Z.ai (Zhipu), open weights
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.
Last verified
Specifications
- Noometry rank
- #44 of 354
- Index score
- 51.1
- Evidence
- Confirmed 51 results
- Provider
- Z.ai (Zhipu)
- 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
- Value
- #146 of 219
- Knowledge cutoff
- Unknown
- Input
- text
- Hugging Face
- zai-org/GLM-5.2
Category scores
Each category score combines every public result we have in that category.
- Coding 51.3
- Agentic & Tool Use 32.4
- Reasoning 42.3
- Math 55.7
- Knowledge 57.1
- Multilingual 55.8
- Instruction Following 76.9
- Long Context 45.3
- Writing & Preference 70.4
| Category | Score | Rank | Results |
|---|---|---|---|
| Coding | 51.3 | #41 | 7 |
| Agentic & Tool Use | 32.4 | #63 | 4 |
| Reasoning | 42.3 | #52 | 13 |
| Math | 55.7 | #43 | 6 |
| Knowledge | 57.1 | #40 | 3 |
| Multilingual | 55.8 | #26 | 1 |
| Instruction Following | 76.9 | #34 | 1 |
| Long Context | 45.3 | #43 | 1 |
| Writing & Preference | 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
| Category | Score | vs median | Rank |
|---|---|---|---|
| Writing & Preference | 70.4 | +16.7 | #21 of 312, top 7% |
| Multilingual | 55.8 | +8.4 | #26 of 297, top 9% |
| Instruction Following | 76.9 | +5.7 | #34 of 305, top 12% |
Weakest categories
| Category | Score | vs median | Rank |
|---|---|---|---|
| Agentic & Tool Use | 32.4 | +2.1 | #63 of 154, top 41% |
| Reasoning | 42.3 | +18.7 | #52 of 350, top 15% |
| 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.
| Model | Rank | Score | Blended $/M | Speed | |
|---|---|---|---|---|---|
| Gemini 3 Flash Preview | #40 | 52.3 | $1.13 | — | Compare |
| GLM-5.3-Flash | #41 | 51.8 | $0.24 | — | Compare |
| Qwen3.7 Max | #42 | 51.5 | $3.75 | — | Compare |
| Qwen3.6 Max Preview | #43 | 51.5 | $2.92 | — | Compare |
| GPT-5 | #45 | 50.9 | $3.44 | 2 | Compare |
| Muse Spark | #46 | 50.6 | — | — | Compare |
| Claude Opus 4.5 | #47 | 50.5 | $10 | 13 | Compare |
| Muse Spark 1.2 | #48 | 50.3 | $2 | — | Compare |
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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
| Benchmark | Score | Position | Setting | Source | Date |
|---|---|---|---|---|---|
| SWE-bench Verified | 78.7% | #5 of 32, top 16% | max | Epoch AI | 2026-06-25 |
| DeepSWE | 36.3% | high | Epoch AI | ||
| DeepSWE | 43.8% | #25 of 29, top 87% | max | Epoch AI | |
| FrontierCode | 24.5% | #30 of 37, top 82% | none | Epoch AI | |
| LMArena WebDev | 1603 | #22 of 113, top 20% | LMArena | 2026-10-08 | |
| SciCode | 50.5% | #41 of 121, top 34% | max | Epoch AI | |
| SciCode | 36.1% | none | Epoch AI | ||
| WeirdML | 67.3% | high | Epoch AI | ||
| WeirdML | 70.1% | #18 of 119, top 16% | max | Epoch AI | |
| LMArena Coding | 1485 | #38 of 294, top 13% | LMArena | 2026-10-08 | |
| ALE-Bench | 1,047 | #40 of 105, top 39% | high | Epoch AI | |
| ALE-Bench | 1,010 | max | Epoch AI |
Agentic & Tool Use
| Benchmark | Score | Position | Setting | Source | Date |
|---|---|---|---|---|---|
| APEX-Agents | 45.2% | #32 of 49, top 66% | Epoch AI | ||
| τ²-bench Banking | 37.1% | #11 of 26, top 43% | xhigh | τ²-bench | 2026-08-04 |
| PostTrainBench | 31.7% | #6 of 11, top 55% | max | Epoch AI | |
| GBAEval | 0% | #21 of 23, top 92% | Epoch AI | ||
| Vending-Bench 2 | 8,314 | #10 of 60, top 17% | Epoch AI |
Reasoning
| Benchmark | Score | Position | Setting | Source | Date |
|---|---|---|---|---|---|
| ARC-AGI-2 | 22.8% | #41 of 83, top 50% | Epoch AI | ||
| SimpleBench | 58.8% | #28 of 77, top 37% | Epoch AI | ||
| Kagi LLM Benchmark | 62.6% | #37 of 99, top 38% | Kagi LLM Benchmark | ||
| Kagi LLM Benchmark | 60% | Kagi LLM Benchmark | |||
| NYT Connections (extended) | 74.3% | #45 of 91, top 50% | high reasoning | Lech Mazur benchmarks | |
| ARC-AGI-1 | 77% | #40 of 83, top 49% | Epoch AI | ||
| CritPt | 20.9% | #21 of 134, top 16% | max | Epoch AI | |
| CritPt | 3.1% | none | Epoch AI | ||
| Chess Puzzles | 14% | low | Epoch AI | 2026-08-10 | |
| Chess Puzzles | 21% | #52 of 129, top 41% | max | Epoch AI | 2026-06-17 |
| Chess Puzzles | 6% | none | Epoch AI | 2026-08-10 | |
