Z.ai (Zhipu), open weights

# GLM-5.2

> GLM-5.2 by Z.ai (Zhipu), released June 2026. Ranked #44 of 354 with a Noometry Index of 51.1. API: $1.40 in / $4.40 out per M tokens. 1M context. Scores, sources and comparisons.
- Canonical page: https://noometry.com/models/glm-5-2
- Last updated: 2026-10-10
- Title: GLM-5.2 Benchmarks, Price & Rank (October 2026) | Noometry

GLM-5.2 by Z.ai (Zhipu) ranks 44th of 354 ranked models on the Noometry Index as of October 2026, with a score of 51.1. Its strongest category is writing & preference, where it ranks 21st. 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:** #44 of 354
- **Index score:** 51.1
- **Evidence:** Confirmed 51 results
- **Provider:** [Z.ai (Zhipu)](https://noometry.com/providers/zai)
- **Released:** June 13, 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:** 23 tokens/s [Kagi](https://help.kagi.com/kagi/ai/llm-benchmark.html)
- **Value:** #146 of 219
- **Knowledge cutoff:** Unknown
- **Input:** text
- **Hugging Face:** [zai-org/GLM-5.2](https://huggingface.co/zai-org/GLM-5.2)

## Category scores

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

GLM-5.2 category scores

1.  Coding 51.3
2.  Agentic & Tool Use 32.4
3.  Reasoning 42.3
4.  Math 55.7
5.  Knowledge 57.1
6.  Multilingual 55.8
7.  Instruction Following 76.9
8.  Long Context 45.3
9.  Writing & Preference 70.4
10.  020406080

GLM-5.2 category ranks
| Category | Score | Rank | Results |
| --- | --- | --- | --- |
| [Coding](https://noometry.com/best/coding) | 51.3 | #41 | 7 |
| [Agentic & Tool Use](https://noometry.com/best/agentic) | 32.4 | #63 | 4 |
| [Reasoning](https://noometry.com/best/reasoning) | 42.3 | #52 | 13 |
| [Math](https://noometry.com/best/math) | 55.7 | #43 | 6 |
| [Knowledge](https://noometry.com/best/knowledge) | 57.1 | #40 | 3 |
| [Multilingual](https://noometry.com/best/multilingual) | 55.8 | #26 | 1 |
| [Instruction Following](https://noometry.com/best/instruction-following) | 76.9 | #34 | 1 |
| [Long Context](https://noometry.com/best/long-context) | 45.3 | #43 | 1 |
| [Writing & Preference](https://noometry.com/best/writing) | 70.4 | #21 | 5 |

## Strengths and weaknesses

Categories where GLM-5.2 places highest and lowest among the models ranked in each, with its score against that category's median.

### Strongest categories

GLM-5.2: strongest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Writing & Preference](https://noometry.com/best/writing) | 70.4 | +16.7 | #21 of 312, top 7% |
| [Multilingual](https://noometry.com/best/multilingual) | 55.8 | +8.4 | #26 of 297, top 9% |
| [Instruction Following](https://noometry.com/best/instruction-following) | 76.9 | +5.7 | #34 of 305, top 12% |

### Weakest categories

GLM-5.2: weakest categories
| Category | Score | vs median | Rank |
| --- | --- | --- | --- |
| [Agentic & Tool Use](https://noometry.com/best/agentic) | 32.4 | +2.1 | #63 of 154, top 41% |
| [Reasoning](https://noometry.com/best/reasoning) | 42.3 | +18.7 | #52 of 350, top 15% |
| [Long Context](https://noometry.com/best/long-context) | 45.3 | +4.4 | #43 of 296, top 15% |

## Closest competitors

The models ranked just above and below GLM-5.2. When scores are this close, price and speed are often the better way to choose.

Models ranked closest to GLM-5.2
| Model | Rank | Score | Blended $/M | Speed |  |
| --- | --- | --- | --- | --- | --- |
| [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-5-2) |
| [GLM-5.3-Flash](https://noometry.com/models/glm-5-3-flash) | #41 | 51.8 | $0.24 | — | [Compare](https://noometry.com/compare/glm-5-2-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-5-2-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-5-2-vs-qwen3-6-max-preview) |
| [GPT-5](https://noometry.com/models/gpt-5) | #45 | 50.9 | $3.44 | 2 | [Compare](https://noometry.com/compare/glm-5-2-vs-gpt-5) |
| [Muse Spark](https://noometry.com/models/muse-spark) | #46 | 50.6 | — | — | [Compare](https://noometry.com/compare/glm-5-2-vs-muse-spark) |
| [Claude Opus 4.5](https://noometry.com/models/claude-opus-4-5) | #47 | 50.5 | $10 | 13 | [Compare](https://noometry.com/compare/claude-opus-4-5-vs-glm-5-2) |
| [Muse Spark 1.2](https://noometry.com/models/muse-spark-1-2) | #48 | 50.3 | $2 | — | [Compare](https://noometry.com/compare/glm-5-2-vs-muse-spark-1-2) |

