Z.ai (Zhipu), open weights

# GLM-5.3-Flash

> GLM-5.3-Flash by Z.ai (Zhipu), released August 2026. Ranked #41 of 354 with a Noometry Index of 51.8. API: $0.15 in / $0.50 out per M tokens. 1M context. Scores, sources and comparisons.
- Canonical page: https://noometry.com/models/glm-5-3-flash
- Last updated: 2026-10-10
- Title: GLM-5.3-Flash Benchmarks, Price & Rank (October 2026)

GLM-5.3-Flash by Z.ai (Zhipu) ranks 41st of 354 ranked models on the Noometry Index as of October 2026, with a score of 51.8. Its strongest category is instruction following, where it ranks 20th. API pricing starts at $0.15 per million input tokens and $0.50 per million output tokens, with a 1M-token context window.

Last verified October 10, 2026

## Specifications

- **Noometry rank:** #41 of 354
- **Index score:** 51.8
- **Evidence:** Confirmed 40 results
- **Provider:** [Z.ai (Zhipu)](https://noometry.com/providers/zai)
- **Released:** August 20, 2026
- **Weights:** Open weights
- **Reasoning:** Yes
- **Context window:** 1M
- **Max output:** 131K
- **Input price:** $0.15 / M
- **Output price:** $0.50 / M
- **Blended price:** $0.24 / M
- **Output speed:** Not measured
- **Value:** #39 of 219
- **Knowledge cutoff:** Unknown
- **Input:** text, image, video, pdf
- **Hugging Face:** [zai-org/GLM-5.3-Flash](https://huggingface.co/zai-org/GLM-5.3-Flash)

## Category scores

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

GLM-5.3-Flash category scores

1.  Coding 53.1
2.  Agentic & Tool Use 34.2
3.  Reasoning 48.0
4.  Math 53.3
5.  Knowledge 58.4
6.  Multimodal 42.8
7.  Multilingual 56.0
8.  Instruction Following 77.5
9.  Long Context 45.4
10.  Writing & Preference 65.3
11.  020406080

GLM-5.3-Flash category ranks
| Category | Score | Rank | Results |
| --- | --- | --- | --- |
| [Coding](https://noometry.com/best/coding) | 53.1 | #31 | 7 |
| [Agentic & Tool Use](https://noometry.com/best/agentic) | 34.2 | #47 | 2 |
| [Reasoning](https://noometry.com/best/reasoning) | 48.0 | #42 | 7 |
| [Math](https://noometry.com/best/math) | 53.3 | #47 | 5 |
| [Knowledge](https://noometry.com/best/knowledge) | 58.4 | #36 | 2 |
| [Multimodal](https://noometry.com/best/multimodal) | 42.8 | #27 | 1 |
| [Multilingual](https://noometry.com/best/multilingual) | 56.0 | #25 | 1 |
| [Instruction Following](https://noometry.com/best/instruction-following) | 77.5 | #20 | 1 |
| [Long Context](https://noometry.com/best/long-context) | 45.4 | #39 | 1 |
| [Writing & Preference](https://noometry.com/best/writing) | 65.3 | #50 | 3 |

## Strengths and weaknesses

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

### Strongest categories

GLM-5.3-Flash: strongest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Instruction Following](https://noometry.com/best/instruction-following) | 77.5 | +6.3 | #20 of 305, top 7% |
| [Multilingual](https://noometry.com/best/multilingual) | 56.0 | +8.6 | #25 of 297, top 9% |
| [Coding](https://noometry.com/best/coding) | 53.1 | +14.4 | #31 of 340, top 10% |

### Weakest categories

GLM-5.3-Flash: weakest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Agentic & Tool Use](https://noometry.com/best/agentic) | 34.2 | +3.9 | #47 of 154, top 31% |
| [Multimodal](https://noometry.com/best/multimodal) | 42.8 | +4.3 | #27 of 128, top 22% |
| [Writing & Preference](https://noometry.com/best/writing) | 65.3 | +11.5 | #50 of 312, top 17% |

