ChatGLM Models

40 modelsGeneral models free to startUp to 200K context

Usage

Last 9 days · 2026-08-17 to 2026-09-08

Tokens

32.6K

Requests

50

Models in use

1 of 40

Tokens per day, stacked by model

04.2K8.4K08-1709-072026-08-17 — 335 tokens zai-glm-5.2: 3352026-08-18 — 8,370 tokens zai-glm-5.2: 8,3702026-08-19 — 2,415 tokens zai-glm-5.2: 2,4152026-08-24 — 2,110 tokens zai-glm-5.2: 2,1102026-08-26 — 1,505 tokens zai-glm-5.2: 1,5052026-08-28 — 7,090 tokens zai-glm-5.2: 7,0902026-09-04 — 1,990 tokens zai-glm-5.2: 1,9902026-09-07 — 7,440 tokens zai-glm-5.2: 7,4402026-09-08 — 1,380 tokens zai-glm-5.2: 1,380

Which models that traffic went to

  1. Zai Glm 5.2100.0%32.6K

Share of 32.6K tokens.

The two views disagree on purpose: a model can take a large share of the calls and a small share of the tokens — many short requests — or the reverse. Which one matters depends on whether your cost is driven by call volume or by prompt length. Measured on AIHubMix over the last 9 days, counting the 40 model IDs listed on this page; traffic routed through upstream-specific IDs that are not in the public catalog is not included.

All 40 ChatGLM Models

Open in model list
ChatGLM models on AIHubMix with input and output modalities, context length, maximum output, price per million tokens including cache read and cache write rates, and measured throughput and latency.
Modalities
coding-glm-4.6-freeTakes text, returns text.200K131KFreeFree/M
glm-4.6Takes text, returns text.200K131KFreeFree/M34 tok/s1.67 s
coding-glm-4.6Takes text, returns text.200K131K$0.06$0.22/M$0.011/M50 tok/s1.00 s
glm-5Takes text. Output modality not published.200K131K$60$360/M107 tok/s0.75 s
coding-glm-4.5-airTakes text. Output modality not published.131K$0.014$0.084/M
glm-4.5-airTakes text. Output modality not published.131K$0.14$0.84/M91 tok/s1.42 s
embedding-2Takes text. Output modality not published.8K$0.0686$0.0686/M
embedding-3Takes text. Output modality not published.8K$0.0686$0.0686/M
zhipu-ocrTakes vision, returns text.FreeFree/M
glm-4.5-flashTakes text. Output modality not published.$0.02$0.02/M
Pro/THUDM/GLM-4.1V-9B-Thinking$0.04$0.16/M
THUDM/GLM-4-9B-0414$0.05$0.05/M
THUDM/GLM-Z1-9B-0414$0.05$0.05/M
THUDM/GLM-4-32B-0414$0.08$0.08/M
THUDM/GLM-Z1-32B-0414$0.08$0.08/M
glm-4-flash$0.1$0.1/M
THUDM/GLM-4.1V-9B-Thinking$0.1$0.1/M
doubao-1-5-pro-32k-250115$0.108$0.27/M
Z/glm-4.5-air$0.14$0.84/M
GLM-4.5V$0.28$0.84/M81 tok/s0.53 s
chatglm_lite$0.2858$0.2858/M
GLM-4.5$0.4$1.6/M107 tok/s0.25 s
Z/glm-4.5$0.5$2/M
doubao-1-5-pro-256k-250115$0.684$1.2312/M
glm-3-turbo$0.71$0.71/M
chatglm_std$0.7144$0.7144/M
chatglm_turbo$0.7144$0.7144/M
glm-4.5-airxTakes text. Output modality not published.$1.1$4.51/M$0.22/M
chatglm_pro$1.4286$1.4286/M
glm-4v-plus$2$2/M
glm-zero-preview$2$2/M
glm-4.5-xTakes text. Output modality not published.$2.2$8.91/M$0.44/M
glm-4-plus$8$8/M
cogview-3-plus$10$10/M
glm-4$14.2$14.2/M
glm-4v$14.2$14.2/M
code-davinci-edit-001$20$20/M
cogview-3$35.5$35.5/M
zai-glm-5-proTakes text. Output modality not published.$60$360/M107 tok/s0.75 s
zai-glm-5.2Takes text. Output modality not published.$60$360/M107 tok/s0.75 s

Prices are USD per million tokens; cache read and cache write are the rates for prompt-cache hits and for writing a prompt into the cache. Throughput and latency are measured on AIHubMix — the same figures the model detail page shows — not vendor claims. A dash means the catalog does not publish that field for that model, which is not the same as the model not supporting it.

ChatGLM on AIHubMix

Which ChatGLM model should I start with?

coding-glm-4.6-free is free on input — the cheapest entry here that declares tool calling, and it carries a 200K context. Move up to glm-5 when answer quality matters more than cost, or to glm-4.6 for long-form reasoning.

Which of these models reason before answering?

3 of the 40 models here declare a reasoning phase — they work through the problem before producing an answer, which helps on multi-step problems at the cost of extra output tokens. Use the Reasoning filter above the table to see them. The catalog does not record anything further about how they differ, so this page does not sort them into families.

Why are there several entries for the same model?

Because each row is a route you can call, not a model release. Some IDs name an upstream (azure-, alicloud-, cc-), some are the open-weight repository form (THUDM/…), and some differ only in capitalisation, kept so older integrations keep working.

The catalog does not carry a field saying which of those a given row is, so this page does not sort them into buckets it would have to invent. Every row shows that route’s own price, context and speed — compare those directly, and open a model to see the upstreams that serve it.

How is cached input billed?

The Cache read column is the rate for input tokens served from the prompt cache — for example coding-glm-4.6 bills cache hits at 18.33% of the input rate and glm-4.5-airx bills cache hits at 20% of the input rate. Cache write is the surcharge for putting a prompt into the cache in the first place, and only a few upstreams bill it separately. A dash in either column means the catalog carries no cache rate for that model, so plan on paying the full input rate.

Do I need a separate ChatGLM account?

No. One AIHubMix key covers every model on this page, and switching between them is a change to the model string — billing, rate limits, and logs stay in one place.

Start calling ChatGLM in one line

One key, one endpoint, 750 models across 29 model authors.