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-5.2 Coding benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [SWE-bench Verified](https://noometry.com/benchmarks/swe-bench-verified) | 78.7% | #5 of 32, top 16% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-06-25 |
| [DeepSWE](https://noometry.com/benchmarks/deepswe) | 36.3% |  | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [DeepSWE](https://noometry.com/benchmarks/deepswe) | 43.8% | #25 of 29, top 87% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [FrontierCode](https://noometry.com/benchmarks/frontiercode) | 24.5% | #30 of 37, top 82% | none | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena WebDev](https://noometry.com/benchmarks/arena-webdev) | 1603 | #22 of 113, top 20% |  | [LMArena](https://lmarena.ai/leaderboard/webdev) | 2026-10-08 |
| [SciCode](https://noometry.com/benchmarks/scicode) | 50.5% | #41 of 121, top 34% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [SciCode](https://noometry.com/benchmarks/scicode) | 36.1% |  | none | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [WeirdML](https://noometry.com/benchmarks/weirdml) | 67.3% |  | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [WeirdML](https://noometry.com/benchmarks/weirdml) | 70.1% | #18 of 119, top 16% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Coding](https://noometry.com/benchmarks/arena-coding) | 1485 | #38 of 294, top 13% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [ALE-Bench](https://noometry.com/benchmarks/ale-bench) | 1,047 | #40 of 105, top 39% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [ALE-Bench](https://noometry.com/benchmarks/ale-bench) | 1,010 |  | max | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Agentic & Tool Use

GLM-5.2 Agentic & Tool Use benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [APEX-Agents](https://noometry.com/benchmarks/apex-agents) | 45.2% | #32 of 49, top 66% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [τ²-bench Banking](https://noometry.com/benchmarks/tau2-banking) | 37.1% | #11 of 26, top 43% | xhigh | [τ²-bench](https://taubench.com/) | 2026-08-04 |
| [PostTrainBench](https://noometry.com/benchmarks/posttrainbench) | 31.7% | #6 of 11, top 55% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [GBAEval](https://noometry.com/benchmarks/gbaeval) | 0% | #21 of 23, top 92% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Vending-Bench 2](https://noometry.com/benchmarks/vending-bench-2) | 8,314 | #10 of 60, top 17% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |

### Reasoning

GLM-5.2 Reasoning benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [ARC-AGI-2](https://noometry.com/benchmarks/arc-agi-2) | 22.8% | #41 of 83, top 50% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [SimpleBench](https://noometry.com/benchmarks/simplebench) | 58.8% | #28 of 77, top 37% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Kagi LLM Benchmark](https://noometry.com/benchmarks/kagi-reasoning) | 62.6% | #37 of 99, top 38% |  | [Kagi LLM Benchmark](https://help.kagi.com/kagi/ai/llm-benchmark.html) |  |
| [Kagi LLM Benchmark](https://noometry.com/benchmarks/kagi-reasoning) | 60% |  |  | [Kagi LLM Benchmark](https://help.kagi.com/kagi/ai/llm-benchmark.html) |  |
| [NYT Connections (extended)](https://noometry.com/benchmarks/nyt-connections) | 74.3% | #45 of 91, top 50% | high reasoning | [Lech Mazur benchmarks](https://github.com/lechmazur/nyt-connections) |  |
| [ARC-AGI-1](https://noometry.com/benchmarks/arc-agi-1) | 77% | #40 of 83, top 49% |  | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CritPt](https://noometry.com/benchmarks/critpt) | 20.9% | #21 of 134, top 16% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [CritPt](https://noometry.com/benchmarks/critpt) | 3.1% |  | none | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Chess Puzzles](https://noometry.com/benchmarks/chess-puzzles) | 14% |  | low | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-10 |
| [Chess Puzzles](https://noometry.com/benchmarks/chess-puzzles) | 21% | #52 of 129, top 41% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-06-17 |
| [Chess Puzzles](https://noometry.com/benchmarks/chess-puzzles) | 6% |  | none | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-10 |
| [EBR-Bench](https://noometry.com/benchmarks/ebr-bench) | 9.5% | #19 of 24, top 80% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-06-29 |
| [LMArena Hard Prompts](https://noometry.com/benchmarks/arena-hard-prompts) | 1480 | #34 of 297, top 12% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [Mystery Game Puzzles](https://noometry.com/benchmarks/mystery-game-puzzles) | 19% | #44 of 74, top 60% | low | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-27 |
| [Mystery Game Puzzles](https://noometry.com/benchmarks/mystery-game-puzzles) | 15% |  | medium | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-27 |
| [Mystery Game Puzzles](https://noometry.com/benchmarks/mystery-game-puzzles) | 18% |  | minimal | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-27 |
| [Mystery Game Puzzles](https://noometry.com/benchmarks/mystery-game-puzzles) | 18% |  | none | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-27 |
| [DTBench](https://noometry.com/benchmarks/dtbench) | 93.6% | #25 of 151, top 17% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMCA](https://noometry.com/benchmarks/lmca) | 45.8% | #31 of 125, top 25% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Surface Evolver Bench](https://noometry.com/benchmarks/surface-evolver-bench) | 55.6% | #12 of 25, top 48% | high | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [Epoch Capabilities Index](https://noometry.com/benchmarks/epoch-capabilities-index) | 151.78 | #48 of 213, top 23% |  | [Epoch AI](https://epoch.ai/eci) | 2026-06-16 |