## Closest competitors

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

Models ranked closest to GLM-5.3-Flash
| Model | Rank | Score | Blended $/M | Speed |  |
| --- | --- | --- | --- | --- | --- |
| [Grok 4.7](https://noometry.com/models/grok-4-7) | #37 | 53.1 | $3 | — | [Compare](https://noometry.com/compare/glm-5-3-flash-vs-grok-4-7) |
| [DeepSeek V4.1 Flash](https://noometry.com/models/deepseek-v4-1-flash) | #38 | 52.8 | $0.26 | — | [Compare](https://noometry.com/compare/deepseek-v4-1-flash-vs-glm-5-3-flash) |
| [GPT-5.2 Pro](https://noometry.com/models/gpt-5-2-pro) | #39 | 52.3 | $57.75 | — | [Compare](https://noometry.com/compare/glm-5-3-flash-vs-gpt-5-2-pro) |
| [Gemini 3 Flash Preview](https://noometry.com/models/gemini-3-flash-preview) | #40 | 52.3 | $1.13 | — | [Compare](https://noometry.com/compare/gemini-3-flash-preview-vs-glm-5-3-flash) |
| [Qwen3.7 Max](https://noometry.com/models/qwen3-7-max) | #42 | 51.5 | $3.75 | — | [Compare](https://noometry.com/compare/glm-5-3-flash-vs-qwen3-7-max) |
| [Qwen3.6 Max Preview](https://noometry.com/models/qwen3-6-max-preview) | #43 | 51.5 | $2.92 | — | [Compare](https://noometry.com/compare/glm-5-3-flash-vs-qwen3-6-max-preview) |
| [GLM-5.2](https://noometry.com/models/glm-5-2) | #44 | 51.1 | $2.15 | 23 | [Compare](https://noometry.com/compare/glm-5-2-vs-glm-5-3-flash) |
| [GPT-5](https://noometry.com/models/gpt-5) | #45 | 50.9 | $3.44 | 2 | [Compare](https://noometry.com/compare/glm-5-3-flash-vs-gpt-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-5.3-Flash Coding benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [DeepSWE](https://noometry.com/benchmarks/deepswe) | 63.4% | #16 of 29, top 56% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [FrontierCode](https://noometry.com/benchmarks/frontiercode) | 31.8% | #25 of 37, top 68% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CursorBench](https://noometry.com/benchmarks/cursorbench) | 31.1% |  | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CursorBench](https://noometry.com/benchmarks/cursorbench) | 26.9% |  | low | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CursorBench](https://noometry.com/benchmarks/cursorbench) | 36.8% | #12 of 14, top 86% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena WebDev](https://noometry.com/benchmarks/arena-webdev) | 1609 | #21 of 113, top 19% |  | [LMArena](https://lmarena.ai/leaderboard/webdev) | 2026-10-08 |
| [FrontierSWE](https://noometry.com/benchmarks/frontierswe) | 18.1% | #15 of 18, top 84% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [SciCode](https://noometry.com/benchmarks/scicode) | 51.6% | #37 of 121, top 31% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Coding](https://noometry.com/benchmarks/arena-coding) | 1508 | #13 of 294, top 5% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [ALE-Bench](https://noometry.com/benchmarks/ale-bench) | 303.55 | #98 of 105, top 94% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Agentic & Tool Use

GLM-5.3-Flash Agentic & Tool Use benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [APEX-Agents](https://noometry.com/benchmarks/apex-agents) | 52.8% | #22 of 49, top 45% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [GDP.pdf](https://noometry.com/benchmarks/gdp-pdf) | 14% | #30 of 36, top 84% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Reasoning

