Z.ai (Zhipu), open weights
GLM-5
GLM-5 by Z.ai (Zhipu) ranks 66th of 354 ranked models on the Noometry Index as of October 2026, with a score of 46.1. Its strongest category is writing & preference, where it ranks 38th. API pricing starts at $1 per million input tokens and $3.20 per million output tokens, with a 205K-token context window.
Last verified
Specifications
- Noometry rank
- #66 of 354
- Index score
- 46.1
- Evidence
- Confirmed 45 results
- Provider
- Z.ai (Zhipu)
- Released
- February 11, 2026
- Weights
- Open weights
- Reasoning
- Yes
- Context window
- 205K
- Max output
- 131K
- Input price
- $1 / M
- Output price
- $3.20 / M
- Blended price
- $1.55 / M
- Output speed
- 23 tokens/s Kagi
- Value
- #134 of 219
- Knowledge cutoff
- December 2025
- Input
- text
- Hugging Face
- zai-org/GLM-5
Category scores
Each category score combines every public result we have in that category.
- Coding 49.0
- Agentic & Tool Use 31.1
- Reasoning 27.6
- Math 46.4
- Knowledge 52.3
- Multilingual 53.7
- Instruction Following 75.2
- Long Context 44.7
- Writing & Preference 66.0
| Category | Score | Rank | Results |
|---|---|---|---|
| Coding | 49.0 | #52 | 6 |
| Agentic & Tool Use | 31.1 | #71 | 5 |
| Reasoning | 27.6 | #116 | 7 |
| Math | 46.4 | #71 | 3 |
| Knowledge | 52.3 | #64 | 3 |
| Multilingual | 53.7 | #58 | 1 |
| Instruction Following | 75.2 | #67 | 1 |
| Long Context | 44.7 | #60 | 2 |
| Writing & Preference | 66.0 | #38 | 4 |
Strengths and weaknesses
Categories where GLM-5 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 | 66.0 | +12.2 | #38 of 312, top 13% |
| Coding | 49.0 | +10.3 | #52 of 340, top 16% |
| Multilingual | 53.7 | +6.3 | #58 of 297, top 20% |
Weakest categories
| Category | Score | vs median | Rank |
|---|---|---|---|
| Agentic & Tool Use | 31.1 | +0.7 | #71 of 154, top 47% |
| Reasoning | 27.6 | +4.0 | #116 of 350, top 34% |
| Instruction Following | 75.2 | +3.9 | #67 of 305, top 22% |
Closest competitors
The models ranked just above and below GLM-5. When scores are this close, price and speed are often the better way to choose.
| Model | Rank | Score | Blended $/M | Speed | |
|---|---|---|---|---|---|
| Qwen3.6 Plus | #62 | 47.5 | $1.13 | — | Compare |
| Inkling-Small | #63 | 46.5 | $0.64 | — | Compare |
| GPT-5 Pro | #64 | 46.4 | $41.25 | 5 | Compare |
| Grok 4.20 Multi-Agent | #65 | 46.2 | $1.56 | — | Compare |
| Qwen3.5 397B-A17B | #67 | 46.0 | $1.35 | 9 | Compare |
| Qwen3.8 27B | #68 | 46.0 | $1.11 | — | Compare |
| GPT-5.3 Codex | #69 | 45.8 | $4.81 | — | Compare |
| Kimi K2 Thinking Turbo | #70 | 45.8 | — | — | 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 |
|---|---|---|---|---|---|
| SWE-bench Verified | 72.1% | #22 of 32, top 69% | Epoch AI | 2026-02-15 | |
| SWE-bench Verified (bash only) | 72.8% | #6 of 39, top 16% | high | SWE-bench | 2026-02-17 |
| LMArena WebDev | 1434 | #63 of 113, top 56% | LMArena | 2026-10-08 | |
| SWE-bench Multilingual | 69.7% | #4 of 13, top 31% | SWE-bench | 2026-02-13 | |
| WeirdML | 48.2% | #50 of 119, top 43% | Epoch AI | ||
| LMArena Coding | 1461 | #69 of 294, top 24% | LMArena | 2026-10-08 | |
| ALE-Bench | 765.62 | #63 of 105, top 60% | Epoch AI |
Agentic & Tool Use
| Benchmark | Score | Position | Setting | Source | Date |
|---|---|---|---|---|---|
| Terminal-Bench | 52.4% | #16 of 41, top 40% | Epoch AI | ||
| τ²-bench Airline | 82.5% | #4 of 7, top 58% | enabled | τ²-bench | 2026-03-02 |
| τ²-bench Banking | 9.8% | #25 of 26, top 97% | enabled | τ²-bench | 2026-03-02 |
| τ²-bench Retail | 73.7% | #6 of 7, top 86% | enabled | τ²-bench | 2026-03-02 |
| τ²-bench Telecom | 86.8% | #6 of 7, top 86% | enabled | τ²-bench | 2026-03-02 |
| Vending-Bench 2 | 4,432 | #33 of 60, top 56% | Epoch AI |
Reasoning
| Benchmark | Score | Position | Setting | Source | Date |
|---|---|---|---|---|---|
| ARC-AGI-2 | 4.9% | #57 of 83, top 69% | Epoch AI | ||
| SimpleBench | 53.2% | #36 of 77, top 47% | Epoch AI | ||
| Kagi LLM Benchmark | 75% | #12 of 99, top 13% | Kagi LLM Benchmark | ||
| Kagi LLM Benchmark | 51.7% | Kagi LLM Benchmark | |||
| NYT Connections (extended) | 74.8% | #43 of 91, top 48% | Lech Mazur benchmarks | ||
| ARC-AGI-1 | 44.7% | #59 of 83, top 72% | Epoch AI | ||
