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GLM-4.7 (Reasoning)

Z AIGLM開源權重MIT · 商用許可

描述

GLM 4.7 is a coding‑centric model that thinks before acting, preserves its reasoning across turns, and lets you control thinking per request for speed or accuracy. It upgrades agentic workflows with stronger multi‑step tool use, better terminal and multilingual coding, and a noticeable jump in UI output quality for modern, clean webpages and slides. You can use it in popular coding agents, call it via the Z.ai API, and even run it locally with public weights on HuggingFace and ModelScope using vLLM or SGLang.

發布日期
2025-12-22
參數規模
358.0B
上下文長度
205K
支援模態
text

能力雷達圖

42
general
62
coding
93
reasoning
68
science
60
agents
0
multimodal

排行榜排名

領域#排名分數來源
程式碼能力榜202
67.0
AA
通用能力榜94
68.0
AA
科學能力154
66.0
AA

基準測試分數 (LLM Stats)

(LLM Stats (zeroeval))

General

Tau-bench87.4%自報

Language

MMLU-Pro84.3%自報

Math

AIME 202595.7%自報
IMO-AnswerBench82.0%自報

Reasoning

GPQANYU + Cohere + Anthropic (2023)85.7%自報
LiveCodeBench v684.9%自報
SWE-Bench Verified73.8%自報
SWE-bench Multilingual66.7%自報
BrowseComp-zh66.6%自報
BrowseCompOpenAI (2025)52.0%自報
Humanity's Last Exam42.8%自報
Terminal-Bench 2.0Stanford × Laude Institute (2026)41.0%自報
Terminal-Bench33.3%自報

AA 評測指數

(Artificial Analysis)
Tau2(Sierra + U Toronto + Vector Institute (2025))
95.9
Math Index(Artificial Analysis)
95.0
Aime 25(MAA (Mathematical Association of America))
95.0
Livecodebench(UC Berkeley + MIT + Cornell (2024))
89.4
Gpqa(NYU + Cohere + Anthropic (2023))
85.9
Mmlu Pro(TIGER-Lab (Univ. of Waterloo, Toronto, CMU, 2024))
85.6
Lcr(Artificial Analysis)
71.0
Ifbench(Google Research (2023))
67.9
Terminalbench V2 1
45.3
Coding Index(Artificial Analysis)
45.3
Terminalbench Hard(Stanford × Laude Institute (2026))
31.8
Hle(Center for AI Safety + Scale AI (2025))
27.4
Intelligence Index(Artificial Analysis)
22.2
Tau Banking
12.2

LLM Stats 分類評分

(LLM Stats (zeroeval))
Physics
90
Biology
90
Chemistry
90
Language
80
Legal
80
Math
80
Finance
80
Healthcare
80
Reasoning
70
Frontend Development
70
General
70
Search
60
Tool Calling
60
Agents
50
Code
50
Vision
40

定價

輸入價格$0.6 / 1M tokens
輸出價格$2.2 / 1M tokens
混合價格(3:1)$1 / 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

2 個供應商

最便宜: DeepInfra最貴: Z AI
供應商輸入輸出
1DeepInfra最便宜
$0
$0
2Z AI主要
$0.6
$2.2

比較該模型在不同 API 供應商之間的定價。

外部連結