GLM-5.3-Flash Reasoning benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 50.1% |  | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 27.9% |  | low | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 65.8% | #23 of 83, top 28% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 71.8% |  | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 47% |  | low | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 91% | #24 of 83, top 29% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CritPt](https://noometry.com/benchmarks/critpt) | 15.4% | #35 of 134, top 27% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Chess Puzzles](https://noometry.com/benchmarks/chess-puzzles) | 14% | #70 of 129, top 55% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-26 |
| [LMArena Hard Prompts](https://noometry.com/benchmarks/arena-hard-prompts) | 1491 | #15 of 297, top 6% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [Mystery Game Puzzles](https://noometry.com/benchmarks/mystery-game-puzzles) | 8% | #64 of 74, top 87% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-28 |
| [Surface Evolver Bench](https://noometry.com/benchmarks/surface-evolver-bench) | 52.5% | #14 of 25, top 57% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Bench to the Future 3](https://noometry.com/benchmarks/btf-3) | 0.15 | #2 of 10, top 20% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Epoch Capabilities Index](https://noometry.com/benchmarks/epoch-capabilities-index) | 151.88 | #46 of 213, top 22% |  | [Epoch AI](https://epoch.ai/eci) | 2026-08-20 |

### Math

GLM-5.3-Flash Math benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [FrontierMath (Tiers 1-3)](https://noometry.com/benchmarks/frontiermath) | 55.8% | #41 of 81, top 51% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-27 |
| [FrontierMath Tier 4](https://noometry.com/benchmarks/frontiermath-tier-4) | 17.1% | #45 of 63, top 72% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-27 |
| [OTIS Mock AIME 2024-2025](https://noometry.com/benchmarks/otis-mock-aime) | 93.9% | #40 of 173, top 24% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-26 |
| [ProofBench](https://noometry.com/benchmarks/proofbench) | 21% | #47 of 77, top 62% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Math](https://noometry.com/benchmarks/arena-math) | 1500 | #12 of 285, top 5% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Knowledge

GLM-5.3-Flash Knowledge benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [GPQA Diamond](https://noometry.com/benchmarks/gpqa-diamond) | 90.2% | #38 of 186, top 21% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-26 |
| [LMArena Expert](https://noometry.com/benchmarks/arena-expert) | 1513 | #15 of 273, top 6% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Multimodal

GLM-5.3-Flash Multimodal benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Vision](https://noometry.com/benchmarks/arena-vision) | 1296 | #19 of 122, top 16% |  | [LMArena](https://lmarena.ai/leaderboard/vision) | 2026-10-09 |

### Multilingual

GLM-5.3-Flash Multilingual benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Non-English](https://noometry.com/benchmarks/arena-non-english) | 1462 | #25 of 297, top 9% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Chinese](https://noometry.com/benchmarks/arena-chinese) | 1527 | #20 of 285, top 8% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena French](https://noometry.com/benchmarks/arena-french) | 1496 | #15 of 223, top 7% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena German](https://noometry.com/benchmarks/arena-german) | 1470 | #28 of 231, top 13% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Japanese](https://noometry.com/benchmarks/arena-japanese) | 1429 | #32 of 211, top 16% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Korean](https://noometry.com/benchmarks/arena-korean) | 1446 | #21 of 213, top 10% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Russian](https://noometry.com/benchmarks/arena-russian) | 1469 | #27 of 283, top 10% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Spanish](https://noometry.com/benchmarks/arena-spanish) | 1471 | #22 of 226, top 10% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Instruction Following

GLM-5.3-Flash Instruction Following benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Instruction Following](https://noometry.com/benchmarks/arena-instruction-following) | 1478 | #17 of 298, top 6% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Long Context

GLM-5.3-Flash Long Context benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Longer Query](https://noometry.com/benchmarks/arena-longer-query) | 1482 | #21 of 291, top 8% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Writing & Preference

GLM-5.3-Flash Writing & Preference benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Text](https://noometry.com/benchmarks/arena-text) | 1471 | #25 of 297, top 9% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Creative Writing](https://noometry.com/benchmarks/arena-creative-writing) | 1442 | #33 of 295, top 12% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Multi-Turn](https://noometry.com/benchmarks/arena-multi-turn) | 1467 | #33 of 295, top 12% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

