Everything about GLM-4.7

Overview

GLM-4.7 is a language model from Zhipu AI (Z.ai), released on December 22, 2025. That makes it 10 months old as of October 22, 2026. Its weights are published: you can download it and run it on your own servers, or use one of the 34 API providers that list it on models.dev. It has a reasoning mode: it can "think" before answering, which helps with multi-step problems but uses more tokens, and therefore more money.

We file it under open-weight models (68 models in this category on the site).

How much does GLM-4.7 cost?

GLM-4.7 costs $0.45 per million input tokens and $2.09 per million output tokens (developer's price, checked on October 22, 2026).

To put it in perspective: reading 20,000 tokens (about thirty pages) and writing 2,000 costs roughly $0.013; a thousand of those tasks, $13.15.

That is 2.1 times the median output price of open-weight models ($1).

Tokens read back from cache cost $0.09 per million, which helps an agent that keeps resending the same context.

To estimate your own budget, use the agent cost calculator.

Benchmark results

According to results published by Epoch AI (checked on October 22, 2026), GLM-4.7 has a capability index of 68.4 out of 100, computed on 3 benchmarks from our basket.

With only 3 benchmarks reported, it does not enter the public ranking, which requires at least 4 results, so its index should be read as partial.

• GPQA Diamond (PhD-level science questions): 83.3% — the best tracked model, Gemini 4 Argon, reaches 96.4%
• OTIS Mock AIME (competition mathematics problems): 83.3% — the best tracked model, GPT-5.5 Pro, reaches 100%
• Terminal-Bench (agent tasks in a terminal): 33.4% — the best tracked model, GPT-5.5, reaches 84.7%

Context and capabilities

Its context window reaches 204,800 tokens, roughly 307 pages of text at 500 words per page: enough for a long report or several contracts at once. A single answer can run to 131,072 tokens.

It accepts as input: text. Its knowledge stops at April 2025: beyond that, it needs web search or your documents.

It can call tools (functions, APIs), which makes it a possible foundation for an AI agent.

The weights are published on Hugging Face (zai-org/GLM-4.7). See also our list of open-source models.

When to choose it

Given its category, GLM-4.7 is mainly aimed at projects where you want control over your data or hosting, or want to adapt the model to your field.

Before adopting it, test it on your own examples: benchmarks measure standard tasks, not your job.

Comparable models

In the same category, the models closest to GLM-4.7 are:

• DeepSeek V4 Flash 0731 (DeepSeek, index 68.2)
• Kimi K2 Thinking (Moonshot AI, index 69.6)
• Kimi K2.7 Code (Moonshot AI, index 66.7)
• GLM-5.2 (Zhipu AI (Z.ai), index 70.5)