Z.ai (Zhipu), open weights
GLM-4.7
GLM-4.7 by Z.ai (Zhipu) ranks 124th of 354 ranked models on the Noometry Index as of October 2026, with a score of 42.0. Its strongest category is coding, where it ranks 79th. API pricing starts at $0.60 per million input tokens and $2.20 per million output tokens, with a 205K-token context window.
Last verified
Specifications
- Noometry rank
- #124 of 354
- Index score
- 42.0
- Evidence
- Confirmed 36 results
- Provider
- Z.ai (Zhipu)
- Released
- December 22, 2025
- Weights
- Open weights
- Reasoning
- Yes
- Context window
- 205K
- Max output
- 131K
- Input price
- $0.60 / M
- Output price
- $2.20 / M
- Blended price
- $1 / M
- Output speed
- Not measured
- Value
- #110 of 219
- Knowledge cutoff
- April 2025
- Input
- text
- Hugging Face
- zai-org/GLM-4.7
Category scores
Each category score combines every public result we have in that category.
- Coding 44.0
- Agentic & Tool Use 26.5
- Reasoning 24.3
- Math 38.6
- Knowledge 47.0
- Multilingual 52.8
- Instruction Following 74.4
- Long Context 42.8
- Writing & Preference 60.9
| Category | Score | Rank | Results |
|---|---|---|---|
| Coding | 44.0 | #79 | 3 |
| Agentic & Tool Use | 26.5 | #103 | 1 |
| Reasoning | 24.3 | #164 | 4 |
| Math | 38.6 | #135 | 3 |
| Knowledge | 47.0 | #80 | 4 |
| Multilingual | 52.8 | #79 | 1 |
| Instruction Following | 74.4 | #95 | 1 |
| Long Context | 42.8 | #116 | 3 |
| Writing & Preference | 60.9 | #93 | 4 |
Strengths and weaknesses
Categories where GLM-4.7 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 |
|---|---|---|---|
| Coding | 44.0 | +5.2 | #79 of 340, top 24% |
| Knowledge | 47.0 | +9.7 | #80 of 314, top 26% |
| Multilingual | 52.8 | +5.4 | #79 of 297, top 27% |
Weakest categories
| Category | Score | vs median | Rank |
|---|---|---|---|
| Agentic & Tool Use | 26.5 | −3.8 | #103 of 154, top 67% |
| Reasoning | 24.3 | +0.7 | #164 of 350, top 47% |
| Math | 38.6 | +2.0 | #135 of 327, top 42% |
Closest competitors
The models ranked just above and below GLM-4.7. When scores are this close, price and speed are often the better way to choose.
| Model | Rank | Score | Blended $/M | Speed | |
|---|---|---|---|---|---|
| Longcat Flash Chat | #120 | 42.1 | — | 69 | Compare |
| Solar Pro4 | #121 | 42.1 | $0.52 | — | Compare |
| GLM-4.5 | #122 | 42.0 | $1 | 32 | Compare |
| Qwen3.5 35B-A3B | #123 | 42.0 | $0.69 | — | Compare |
| GPT-5.4 nano | #125 | 41.9 | $0.46 | 19 | Compare |
| Amazon Nova Experimental Chat 10 09 | #126 | 41.9 | — | — | Compare |
| Qwen3.5 27B | #127 | 41.9 | $0.82 | — | Compare |
| GPT-5 Mini | #128 | 41.8 | $0.69 | 3 | Compare |
Sponsored placements are available on pages like this one. Advertise on Noometry
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 |
|---|---|---|---|---|---|
| LMArena WebDev | 1435 | #62 of 113, top 55% | LMArena | 2026-10-08 | |
| SciCode | 45.1% | #59 of 121, top 49% | Epoch AI | ||
| LMArena Coding | 1454 | #79 of 294, top 27% | LMArena | 2026-10-08 | |
| ALE-Bench | 399.48 | #90 of 105, top 86% | Epoch AI |
Agentic & Tool Use
| Benchmark | Score | Position | Setting | Source | Date |
|---|---|---|---|---|---|
| Terminal-Bench | 33.4% | #30 of 41, top 74% | Epoch AI | ||
| Vending-Bench 2 | 2,377 | #42 of 60, top 70% | Epoch AI |
Reasoning
| Benchmark | Score | Position | Setting | Source | Date |
|---|---|---|---|---|---|
| SimpleBench | 47.7% | #42 of 77, top 55% | Epoch AI | ||
| SimpleBench | 47.7% | #42 of 77, top 55% | Epoch AI | ||
| CritPt | 1.7% | #74 of 134, top 56% | Epoch AI | ||
| Chess Puzzles | 6% | #89 of 129, top 69% | Epoch AI | 2026-01-29 | |
| LMArena Hard Prompts | 1443 | #78 of 297, top 27% | LMArena | 2026-10-08 | |
| Epoch Capabilities Index | 143.51 | #91 of 213, top 43% | Epoch AI | 2025-12-22 |
Math
| Benchmark | Score | Position | Setting | Source | Date |
|---|---|---|---|---|---|
| OTIS Mock AIME 2024-2025 | 83.3% | #75 of 173, top 44% | Epoch AI | 2026-01-29 | |
| ProofBench | 6% | #67 of 77, top 88% | Epoch AI | ||
| LMArena Math | 1423 | #97 of 285, top 35% | LMArena | 2026-10-08 | |
