GLM-5 (Non-reasoning)
Z AI • text#222 of 704 in intelligence
GLM-5 is Z.ai’s flagship open-source foundation model engineered for complex systems design and long-horizon agent workflows. Built for expert developers, it delivers production-grade performance on large-scale programming tasks, rivaling leading...
Data source: Artificial Analysis · ID AA: f164b41f-44c5-4675-bca3-fea1db4bd9ae · Snapshot published: 10/27/2026, 6:00:26 PM · Methodology version: 4.3
Specifications
AA Intelligence Index
21.8
Artificial Analysis · 4.3 · 10/27/2026, 6:00:26 PM
Context Window
205K tokens
Input Price/1M
$1.00
#257 of 445
Output Price/1M
$3.20
Parameters
—
Max Output
131K tokens
Cost per Task
—
USD / task · lower is better · Artificial Analysis · 4.3 · 10/27/2026, 6:00:26 PM
Benchmarks
Artificial Analysis metrics for GLM-5 (Non-reasoning). Higher is better for capability indexes; lower is better for cost per task.
overall
| Benchmark | Score | Maximum | Methodology | Source / version / date |
|---|---|---|---|---|
| AA Intelligence Index | 21.8 | 100.0 | Artificial Analysis official API | Artificial Analysis · 4.3 · 10/27/2026 |
Information
- Release date
- February 11, 2026
- Tool Calling
- ✅ Supported
- Vision
- ❌ Not supported
- Audio
- ❌ Not supported
Full Analysis: GLM-5 (Non-reasoning)
What is GLM-5 (Non-reasoning)?
GLM-5 (Non-reasoning) is an AI model developed by Z AI, classified as a text model. It focuses on text processing and natural language generation. As a proprietary model, it is available via Z AI's cloud API. With a context window of 205K tokens, it is suitable for processing long documents such as contracts, books, and complete codebases.
Pricing & Costs in 2026
GLM-5 (Non-reasoning) is usage-based, priced at $1/1M input tokens and $3.2/1M output tokens. For context: 1 million tokens is approximately 750,000 words, or about 10 average-length books. The mid-range pricing balances quality and cost for most professional applications.
Benchmarks & Performance
GLM-5 (Non-reasoning) was evaluated on 1 different benchmarks, covering categories like overall. Results show moderate performance across available evaluations.
It's important to note that benchmarks measure specific aspects and don't capture the full user experience. Factors like instruction adherence, behavior in long conversations, and real-world task quality vary significantly between models and aren't always reflected in standard scores.
Recommended Use Cases
GLM-5 (Non-reasoning) specializes in text, offering advanced capabilities for creating and processing text content.
Comparison with Alternatives
In the 2026 AI model ecosystem, GLM-5 (Non-reasoning) competes directly with similarly capable models. Z AI competes in this segment against OpenAI, Anthropic, Google, and Meta. The choice between models depends on the specific use case, budget, latency requirements, and need for features like multimodality and tool calling.
For a detailed side-by-side comparison, use our comparison tool or check the overall model ranking.
Frequently Asked Questions
What is GLM-5 (Non-reasoning)?
GLM-5 (Non-reasoning) is an AI model developed by Z AI. It is a text model.
How much does GLM-5 (Non-reasoning) cost?
GLM-5 (Non-reasoning) costs $1/1M input tokens and $3.2/1M output tokens. For heavy usage (e.g., a chatbot handling 100k messages/month), costs can range from $10 to $1,000 depending on volume.
How does GLM-5 (Non-reasoning) compare with other models?
In available benchmarks, GLM-5 (Non-reasoning) scored: AA Intelligence Index: 21.8/100. See the full table above for a detailed comparison.
Is GLM-5 (Non-reasoning) open source?
No, GLM-5 (Non-reasoning) is a proprietary model from Z AI. It is available via cloud API. For open source alternatives, check our open source model ranking.
What is GLM-5 (Non-reasoning) best for?
GLM-5 (Non-reasoning) excels at general-purpose language tasks. With its large context window, it handles long documents, codebases, and extended conversations. It supports tool calling for API integrations and automation.
Last updated: October 27, 2026 • View methodology →