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AITier

GLM-4.7 (Reasoning)

Z AIGLMOpen WeightMIT · Uso Comercial

Descripción

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.

Fecha de lanzamiento
2025-12-22
Parámetros
358.0B
Longitud del contexto
205K
Modalidades
text

Radar de capacidades

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

Rankings

Dominio#PosiciónPuntuaciónFuente
Ranking de codificación202
67.0
AA
Ranking general94
68.0
AA
Ciencia154
66.0
AA

Puntuaciones de benchmarks (LLM Stats)

(LLM Stats (zeroeval))

General

Tau-bench87.4%Aut.

Language

MMLU-Pro84.3%Aut.

Math

AIME 202595.7%Aut.
IMO-AnswerBench82.0%Aut.

Reasoning

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

Índices de evaluación AA

(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

Puntuaciones por categoría LLM Stats

(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

Precios

Precio de entrada$0.6 / 1M tokens
Precio de salida$2.2 / 1M tokens
Precio mixto (3:1)$1 / 1M tokens
Precio de lectura caché$0.11 / 1M tokens
Precio de escritura cachéGratis

Velocidad

Tokens/seg0.0
Retraso del primer token0.00s
Tiempo hasta la respuesta0.00s

Ranking de Precios por Proveedor

Ranking de Precios por Proveedor

2 proveedores

Más barato: DeepInfraMás caro: Z AI
ProveedorEntradaSalida
1DeepInfraMás barato
$0
$0
2Z AIPRINCIPAL
$0.6
$2.2

Comparar precios entre diferentes proveedores de API para este modelo.

Fuentes externas

LLM StatsArtificial Analysis