### Math

GLM-5.2 Math benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [FrontierMath (Tiers 1-3)](https://noometry.com/benchmarks/frontiermath) | 54.7% |  | low | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-29 |
| [FrontierMath (Tiers 1-3)](https://noometry.com/benchmarks/frontiermath) | 59.2% | #36 of 81, top 45% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-06-19 |
| [FrontierMath (Tiers 1-3)](https://noometry.com/benchmarks/frontiermath) | 42.5% |  | none | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-29 |
| [FrontierMath Tier 4](https://noometry.com/benchmarks/frontiermath-tier-4) | 29.3% | #30 of 63, top 48% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-06-19 |
| [MathArena Final-Answer Competitions](https://noometry.com/benchmarks/matharena) | 67.6% | #16 of 29, top 56% |  | [MathArena](https://matharena.ai/) |  |
| [OTIS Mock AIME 2024-2025](https://noometry.com/benchmarks/otis-mock-aime) | 75.6% |  | low | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-10 |
| [OTIS Mock AIME 2024-2025](https://noometry.com/benchmarks/otis-mock-aime) | 86.4% | #67 of 173, top 39% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-06-25 |
| [OTIS Mock AIME 2024-2025](https://noometry.com/benchmarks/otis-mock-aime) | 28.9% |  | none | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-10 |
| [ProofBench](https://noometry.com/benchmarks/proofbench) | 35% | #39 of 77, top 51% | max | [Epoch AI](https://epoch.ai/benchmarks) |  |
| [LMArena Math](https://noometry.com/benchmarks/arena-math) | 1482 | #26 of 285, top 10% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Knowledge

GLM-5.2 Knowledge benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [GPQA Diamond](https://noometry.com/benchmarks/gpqa-diamond) | 87.9% |  | low | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-10 |
| [GPQA Diamond](https://noometry.com/benchmarks/gpqa-diamond) | 91.9% | #23 of 186, top 13% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-06-24 |
| [GPQA Diamond](https://noometry.com/benchmarks/gpqa-diamond) | 71.2% |  | none | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-10 |
| [SimpleQA Verified](https://noometry.com/benchmarks/simpleqa-verified) | 34.2% | #50 of 77, top 65% | max | [Epoch AI](https://epoch.ai/benchmarks) | 2026-08-27 |
| [LMArena Expert](https://noometry.com/benchmarks/arena-expert) | 1486 | #36 of 273, top 14% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Multilingual

GLM-5.2 Multilingual benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Non-English](https://noometry.com/benchmarks/arena-non-english) | 1459 | #26 of 297, top 9% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Chinese](https://noometry.com/benchmarks/arena-chinese) | 1519 | #27 of 285, top 10% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena French](https://noometry.com/benchmarks/arena-french) | 1479 | #27 of 223, top 13% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena German](https://noometry.com/benchmarks/arena-german) | 1468 | #29 of 231, top 13% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Japanese](https://noometry.com/benchmarks/arena-japanese) | 1451 | #25 of 211, top 12% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Korean](https://noometry.com/benchmarks/arena-korean) | 1445 | #22 of 213, top 11% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Russian](https://noometry.com/benchmarks/arena-russian) | 1466 | #30 of 283, top 11% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Spanish](https://noometry.com/benchmarks/arena-spanish) | 1477 | #15 of 226, top 7% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Instruction Following

GLM-5.2 Instruction Following benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Instruction Following](https://noometry.com/benchmarks/arena-instruction-following) | 1465 | #30 of 298, top 11% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Long Context

GLM-5.2 Long Context benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Longer Query](https://noometry.com/benchmarks/arena-longer-query) | 1479 | #25 of 291, top 9% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

