GLM 5.2 — Especificaciones
Compiled by Mustafa Ihsan from the vendor’s published documentation · Last updated
| Desarrollador | Zhipu AI |
|---|---|
| Tipo | LLM (para programación/agentes, MoE) |
| Modalidad | Texto → Texto |
| Parámetros | 744 mil millones totales / ~40 mil millones activos (MoE) |
| Ventana de contexto | 1 millón |
| Salida máxima | 131 000 |
| Licencia | MIT (abierto) |
| Pesos abiertos | Sí |
| Lanzado | 2026-06 |
| Precio de entrada | $1.4 /1M |
| Precio de salida | $4.4 /1M |
| Proveedores de API | Zhipu (Z.ai), OpenRouter |
Ejecútelo localmente
| VRAM (4 bits) | ~370 GB |
|---|---|
| GPU mínima | Servidor multi-GPU (p. ej., 5× H100 de 80 GB) |
What is GLM 5.2?
GLM 5.2 is Zhipu AI’s open 1M-context model — a 744-billion-parameter mixture-of-experts
activating roughly 40B parameters per token, built on a new “IndexShare” sparse-attention
design, and notably trained entirely on Huawei chips. The weights are MIT-licensed and it is
strong on coding, design and agentic tasks. Pricing is $1.40 in / $4.40 out per million
tokens.
The Huawei training detail is the strategically interesting one: it is a demonstration that
a frontier-scale model can be trained end to end outside the NVIDIA ecosystem, which has
implications for anyone modelling long-term compute supply. For a buyer, though, the relevant
numbers are simpler. GLM 5.2 blends to roughly $2 per million tokens while scoring near the
top of the open-weight field, which makes it one of the best capability-per-dollar picks
available for agentic and coding workloads and a serious alternative to frontier models
costing five times more. Self-hosting needs around 370 GB at 4-bit — a five-H100 server — so
in practice most teams will use the API and treat the MIT licence as portability insurance.
GLM 5.2 pricing: API cost per 1M tokens
| Entrada (por cada millón de tokens) | $1.40 |
|---|---|
| Salida (por cada millón de tokens) | $4.40 |
| Relación salida/entrada | 3,1× |
| Combinada (4:1 entrada:salida) | $2.00 por 1 millón de tokens |
What GLM 5.2 costs per month
Gasto mensual real con una mezcla entrada:salida de 4:1, es decir, la proporción que realmente genera una carga de trabajo típica de chat o RAG.
| Carga de trabajo | Tokens/mes | Coste mensual |
|---|---|---|
| Proyecto secundario | 1 millón de tokens de entrada / 0,25 millones de tokens de salida | $2.50 |
| Pequeño equipo | 20 millones de tokens de entrada / 5 millones de tokens de salida | $50 |
| Producción | 200 millones de tokens de entrada / 50 millones de tokens de salida | $500 |
Calcule sus propios números en la Calculadora de costos de API de IA.
Cheaper alternatives to GLM 5.2
| Modelo | Dólares por millón combinados | Usted ahorra |
|---|---|---|
| DeepSeek V4-Pro abierta | $0.522 | 74% cheaper |
| Kimi K2.7 Code abierta | $0.980 | 51% cheaper |
| DeepSeek V4-Flash abierta | $0.168 | 92% cheaper |
¿Autoalojarlo o pagar por la API?
GLM 5.2 is open-weight, so you can run it yourself. It needs ~370 GB of VRAM at 4-bit (Multi-GPU server (e.g. 5× H100 80GB)). Self-hosting only beats the API once your volume is high enough to keep that hardware busy — the calculadora de autohospedaje frente a API calcula el punto de equilibrio para su volumen de tokens.
Preguntas frecuentes
How much does GLM 5.2 cost per 1M tokens?
GLM 5.2 costs $1.40 per 1M input tokens and $4.40 per 1M output tokens. At a typical 4:1 input-to-output mix that blends to about $2.00 per 1M tokens.
How much does GLM 5.2 cost per month?
A small-team workload of 20M input and 5M output tokens a month costs about $50 on GLM 5.2. A side project (1M in / 0.25M out) costs roughly $2.50.
What is a cheaper alternative to GLM 5.2?
DeepSeek V4-Pro is the strongest cheaper option in our database at $0.522 per 1M blended — about 74% less than GLM 5.2. It is also open-weight, so self-hosting is an option.
Can I run GLM 5.2 locally?
Yes. GLM 5.2 is open-weight and needs about ~370 GB of VRAM at 4-bit quantisation (Multi-GPU server (e.g. 5× H100 80GB)).
Why does GLM 5.2 charge more for output than input?
Output tokens are generated one at a time and cannot be batched the way a prompt can, so they cost the provider more to serve. GLM 5.2 charges 3.1× more for output, which is why prompt-heavy workloads are far cheaper to run than generation-heavy ones.
Los precios corresponden a las tarifas oficiales publicadas para la API principal del modelo y se revisan periódicamente conforme los proveedores los actualicen. No incluyen descuentos por volumen, procesamiento por lotes ni entradas en caché. Compare todos los modelos uno al lado del otro en la Base de datos de modelos de IA o el Clasificación de modelos de lenguaje grande (LLM).