| FrontierMath (Feb 2025 set) | 2.4% | #53 of 68, top 78% | Epoch AI | 2026-01-30 | |
| FrontierMath Tier 4 (v1) | 0% | #49 of 55, top 90% | Epoch AI | 2026-01-30 |
Knowledge
| Benchmark | Score | Position | Setting | Source | Date |
|---|---|---|---|---|---|
| GPQA Diamond | 83.3% | #71 of 186, top 39% | Epoch AI | 2026-01-29 | |
| SimpleQA Verified | 32.2% | #55 of 77, top 72% | Epoch AI | 2026-08-27 | |
| Vectara Hallucination Rate (lower is better) | 11.7% | #69 of 96, top 72% | Vectara Hallucination Leaderboard | ||
| LMArena Expert | 1424 | #103 of 273, top 38% | LMArena | 2026-10-08 |
Multilingual
| Benchmark | Score | Position | Setting | Source | Date |
|---|---|---|---|---|---|
| LMArena Non-English | 1417 | #79 of 297, top 27% | LMArena | 2026-10-08 | |
| LMArena Chinese | 1495 | #50 of 285, top 18% | LMArena | 2026-10-08 | |
| LMArena French | 1432 | #89 of 223, top 40% | LMArena | 2026-10-08 | |
| LMArena German | 1424 | #72 of 231, top 32% | LMArena | 2026-10-08 | |
| LMArena Japanese | 1439 | #30 of 211, top 15% | LMArena | 2026-10-08 | |
| LMArena Korean | 1399 | #55 of 213, top 26% | LMArena | 2026-10-08 | |
| LMArena Russian | 1423 | #77 of 283, top 28% | LMArena | 2026-10-08 | |
| LMArena Spanish | 1434 | #74 of 226, top 33% | LMArena | 2026-10-08 |
Instruction Following
| Benchmark | Score | Position | Setting | Source | Date |
|---|---|---|---|---|---|
| LMArena Instruction Following | 1411 | #85 of 298, top 29% | LMArena | 2026-10-08 |
Long Context
| Benchmark | Score | Position | Setting | Source | Date |
|---|---|---|---|---|---|
| CL-bench | 15.9% | #14 of 19, top 74% | Epoch AI | ||
| CL-bench Life | 10.9% | #10 of 13, top 77% | Epoch AI | ||
| LMArena Longer Query | 1432 | #76 of 291, top 27% | LMArena | 2026-10-08 |
Writing & Preference
| Benchmark | Score | Position | Setting | Source | Date |
|---|---|---|---|---|---|
| LMArena Text | 1435 | #72 of 297, top 25% | LMArena | 2026-10-08 | |
| LMArena Creative Writing | 1401 | #81 of 295, top 28% | LMArena | 2026-10-08 | |
| EQ-Bench Creative Writing | 1413 | #68 of 115, top 60% | EQ-Bench | ||
| LMArena Multi-Turn | 1446 | #62 of 295, top 22% | LMArena | 2026-10-08 |
API pricing by provider
| Route | Input $/M | Output $/M | Cached input $/M | Checked |
|---|---|---|---|---|
| bedrock | $0.60 | $2.20 | — | 2026-10-10 |
| deepinfra | $0.40 | $1.75 | $0.08 | 2026-10-10 |
| openrouter | $0.60 | $2.20 | $0.11 | 2026-10-10 |
| vertex | $0.60 | $2.20 | $0.06 | 2026-10-10 |
| zai | $0.60 | $2.20 | $0.11 | 2026-10-10 |
Compare GLM-4.7
- GLM-4.7 vs GLM-4.6V
- GLM-4.7 vs Qwen3.5 35B-A3B
- GLM-4.7 vs GPT-5.4 nano
- GLM-4.7 vs GLM-4.5
- GLM-4.7 vs Amazon Nova Experimental Chat 10 09
- GLM-4.7 vs Solar Pro4
- GLM-4.7 vs Qwen3.5 27B
- GLM-4.7 vs GPT-6 Astra
- GLM-4.7 vs Claude Fable 5.1
- GLM-4.7 vs Gemini 3.8 Flash
- GLM-4.7 vs Kimi K3
- GLM-4.7 vs Grok 4.6
- GLM-4.7 vs Qwen3.8 Max
- GLM-4.7 vs Muse Spark 1.3
Other Z.ai (Zhipu) models
- GLM-5.354.8
- GLM-5.3-Flash51.8
- GLM-5.251.1
- GLM-5.147.8
- GLM-546.1
- GLM-5V-Turbo43.8
- GLM-4.542.0
- GLM-4.641.4
Frequently asked questions
How good is GLM-4.7?
GLM-4.7 by Z.ai (Zhipu) ranks 124th of 354 ranked models on the Noometry Index as of October 2026, with a score of 42.0. Its strongest category is coding, where it ranks 79th. API pricing starts at $0.60 per million input tokens and $2.20 per million output tokens, with a 205K-token context window.
How much does GLM-4.7 cost?
GLM-4.7 costs $0.60 per million input tokens and $2.20 per million output tokens on Z.ai (Zhipu)'s own API, with cached input at $0.11.
What is GLM-4.7's context window?
GLM-4.7 accepts up to 205K tokens of input and can write up to 131K tokens in one response.
Is GLM-4.7 open source?
Yes. GLM-4.7's weights are downloadable from Hugging Face (zai-org/GLM-4.7); check the license for commercial terms.
What are GLM-4.7's strengths and weaknesses?
Relative to other ranked models, GLM-4.7 places best in coding, knowledge, multilingual and lowest in agentic & tool use, reasoning, math.
What is GLM-4.7 best at?
Its best category is coding, where it ranks 79th on Noometry.