# GLM-4.7

GLM-4.7 is an AI model released by Z.ai on Dec 22 2025. It has a 128k context window and open weight. At release it scored 1433 on Arena Elo (Code), 85.7% on GPQA Diamond and 73.8% on SWE-Bench Verified.

## Facts

| Field | Value |
| --- | --- |
| Model | GLM-4.7 |
| Developer | Z.ai |
| Release date | Monday, Dec 22 2025 |
| Licensing | Open Weight |
| Context window | 128k |

## API pricing

All rates in USD per 1,000,000 tokens, pay-as-you-go.

| Tier | Input | Cached input | Output |
| --- | --- | --- | --- |
| Standard | $0.60 | $0.11 | $2.20 |

Verified August 18, 2026 against the first-party source: https://docs.z.ai/guides/overview/pricing

## Benchmark scores published at release

| Benchmark | Score | What it measures |
| --- | --- | --- |
| SWE-Bench Verified | 73.8% | Real coding tasks pulled from open-source projects — the AI has to find and fix actual bugs. A human-checked version of the original SWE-Bench. Higher is better. |
| LiveCodeBench | 84.9% | Coding problems published so recently the AI can't have seen them in training — a contamination-free test of raw programming skill. Higher is better. |
| Terminal-Bench 2.0 | 41% | Can the AI work in a command-line terminal — running commands and finishing technical setup tasks the way a developer would? (Version 2.0 of the test.) Higher is better. |
| BrowseComp | 52% | Can the AI browse the web and track down hard-to-find answers? Higher is better. |
| Humanity's Last Exam (no tools) | 24.8% | Humanity's Last Exam — extremely hard expert questions across many subjects, written so you can't just look up the answer. “No tools” means the AI answers on its own. Higher is better. |
| Humanity's Last Exam (with tools) | 42.8% | Humanity's Last Exam — extremely hard expert questions across many subjects. “With tools” means the AI is allowed to search the web or run code while answering. Higher is better. |
| GPQA Diamond | 85.7% | Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. |
| Arena Elo (Code) | 1433 | Like the text arena, but people vote on which AI writes better code. The votes become a chess-style Elo rating on arena.ai. Higher is better. |

## Questions and answers

### When was GLM-4.7 released?

GLM-4.7 was released by Z.ai on Monday, Dec 22 2025.

### Who made GLM-4.7?

GLM-4.7 was built by Z.ai. Chinese AI lab spun out of Tsinghua University (formerly Zhipu AI), building the open-weight GLM family. Rebranded internationally as Z.ai in 2025.

### How much does GLM-4.7 cost?

GLM-4.7 costs $0.60 per million input tokens and $2.20 per million output tokens through the Z.ai API. Cached input is $0.11 per million tokens. Rates are pay-as-you-go API prices verified against Z.ai's published pricing on August 18, 2026.

### What benchmark scores did GLM-4.7 get?

GLM-4.7 reports 8 tracked benchmark scores — SWE-Bench Verified: 73.8%; LiveCodeBench: 84.9%; Terminal-Bench 2.0: 41%; BrowseComp: 52%; Humanity's Last Exam (no tools): 24.8%; Humanity's Last Exam (with tools): 42.8%; GPQA Diamond: 85.7%; Arena Elo (Code): 1433. Scores are the figures published at release by Z.ai.

### What is the context window of GLM-4.7?

GLM-4.7 has a context window of 128k. That is the maximum amount of input plus output the model can hold in a single request.

### Is GLM-4.7 open source?

Partly. GLM-4.7 is an open-weight model: the trained weights are free to download and run locally or on your own infrastructure, but the training data and code are not fully released and the license may restrict some commercial uses. It is not open source in the strict sense.

### What came before and after GLM-4.7?

Z.ai's previous tracked release was GLM-4.6 on Sep 30 2025, 83 days earlier. It was followed by GLM-5 on Feb 12 2026.


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Canonical page: https://aireleasetracker.com/model/zai/glm-4.7
Full dataset: https://aireleasetracker.com/llms-full.txt · JSON: https://aireleasetracker.com/models.json
Source: AI Release Tracker (https://aireleasetracker.com). Benchmark scores are the figures published by the releasing lab at launch.