GLM-5.3 leads overall
Ahead on 9 of 11 benchmarks, averaging 5.0 points higher
Summarised from the 11 benchmarks both models were scored on; details in the charts below. A further 1 Elo/rating-scale benchmarks are left out of the average — their scale cannot be added to percentages.

GLM-5.3
智谱AI
Updates live with the mode filters below.
Best overall
GLM-5.3 · 198.34
Best single
GLM-5.3 · GDPval-AA v2 1769.00
Modality coverage
GLM-5.3 · 1 modalities
Head to head
12
Benchmarks
10
Wins
2
Losses
+12.14
Average diff
Benchmark-by-benchmark comparison. Changing the thinking mode or tool filters updates the chart and table below.
Data sourced primarily from official releases (GitHub, Hugging Face, papers), then benchmark leaderboards, then third-party evaluators. Learn about our data methodology
Each axis is the mean percentage score of one benchmark domain. It is an average, not a capability rating.
Relative edge: 综合评估 +13.1 / Relative gap: none clear
Relative edge: none clear / Relative gap: 综合评估 -13.1
Method: for each model and benchmark, all scores in the current mode scope are averaged (not the best score), then those benchmark scores are averaged within each domain. Only benchmarks scored on a 0-100 scale by at least two of the selected models count — Elo and rating-scale benchmarks such as Codeforces or Arena are excluded, because averaging a 1500 rating with an 85% accuracy produces a meaningless number. Missing values are not counted as zero, and the averages are unweighted, so domains with harder benchmarks read lower.
Every model and runtime mode, benchmark by benchmark. Values are comparable along a row, not between different benchmarks.
12 benchmarks with comparable scores. Each model shows its best score; mode label is displayed below.
| Benchmark | GLM-5.3 | Hy4 preview |
|---|---|---|
HLE 综合评估 | 62.50Thinking Level · High | Tools | 55.40Thinking Level · High | Tools |
AutomationBench AI Agent - 工具使用 | 48.20Thinking Level · High | Tools | 32.10Thinking Level · High | Tools |
CyberGym AI Agent - 工具使用 | 84.50Thinking Level · High | Tools | 78.40Thinking Level · High | Tools |
Terminal-Bench 2.1 AI Agent - 工具使用 | 88.20Thinking Level · High | Tools | 85.40Thinking Level · High | Tools |
Toolathlon-Verified AI Agent - 工具使用 | 73.00Thinking Level · High | Tools | 74.10Thinking Level · High | Tools |
DeepSWE 编程与软件工程 | 66.90Thinking Level · High | Tools | 64.30Thinking Level · High | Tools |
NL2Repo-Bench 编程与软件工程 | 58.00Thinking Level · High | Tools | 58.90Thinking Level · High | Tools |
PostTrain Bench 编程与软件工程 | 39.80Thinking Level · High | Tools | 35.60Thinking Level · High | Tools |
Program Bench 编程与软件工程 | 19.00Thinking Level · High | Tools | 17.50Thinking Level · High | Tools |
SWE-Marathon 编程与软件工程 | 42.50Thinking Level · High | Tools | 31.90Thinking Level · High | Tools |
Agents' Last Exam Agent能力评测 | 28.50Thinking Level · High | Tools | 22.80Thinking Level · High | Tools |
GDPval-AA v2 生产力知识 | 1769.00Thinking Level · High | Tools | 1678.00Thinking Level · High | Tools |
Official list prices per model API, split by input and output. Unit: USD per 1M tokens.
Architecture, licensing and API modalities. "Not provided" means the field is missing from our database.
| Features & specs | GLM-5.3智谱AI | Hy4 preview腾讯AI实验室 |
|---|---|---|
Core specsRelease | 2026-08-14 | 2026-08-28 |
Context length | 1M | 1M |
Total parameters | 744B | 770B |
Active parameters | 40B | 49B |
Max output length | 128,000 tokens | Not provided |
Architecture | MoE (mixture of experts) | MoE (mixture of experts) |
Runtime modes | 最高低高 | 高关闭 |
LicenseCode Open Source | Open Source · Apache 2.0 | Open Source · Apache 2.0 |
Weights Open Source | Not provided | Open Source · Apache 2.0 |
Licensing status | Not provided | 免费商用授权 |
Local deploymentWeight size | Not provided | ≈1.42 TiB (BF16, 131 safetensors shards) |
VRAM for weights | Not provided | ≈ 1454 GB (weights only, excludes KV cache) |
Weights | Hugging Face | Hugging Face |
Source repo | GitHub | GitHub |
API modality supportText Input/Output | / | / |
ResourcesPaper / report | GLM-5: from Vibe Coding to Agentic Engineering | Introducing Hy4 preview |

Hy4 preview
腾讯AI实验室