometry.com/compare/glm-5-3-vs-grok-4-5)
-   [GLM-5.3 vs Muse Spark 1.3](https://noometry.com/compare/glm-5-3-vs-muse-spark-1-3)
-   [GLM-5.3 vs Gemini 4 Argon](https://noometry.com/compare/gemini-4-argon-vs-glm-5-3)
-   [GLM-5.3 vs Gemini 3 Pro](https://noometry.com/compare/gemini-3-pro-vs-glm-5-3)
-   [GLM-5.3 vs Gemini 3.1 Pro Preview](https://noometry.com/compare/gemini-3-1-pro-preview-vs-glm-5-3)
-   [GLM-5.3 vs Claude Sonnet 5](https://noometry.com/compare/claude-sonnet-5-vs-glm-5-3)
-   [GLM-5.3 vs GPT-6 Astra](https://noometry.com/compare/glm-5-3-vs-gpt-6-astra)
-   [GLM-5.3 vs Claude Fable 5.1](https://noometry.com/compare/claude-fable-5-1-vs-glm-5-3)
-   [GLM-5.3 vs Gemini 3.8 Flash](https://noometry.com/compare/gemini-3-8-flash-vs-glm-5-3)
-   [GLM-5.3 vs Kimi K3](https://noometry.com/compare/glm-5-3-vs-kimi-k3)
-   [GLM-5.3 vs Grok 4.6](https://noometry.com/compare/glm-5-3-vs-grok-4-6)
-   [GLM-5.3 vs Qwen3.8 Max](https://noometry.com/compare/glm-5-3-vs-qwen3-8-max)
-   [GLM-5.3 vs DeepSeek V4 Pro](https://noometry.com/compare/deepseek-v4-pro-vs-glm-5-3)

## Other Z.ai (Zhipu) models

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

## Frequently asked questions

### How good is GLM-5.3?

GLM-5.3 by Z.ai (Zhipu) ranks 26th of 354 ranked models on the Noometry Index as of October 2026, with a score of 54.8. Its strongest category is writing & preference, where it ranks 6th. API pricing starts at $1.40 per million input tokens and $4.40 per million output tokens, with a 1M-token context window.

### How much does GLM-5.3 cost?

GLM-5.3 costs $1.40 per million input tokens and $4.40 per million output tokens on Z.ai (Zhipu)'s own API, with cached input at $0.26.

### What is GLM-5.3's context window?

GLM-5.3 accepts up to 1M tokens of input and can write up to 131K tokens in one response.

### Is GLM-5.3 open source?

Yes. GLM-5.3's weights are downloadable from Hugging Face (zai-org/GLM-5.3); check the license for commercial terms.

### What are GLM-5.3's strengths and weaknesses?

Relative to other ranked models, GLM-5.3 places best in writing & preference, coding, instruction following and lowest in agentic & tool use, long context, reasoning.

### What is GLM-5.3 best at?

Its best category is writing & preference, where it ranks 6th on Noometry.

### Cite this page

Noometry. (2026). GLM-5.3 benchmarks and pricing. Retrieved October 10, 2026, from https://noometry.com/models/glm-5-3

Quote Noometry with a link back to this page. It is also available in [Markdown](https://noometry.com/md/models/glm-5-3.md).