## API pricing by provider

GLM-5.3-Flash API prices
| Route | Input $/M | Output $/M | Cached input $/M | Checked |
| --- | --- | --- | --- | --- |
| [deepinfra](https://deepinfra.com/models) | $0.15 | $0.50 | $0.03 | 2026-10-10 |
| [fireworks](https://fireworks.ai/docs/) | $0.15 | $0.50 | $0.03 | 2026-10-10 |
| [openrouter](https://openrouter.ai/z-ai/glm-5.3-flash) | $0.15 | $0.50 | $0.03 | 2026-10-10 |
| [together](https://docs.together.ai/docs/serverless-models) | $0.15 | $0.50 | $0.03 | 2026-10-10 |
| [zai](https://docs.z.ai/guides/overview/pricing) | $0.15 | $0.50 | $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-5.3-Flash

-   [GLM-5.3-Flash vs GLM-4.7-Flash](https://noometry.com/compare/glm-4-7-flash-vs-glm-5-3-flash)
-   [GLM-5.3-Flash vs Gemini 3 Flash Preview](https://noometry.com/compare/gemini-3-flash-preview-vs-glm-5-3-flash)
-   [GLM-5.3-Flash vs Qwen3.7 Max](https://noometry.com/compare/glm-5-3-flash-vs-qwen3-7-max)
-   [GLM-5.3-Flash vs GPT-5.2 Pro](https://noometry.com/compare/glm-5-3-flash-vs-gpt-5-2-pro)
-   [GLM-5.3-Flash vs Qwen3.6 Max Preview](https://noometry.com/compare/glm-5-3-flash-vs-qwen3-6-max-preview)
-   [GLM-5.3-Flash vs DeepSeek V4.1 Flash](https://noometry.com/compare/deepseek-v4-1-flash-vs-glm-5-3-flash)
-   [GLM-5.3-Flash vs GLM-5.2](https://noometry.com/compare/glm-5-2-vs-glm-5-3-flash)
-   [GLM-5.3-Flash vs GPT-6 Astra](https://noometry.com/compare/glm-5-3-flash-vs-gpt-6-astra)
-   [GLM-5.3-Flash vs Claude Fable 5.1](https://noometry.com/compare/claude-fable-5-1-vs-glm-5-3-flash)
-   [GLM-5.3-Flash vs Gemini 3.8 Flash](https://noometry.com/compare/gemini-3-8-flash-vs-glm-5-3-flash)
-   [GLM-5.3-Flash vs Kimi K3](https://noometry.com/compare/glm-5-3-flash-vs-kimi-k3)
-   [GLM-5.3-Flash vs Grok 4.6](https://noometry.com/compare/glm-5-3-flash-vs-grok-4-6)
-   [GLM-5.3-Flash vs Qwen3.8 Max](https://noometry.com/compare/glm-5-3-flash-vs-qwen3-8-max)
-   [GLM-5.3-Flash vs Muse Spark 1.3](https://noometry.com/compare/glm-5-3-flash-vs-muse-spark-1-3)

## Other Z.ai (Zhipu) models

-   [GLM-5.3](https://noometry.com/models/glm-5-3)54.8
-   [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-Flash?

GLM-5.3-Flash by Z.ai (Zhipu) ranks 41st of 354 ranked models on the Noometry Index as of October 2026, with a score of 51.8. Its strongest category is instruction following, where it ranks 20th. API pricing starts at $0.15 per million input tokens and $0.50 per million output tokens, with a 1M-token context window.

### How much does GLM-5.3-Flash cost?

GLM-5.3-Flash costs $0.15 per million input tokens and $0.50 per million output tokens on Z.ai (Zhipu)'s own API, with cached input at $0.03.

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

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

### Is GLM-5.3-Flash open source?

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

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

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

### What is GLM-5.3-Flash best at?

Its best category is instruction following, where it ranks 20th on Noometry.

### Cite this page

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

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