| Chess Puzzles | 10% | #82 of 129, top 64% | Epoch AI | 2026-02-12 | |
| LMArena Hard Prompts | 1452 | #58 of 297, top 20% | LMArena | 2026-10-08 | |
| Epoch Capabilities Index | 145.83 | #78 of 213, top 37% | Epoch AI | 2026-02-11 | |
| ForecastBench | 61 | #18 of 72, top 25% | Epoch AI |
Math
| Benchmark | Score | Position | Setting | Source | Date |
|---|---|---|---|---|---|
| MathArena Final-Answer Competitions | 65.7% | #20 of 29, top 69% | MathArena | ||
| OTIS Mock AIME 2024-2025 | 80% | #81 of 173, top 47% | Epoch AI | 2026-02-12 | |
| LMArena Math | 1440 | #73 of 285, top 26% | LMArena | 2026-10-08 | |
| FrontierMath (Feb 2025 set) | 16.4% | #33 of 68, top 49% | Epoch AI | 2026-02-19 | |
| FrontierMath Tier 4 (v1) | 2.1% | #38 of 55, top 70% | Epoch AI | 2026-02-19 |
Knowledge
| Benchmark | Score | Position | Setting | Source | Date |
|---|---|---|---|---|---|
| GPQA Diamond | 87.8% | #51 of 186, top 28% | Epoch AI | 2026-02-12 | |
| Vectara Hallucination Rate (lower is better) | 10.1% | #55 of 96, top 58% | Vectara Hallucination Leaderboard | ||
| LMArena Expert | 1454 | #67 of 273, top 25% | LMArena | 2026-10-08 |
Multilingual
| Benchmark | Score | Position | Setting | Source | Date |
|---|---|---|---|---|---|
| LMArena Non-English | 1430 | #58 of 297, top 20% | LMArena | 2026-10-08 | |
| LMArena Chinese | 1511 | #35 of 285, top 13% | LMArena | 2026-10-08 | |
| LMArena French | 1455 | #60 of 223, top 27% | LMArena | 2026-10-08 | |
| LMArena German | 1445 | #52 of 231, top 23% | LMArena | 2026-10-08 | |
| LMArena Japanese | 1416 | #43 of 211, top 21% | LMArena | 2026-10-08 | |
| LMArena Korean | 1423 | #35 of 213, top 17% | LMArena | 2026-10-08 | |
| LMArena Russian | 1436 | #56 of 283, top 20% | LMArena | 2026-10-08 | |
| LMArena Spanish | 1454 | #48 of 226, top 22% | LMArena | 2026-10-08 |
Instruction Following
| Benchmark | Score | Position | Setting | Source | Date |
|---|---|---|---|---|---|
| LMArena Instruction Following | 1428 | #63 of 298, top 22% | LMArena | 2026-10-08 |
Long Context
| Benchmark | Score | Position | Setting | Source | Date |
|---|---|---|---|---|---|
| CL-bench | 18.7% | #10 of 19, top 53% | Epoch AI | ||
| LMArena Longer Query | 1446 | #57 of 291, top 20% | LMArena | 2026-10-08 |
Writing & Preference
| Benchmark | Score | Position | Setting | Source | Date |
|---|---|---|---|---|---|
| LMArena Text | 1446 | #51 of 297, top 18% | LMArena | 2026-10-08 | |
| LMArena Creative Writing | 1439 | #39 of 295, top 14% | LMArena | 2026-10-08 | |
| EQ-Bench Creative Writing | 1601 | #42 of 115, top 37% | EQ-Bench | ||
| LMArena Multi-Turn | 1456 | #42 of 295, top 15% | LMArena | 2026-10-08 |
API pricing by provider
Compare GLM-5
- GLM-5 vs GLM-4.7
- GLM-5 vs Grok 4.20 Multi-Agent
- GLM-5 vs Qwen3.5 397B-A17B
- GLM-5 vs GPT-5 Pro
- GLM-5 vs Qwen3.8 27B
- GLM-5 vs Inkling-Small
- GLM-5 vs GPT-5.3 Codex
- GLM-5 vs GPT-6 Astra
- GLM-5 vs Claude Fable 5.1
- GLM-5 vs Gemini 3.8 Flash
- GLM-5 vs Kimi K3
- GLM-5 vs Grok 4.6
- GLM-5 vs Qwen3.8 Max
- GLM-5 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-5V-Turbo43.8
- GLM-4.542.0
- GLM-4.742.0
- GLM-4.641.4
Frequently asked questions
How good is GLM-5?
GLM-5 by Z.ai (Zhipu) ranks 66th of 354 ranked models on the Noometry Index as of October 2026, with a score of 46.1. Its strongest category is writing & preference, where it ranks 38th. API pricing starts at $1 per million input tokens and $3.20 per million output tokens, with a 205K-token context window.
How much does GLM-5 cost?
GLM-5 costs $1 per million input tokens and $3.20 per million output tokens on Z.ai (Zhipu)'s own API, with cached input at $0.20.
What is GLM-5's context window?
GLM-5 accepts up to 205K tokens of input and can write up to 131K tokens in one response.
Is GLM-5 open source?
Yes. GLM-5's weights are downloadable from Hugging Face (zai-org/GLM-5); check the license for commercial terms.
How fast is GLM-5?
GLM-5 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's strengths and weaknesses?
Relative to other ranked models, GLM-5 places best in writing & preference, coding, multilingual and lowest in agentic & tool use, reasoning, instruction following.
What is GLM-5 best at?
Its best category is writing & preference, where it ranks 38th on Noometry.