### Writing & Preference

GLM-5.2 Writing & Preference benchmark results
| Benchmark | Score | Position | Setting | Source | Date |
| --- | --- | --- | --- | --- | --- |
| [LMArena Text](https://noometry.com/benchmarks/arena-text) | 1470 | #27 of 297, top 10% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [LMArena Creative Writing](https://noometry.com/benchmarks/arena-creative-writing) | 1462 | #18 of 295, top 7% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |
| [EQ-Bench Creative Writing](https://noometry.com/benchmarks/eqbench-creative-writing) | 1757 | #26 of 115, top 23% |  | [EQ-Bench](https://eqbench.com/creative_writing.html) |  |
| [EQ-Bench 4](https://noometry.com/benchmarks/eqbench-4) | 1222 | #14 of 28, top 50% |  | [EQ-Bench](https://eqbench.com/) |  |
| [LMArena Multi-Turn](https://noometry.com/benchmarks/arena-multi-turn) | 1469 | #31 of 295, top 11% |  | [LMArena](https://lmarena.ai/leaderboard/text) | 2026-10-08 |

## API pricing by provider

GLM-5.2 API prices
| Route | Input $/M | Output $/M | Cached input $/M | Checked |
| --- | --- | --- | --- | --- |
| [alibaba](https://www.alibabacloud.com/help/en/model-studio/models) | $1.40 | $4.40 | $0.28 | 2026-10-10 |
| [deepinfra](https://deepinfra.com/models) | $0.75 | $2.40 | $0.14 | 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-5.2) | $0.06 | $7 | $0.059 | 2026-10-10 |
| [together](https://docs.together.ai/docs/serverless-models) | $1.40 | $4.40 | $0.26 | 2026-10-10 |
| [vertex](https://cloud.google.com/vertex-ai/generative-ai/docs/models) | $1.40 | $4.40 | $0.14 | 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-5.2

-   [GLM-5.2 vs GLM-5.1](https://noometry.com/compare/glm-5-1-vs-glm-5-2)
-   [GLM-5.2 vs Qwen3.6 Max Preview](https://noometry.com/compare/glm-5-2-vs-qwen3-6-max-preview)
-   [GLM-5.2 vs GPT-5](https://noometry.com/compare/glm-5-2-vs-gpt-5)
-   [GLM-5.2 vs Qwen3.7 Max](https://noometry.com/compare/glm-5-2-vs-qwen3-7-max)
-   [GLM-5.2 vs Muse Spark](https://noometry.com/compare/glm-5-2-vs-muse-spark)
-   [GLM-5.2 vs GLM-5.3-Flash](https://noometry.com/compare/glm-5-2-vs-glm-5-3-flash)
-   [GLM-5.2 vs Claude Opus 4.5](https://noometry.com/compare/claude-opus-4-5-vs-glm-5-2)
-   [GLM-5.2 vs GPT-6 Astra](https://noometry.com/compare/glm-5-2-vs-gpt-6-astra)
-   [GLM-5.2 vs Claude Fable 5.1](https://noometry.com/compare/claude-fable-5-1-vs-glm-5-2)
-   [GLM-5.2 vs Gemini 3.8 Flash](https://noometry.com/compare/gemini-3-8-flash-vs-glm-5-2)
-   [GLM-5.2 vs Kimi K3](https://noometry.com/compare/glm-5-2-vs-kimi-k3)
-   [GLM-5.2 vs Grok 4.6](https://noometry.com/compare/glm-5-2-vs-grok-4-6)
-   [GLM-5.2 vs Qwen3.8 Max](https://noometry.com/compare/glm-5-2-vs-qwen3-8-max)
-   [GLM-5.2 vs Muse Spark 1.3](https://noometry.com/compare/glm-5-2-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-5.1](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-5.2?

GLM-5.2 by Z.ai (Zhipu) ranks 44th of 354 ranked models on the Noometry Index as of October 2026, with a score of 51.1. Its strongest category is writing & preference, where it ranks 21st. 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-5.2 cost?

GLM-5.2 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-5.2's context window?

GLM-5.2 accepts up to 1M tokens of input and can write up to 131K tokens in one response.

### Is GLM-5.2 open source?

Yes. GLM-5.2's weights are downloadable from Hugging Face (zai-org/GLM-5.2); check the license for commercial terms.

### How fast is GLM-5.2?

GLM-5.2 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-5.2's strengths and weaknesses?

Relative to other ranked models, GLM-5.2 places best in writing & preference, multilingual, instruction following and lowest in agentic & tool use, reasoning, long context.

### What is GLM-5.2 best at?

Its best category is writing & preference, where it ranks 21st on Noometry.

### Cite this page

Noometry. (2026). GLM-5.2 benchmarks and pricing. Retrieved October 10, 2026, from https://noometry.com/models/glm-5-2

Quote Noometry with a link back to this page. It is also available in [Markdown](https://noometry.com/md/models/glm-5-2.md).