| EBR-Bench | 9.5% | #19 of 24, top 80% | max | Epoch AI | 2026-06-29 |
| LMArena Hard Prompts | 1480 | #34 of 297, top 12% | LMArena | 2026-10-08 | |
| Mystery Game Puzzles | 19% | #44 of 74, top 60% | low | Epoch AI | 2026-08-27 |
| Mystery Game Puzzles | 15% | medium | Epoch AI | 2026-08-27 | |
| Mystery Game Puzzles | 18% | minimal | Epoch AI | 2026-08-27 | |
| Mystery Game Puzzles | 18% | none | Epoch AI | 2026-08-27 | |
| DTBench | 93.6% | #25 of 151, top 17% | max | Epoch AI | |
| LMCA | 45.8% | #31 of 125, top 25% | max | Epoch AI | |
| Surface Evolver Bench | 55.6% | #12 of 25, top 48% | high | Epoch AI | |
| Epoch Capabilities Index | 151.78 | #48 of 213, top 23% | Epoch AI | 2026-06-16 |
Math
| Benchmark | Score | Position | Setting | Source | Date |
|---|---|---|---|---|---|
| FrontierMath (Tiers 1-3) | 54.7% | low | Epoch AI | 2026-08-29 | |
| FrontierMath (Tiers 1-3) | 59.2% | #36 of 81, top 45% | max | Epoch AI | 2026-06-19 |
| FrontierMath (Tiers 1-3) | 42.5% | none | Epoch AI | 2026-08-29 | |
| FrontierMath Tier 4 | 29.3% | #30 of 63, top 48% | max | Epoch AI | 2026-06-19 |
| MathArena Final-Answer Competitions | 67.6% | #16 of 29, top 56% | MathArena | ||
| OTIS Mock AIME 2024-2025 | 75.6% | low | Epoch AI | 2026-08-10 | |
| OTIS Mock AIME 2024-2025 | 86.4% | #67 of 173, top 39% | max | Epoch AI | 2026-06-25 |
| OTIS Mock AIME 2024-2025 | 28.9% | none | Epoch AI | 2026-08-10 | |
| ProofBench | 35% | #39 of 77, top 51% | max | Epoch AI | |
| LMArena Math | 1482 | #26 of 285, top 10% | LMArena | 2026-10-08 |
Knowledge
| Benchmark | Score | Position | Setting | Source | Date |
|---|---|---|---|---|---|
| GPQA Diamond | 87.9% | low | Epoch AI | 2026-08-10 | |
| GPQA Diamond | 91.9% | #23 of 186, top 13% | max | Epoch AI | 2026-06-24 |
| GPQA Diamond | 71.2% | none | Epoch AI | 2026-08-10 | |
| SimpleQA Verified | 34.2% | #50 of 77, top 65% | max | Epoch AI | 2026-08-27 |
| LMArena Expert | 1486 | #36 of 273, top 14% | LMArena | 2026-10-08 |
Multilingual
| Benchmark | Score | Position | Setting | Source | Date |
|---|---|---|---|---|---|
| LMArena Non-English | 1459 | #26 of 297, top 9% | LMArena | 2026-10-08 | |
| LMArena Chinese | 1519 | #27 of 285, top 10% | LMArena | 2026-10-08 | |
| LMArena French | 1479 | #27 of 223, top 13% | LMArena | 2026-10-08 | |
| LMArena German | 1468 | #29 of 231, top 13% | LMArena | 2026-10-08 | |
| LMArena Japanese | 1451 | #25 of 211, top 12% | LMArena | 2026-10-08 | |
| LMArena Korean | 1445 | #22 of 213, top 11% | LMArena | 2026-10-08 | |
| LMArena Russian | 1466 | #30 of 283, top 11% | LMArena | 2026-10-08 | |
| LMArena Spanish | 1477 | #15 of 226, top 7% | LMArena | 2026-10-08 |
Instruction Following
| Benchmark | Score | Position | Setting | Source | Date |
|---|---|---|---|---|---|
| LMArena Instruction Following | 1465 | #30 of 298, top 11% | LMArena | 2026-10-08 |
Long Context
| Benchmark | Score | Position | Setting | Source | Date |
|---|---|---|---|---|---|
| LMArena Longer Query | 1479 | #25 of 291, top 9% | LMArena | 2026-10-08 |
Writing & Preference
| Benchmark | Score | Position | Setting | Source | Date |
|---|---|---|---|---|---|
| LMArena Text | 1470 | #27 of 297, top 10% | LMArena | 2026-10-08 | |
| LMArena Creative Writing | 1462 | #18 of 295, top 7% | LMArena | 2026-10-08 | |
| EQ-Bench Creative Writing | 1757 | #26 of 115, top 23% | EQ-Bench | ||
| EQ-Bench 4 | 1222 | #14 of 28, top 50% | EQ-Bench | ||
| LMArena Multi-Turn | 1469 | #31 of 295, top 11% | LMArena | 2026-10-08 |
API pricing by provider
Compare GLM-5.2
- GLM-5.2 vs GLM-5.1
- GLM-5.2 vs Qwen3.6 Max Preview
- GLM-5.2 vs GPT-5
- GLM-5.2 vs Qwen3.7 Max
- GLM-5.2 vs Muse Spark
- GLM-5.2 vs GLM-5.3-Flash
- GLM-5.2 vs Claude Opus 4.5
- GLM-5.2 vs GPT-6 Astra
- GLM-5.2 vs Claude Fable 5.1
- GLM-5.2 vs Gemini 3.8 Flash
- GLM-5.2 vs Kimi K3
- GLM-5.2 vs Grok 4.6
- GLM-5.2 vs Qwen3.8 Max
- GLM-5.2 vs Muse Spark 1.3
Other Z.ai (Zhipu) models
- GLM-5.354.8
- GLM-5.3-Flash51.8
- GLM-5.147.8
- GLM-546.1
- GLM-5V-Turbo43.8
- GLM-4.542.0
- GLM-4.742.0
- GLM-4.641.4
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.