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AITier

GLM-4.6 (Reasoning)

Z AIGLM開源權重MIT · 商用許可

描述

GLM-4.6 is the latest version of Z.ai's flagship model, bringing significant improvements over GLM-4.5. Key features include: 200K token context window (expanded from 128K), superior coding performance with better real-world application in Claude Code/Cline/Roo Code/Kilo Code, advanced reasoning with tool use during inference, stronger agent capabilities, and refined writing aligned with human preferences. GLM-4.6 achieves competitive performance with DeepSeek-V3.2-Exp and Claude Sonnet 4, reaching near parity with Claude Sonnet 4 (48.6% win rate) on CC-Bench real-world coding tasks.

發布日期
2025-09-30
參數規模
357.0B
上下文長度
205K
支援模態
image, text, video

能力雷達圖

43
general
53
coding
85
reasoning
51
science
40
agents
20
multimodal

排行榜排名

領域#排名分數來源
智慧體能力模型榜67
47.0
LS
程式碼能力榜174
59.0
AA
通用能力榜169
60.0
AA
科學能力195
56.0
AA

基準測試分數 (LLM Stats)

(LLM Stats (zeroeval))

Agents

BrowseCompOpenAI (2025)45.1%自報
Terminal-Bench40.5%自報

Biology

GPQANYU + Cohere + Anthropic (2023)81.0%自報

Code

SWE-Bench Verified68.0%自報

General

LiveCodeBench v682.8%自報

Math

AIME 202593.9%自報
Humanity's Last Exam17.2%自報

AA 評測指數

(Artificial Analysis)
Math Index(Artificial Analysis)
86.0
Coding Index(Artificial Analysis)
45.8
Intelligence Index(Artificial Analysis)
29.3
Aime 25(MAA (Mathematical Association of America))
0.9
Mmlu Pro(TIGER-Lab (Univ. of Waterloo, Toronto, CMU, 2024))
0.8
Gpqa(NYU + Cohere + Anthropic (2023))
0.8
Tau2(Sierra + U Toronto + Vector Institute (2025))
0.7
Livecodebench(UC Berkeley + MIT + Cornell (2024))
0.7
Lcr(Artificial Analysis)
0.6
Terminalbench V2 1
0.5
Ifbench(Google Research (2023))
0.4
Scicode(UIUC + Argonne National Lab (2024))
0.4
Terminalbench Hard(Stanford × Laude Institute (2026))
0.3
Hle(Center for AI Safety + Scale AI (2025))
0.1
Tau Banking
0.1

LLM Stats 分類評分

(LLM Stats (zeroeval))
Physics
80
Biology
80
Chemistry
80
Frontend Development
70
Math
60
Reasoning
60
General
60
Search
50
Code
50
Agents
40
Vision
20

定價

輸入價格$0.55 / 1M tokens
輸出價格$2.2 / 1M tokens
混合價格(3:1)$0.963 / 1M tokens
快取讀取價格$0.11 / 1M tokens
快取寫入價格免費

速度

Tokens/秒0.0
首Token延遲0.00s
首回答延遲0.00s

供應商價格排行

供應商價格排行

1 個供應商

供應商輸入輸出
1Z AI主要
$0.55
$2.2

比較該模型在不同 API 供應商之間的定價。

外部連結

LLM StatsArtificial Analysis