Kimi K3 leads on the shared-benchmark average
Ahead on 12 of 13 shared benchmarks, averaging 9.0 points higher
Summarised only from the 13 percentage-scale benchmarks scored by every selected model; details are below. A further 2 Elo/rating-scale benchmarks are left out of the average — their scale cannot be added to percentages.
“Best available” takes each model’s highest recorded non-parallel mode per benchmark, so it may combine modes into a virtual configuration that does not exist. Read it with the mode breakdown.

Kimi K3
Moonshot AI
Benchmark-by-benchmark comparison. Changing the thinking mode or tool filters updates the chart and table below.
“Best available” picks the highest non-parallel mode separately for each benchmark. The resulting series can combine several reasoning levels and is not one reproducible runtime configuration. Choose a mode filter to compare like-for-like runs.
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: 编程与软件工程 +15.1 / Relative gap: none clear
Relative edge: none clear / Relative gap: 编程与软件工程 -15.1
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.
15 benchmarks with comparable scores. Each model shows its best score; mode label is displayed below.
| Benchmark | Kimi K3 | GLM-5.2 |
|---|---|---|
HLE 综合评估 | 56.00Thinking Level · High | Tools | 54.70Thinking Enabled | Tools |
GPQA Diamond 科学与综合推理 | 93.50Thinking Level · High | 91.86Thinking Level · High |
Creative Writing 写作和创作 | 2070.80Standard Mode | 1750.90Standard Mode |
SimpleBench 常识推理 | 60.70Thinking Level · High | 58.80Standard Mode |
Context Arena 文本向量检索 | 71.75Thinking Level · High | 72.34Thinking Level · High |
MCP-Atlas AI Agent - 工具使用 | 84.20Thinking Level · High | Tools | 76.80Thinking Enabled | Tools |
Terminal-Bench 2.1 AI Agent - 工具使用 | 88.30Thinking Level · High | Tools | 81.00Thinking Level · High | Tools |
DeepSWE 编程与软件工程 | 67.50Thinking Level · High | Tools | 44.00Deep Thinking Mode | Tools |
FrontierSWE 编程与软件工程 | 81.20Thinking Level · High | Tools | 74.40Thinking Level · High | Tools |
PostTrain Bench 编程与软件工程 | 36.60Thinking Level · High | Tools | 34.30Thinking Level · High | Tools |
Program Bench 编程与软件工程 | 77.80Thinking Level · High | Tools | 63.70Thinking Enabled | Tools |
SWE-Marathon 编程与软件工程 | 42.00Thinking Level · High | Tools | 13.00Thinking Level · High | Tools |
Text Arena (Coding) 编程与软件工程 | 1681.75Thinking Level · High | 1593.25Thinking Level · High |
39.02Thinking Level · High | 29.27Thinking Level · High | |
FrontierMath v2 数学推理 | 72.18Thinking Level · High | 59.21Thinking Level · High |
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 | Kimi K3Moonshot AI | GLM-5.2智谱AI |
|---|---|---|
Core specsRelease | 2026-07-16 | 2026-06-13 |
Context length | 1M | 1M |
Total parameters | 2.8T | 753.33B |
Active parameters | 104B | 40B |
Max output length | 1,048,576 tokens | 128,000 tokens |
Architecture | MoE (mixture of experts) | MoE (mixture of experts) |
Runtime modes | 低高最高 | 关闭高最高 |
Availability & licensingCode availability | Available · Kimi K3 License | Available · MIT License |
Weight availability | Available · Kimi K3 License | Available · MIT License |
Use & commercial terms | 有条件免费商用授权 | 免费商用授权 |
Local deploymentWeight size | 约 1.42 TiB(MXFP4,96 个 Safetensors 分片) | 约 1.5 TB(BF16 safetensors) |
VRAM for weights | ≈ 1454 GB (weights only, excludes KV cache) | ≈ 1500 GB (weights only, excludes KV cache) |
Weights | Hugging Face | Hugging Face |
Source repo | GitHub | GitHub |
API modality supportText Input/Output | Input:YesOutput:Yes | Input:YesOutput:Yes |
Image Input/Output | Input:YesOutput:No | Input:NoOutput:No |
Video Input/Output | Input:YesOutput:No | Input:NoOutput:No |
ResourcesPaper / report | Kimi K3 技术报告 | GLM-5: from Vibe Coding to Agentic Engineering |

GLM-5.2
智谱AI